IntroductionIBM Capacity Management Analytics provides IT infrastructure system managers, system administrators, and data center executives with access to the information they need to optimize their IT performance, availability, and scalability. This information is used for cost effective analysis of service objectives, usage and resource utilization, system tuning, accounting, and cost recovery. Using the measurement and management of available and planned capacity, IT can move quickly from insight to action to improve services for the business.Two products are available for IBM Capacity Management Analytics:IBM Capacity Management Analytics on distributed platformsThis product includes:IBM Cognos AnalyticsIBM SPSS Modeler GoldIBM ILOG CPLEX Optimization StudioIBM Capacity Management Analytics Solutions KitIBM Capacity Management Analytics Solutions InstallerIBM Tivoli Decision Support for z/OS is not included with this product. You will need to purchase it separately. IBM Capacity Management Analytics on z/OSThis product includes:IBM Capacity Management Analytics on distributed platformsIBM Capacity Management Analytics Product RegistrationIBM Tivoli Decision Support for z/OSTo run Capacity Management Analytics, you must have IBM Tivoli Decision Support for z/OS already installed. The following options are recommended to be able to manage most systems:SystemCICSIMSNetworkDistributedAS400The Capacity Management Analytics Solutions Kit provides built-in expertise on capacity management, which is designed to support the core capabilities associated with capacity management for IBM z Systems and distributed-based infrastructures. It consists of a predefined data model to warehouse machine data from across the data center, a set of predefined reports to visualize what is defined and how it is being used, a set of predictive models to understand what is needed in the future and likely usage scenarios, and a set of prescriptive models to optimize the use of IT resources.The Capacity Management Analytics Solution Kit leverages the operational intelligence platform using industry best practices and prebuilt integration between the components to get you up and running quickly and effectively, focusing on the following core capabilities:System management and optimizationSophisticated reporting capabilities to allow analysts, systems managers and executives to view, interact and personalize their reporting that is based on their unique requirements.Problem identification and resolutionA top-down view of zEnterprise and distributed system workloads with the ability to drill into further detail, perform ad hoc analysis, create system alerts, or monitor performance to predict potential issues before they affect the business.Software costs analysisAbility for your customer to help manage z/OS® software costs and enable capacity management planners to identify when and where workloads need to be adjusted and when more capacity is required.Capacity forecastingAbility to forecast your future requirements to ensure that capacity is available when the business needs it. Real-time anomaly detectionAbility to improve systems management response time with a tool that can detect CICS transaction anomalies in real time.Audience This guide is intended to provide users with an understanding of how to install, maintain, and use the Capacity Management Analytics Solution Kit. It describes the reports that are included with the solution kit and the models and streams that are used to provide the data for the reports. It is intended for Capacity Management Analytics administrators, system administrators, database administrators, analysts, report authors, content administrators, and modelers.Finding informationTo find Capacity Management Analytics product documentation, go to Capacity Management Analytics (http://www.ibm.com/support/knowledgecenter/SSUFR9_2.1.1) on IBM Knowledge Center.Accessibility features Accessibility features help users who have a physical disability, such as restricted mobility or limited vision, to use information technology products. Some of the components included in the Capacity Management Analytics solution have accessibility features. For information about these features, see the Accessibility featuresAccessibility features help users who have a physical disability, such as restricted mobility or limited vision, to use information technology products. section in this document. Capacity Management Analytics HTML documentation has accessibility features. PDF documents are supplemental and, as such, include no added accessibility features. Forward-looking statements This documentation describes the current functionality of the product. References to items that are not currently available may be included. No implication of any future availability should be inferred. Any such references are not a commitment, promise, or legal obligation to deliver any material, code, or functionality. The development, release, and timing of features or functionality remain at the sole discretion of IBM.

Introduction

IBM® Capacity Management Analytics provides IT infrastructure system managers, system administrators, and data center executives with access to the information they need to optimize their IT performance, availability, and scalability.

This information is used for cost effective analysis of service objectives, usage and resource utilization, system tuning, accounting, and cost recovery. Using the measurement and management of available and planned capacity, IT can move quickly from insight to action to improve services for the business.

Two products are available for IBM Capacity Management Analytics:

IBM Capacity Management Analytics on distributed platforms

This product includes:

IBM Cognos® Analytics

IBM SPSS® Modeler Gold

IBM ILOG® CPLEX® Optimization Studio

IBM Capacity Management Analytics Solutions Kit

IBM Capacity Management Analytics Solutions Installer

Important: IBM Tivoli® Decision Support for z/OS® is not included with this product. You will need to purchase it separately.

IBM Capacity Management Analytics on z/OS

This product includes:

IBM Capacity Management Analytics on distributed platforms

IBM Capacity Management Analytics Product Registration

IBM Tivoli Decision Support for z/OS

Note: To run Capacity Management Analytics, you must have IBM Tivoli Decision Support for z/OS already installed. The following options are recommended to be able to manage most systems:

System

CICS®

IMS™

Network

Distributed

AS400

The Capacity Management Analytics Solutions Kit provides built-in expertise on capacity management, which is designed to support the core capabilities associated with capacity management for IBM z Systems™ and distributed-based infrastructures. It consists of a predefined data model to warehouse machine data from across the data center, a set of predefined reports to visualize what is defined and how it is being used, a set of predictive models to understand what is needed in the future and likely usage scenarios, and a set of prescriptive models to optimize the use of IT resources.

The Capacity Management Analytics Solution Kit leverages the operational intelligence platform using industry best practices and prebuilt integration between the components to get you up and running quickly and effectively, focusing on the following core capabilities:

System management and optimization

Sophisticated reporting capabilities to allow analysts, systems managers and executives to view, interact and personalize their reporting that is based on their unique requirements.

Problem identification and resolution

A top-down view of zEnterprise® and distributed system workloads with the ability to drill into further detail, perform ad hoc analysis, create system alerts, or monitor performance to predict potential issues before they affect the business.

Software costs analysis

Ability for your customer to help manage z/OS® software costs and enable capacity management planners to identify when and where workloads need to be adjusted and when more capacity is required.

Capacity forecasting

Ability to forecast your future requirements to ensure that capacity is available when the business needs it.

Real-time anomaly detection

Ability to improve systems management response time with a tool that can detect CICS transaction anomalies in real time.

Audience

This guide is intended to provide users with an understanding of how to install, maintain, and use the Capacity Management Analytics Solution Kit. It describes the reports that are included with the solution kit and the models and streams that are used to provide the data for the reports. It is intended for Capacity Management Analytics administrators, system administrators, database administrators, analysts, report authors, content administrators, and modelers.

Finding information

To find Capacity Management Analytics product documentation, go to Capacity Management Analytics (http://www.ibm.com/support/knowledgecenter/SSUFR9_2.1.1) on IBM Knowledge Center.

Accessibility features

Accessibility features help users who have a physical disability, such as restricted mobility or limited vision, to use information technology products. Some of the components included in the Capacity Management Analytics solution have accessibility features. For information about these features, see the Accessibility features section in this document.

Capacity Management Analytics HTML documentation has accessibility features. PDF documents are supplemental and, as such, include no added accessibility features.

Forward-looking statements

This documentation describes the current functionality of the product. References to items that are not currently available may be included. No implication of any future availability should be inferred. Any such references are not a commitment, promise, or legal obligation to deliver any material, code, or functionality. The development, release, and timing of features or functionality remain at the sole discretion of IBM.

What's new?The following new features are available in Capacity Management Analytics 2.1.1. New features in Capacity Management Analytics 2.1.1 A new WEB UI to manage and drive automated installation and configuration of the Capacity Management Analytics 2.1.1 operational intelligence platform components that install on distributed systems as well as the Solution Kit expert-based application modules. The following sub-hills provide more details: Installer WEB UIInstallation is made easier with a WEB user interface that leverages NodeJS thus eliminating the need for a fat client that existed in prior versions of Capacity Management Analytics. The UI provides a recommended configuration and recommended defaults for the required attributes. The WEB UI drives a solution installer to automatically install and configure the platform components and solution kit modules. Install verification via use of sample databaseA sample database with TDSz data is provided that can be used to verify that all of Capacity Management Analytics is properly installed and configured. It is an optional selection for installation that when selected will create the sample TDSz database and load the data. Subsequent Solution Kit modules will use this data allowing for the verification of the streams and reports for those modules.Automatic installation and configuration of platform componentsA solution installer is leveraged to install and configure Cognos, SPSS Modeler, CPLEX, and DB2 LUW on a single node. The installer WEB UI is used to override the minimal set of attributes required if so desired. Automatic installation and configuration of Solution Kit modulesA solution installer is leveraged to install and configure the Solution Kit modules and sample database on a feature basis. Only the BASE module is required to install the first time. The rest of modules can be installed on an as needed basis.Solution Kit Command line installation, configuration, and managementThe installation, configuration, and management of the Solution Kit can also be done via command line instead of through the WEB UI and Solution Installer. This is useful for continued control of solution kit features after the initial installation and configuration. It is also useful in operating systems where the solution installer is not supported.Commands are available for CMAHOME and CMAINSTANCE which drives the individual solution kit modules. Each solution kit module provides its own command line with additional levels of control.Preview Solution Kit installation, configuration, managementA preview mode is provided for the installation, configuration, and management of the Solution Kit. This allows you to see what will be executed before running it so you can review and make sure you organization’s standards are being followed.Solution Kit InstanceThe Solution Kit installation is separated into a CMAHOME directory and a CMAINSTANCE directory. The CMAHOME is for executable code, configuration information, and data that should not be changed after the initial installation. The CMAINSTANCE directory is for configuration and data specific to an individual instance of Capacity Management Analytics. The CMAHOME can be used by multiple CMAINSTANCE(s) each with its own directory and Capacity Management Analytics metadata database.

What's new?

The following new features are available in Capacity Management Analytics 2.1.1.

New features in Capacity Management Analytics 2.1.1

A new WEB UI to manage and drive automated installation and configuration of the Capacity Management Analytics 2.1.1 operational intelligence platform components that install on distributed systems as well as the Solution Kit expert-based application modules. The following sub-hills provide more details:

Installer WEB UI

Installation is made easier with a WEB user interface that leverages NodeJS thus eliminating the need for a fat client that existed in prior versions of Capacity Management Analytics. The UI provides a recommended configuration and recommended defaults for the required attributes.

The WEB UI drives a solution installer to automatically install and configure the platform components and solution kit modules.

Install verification via use of sample database

A sample database with TDSz data is provided that can be used to verify that all of Capacity Management Analytics is properly installed and configured. It is an optional selection for installation that when selected will create the sample TDSz database and load the data. Subsequent Solution Kit modules will use this data allowing for the verification of the streams and reports for those modules.

Automatic installation and configuration of platform components

A solution installer is leveraged to install and configure Cognos®, SPSS® Modeler, CPLEX®, and DB2® LUW on a single node. The installer WEB UI is used to override the minimal set of attributes required if so desired.

Automatic installation and configuration of Solution Kit modules

A solution installer is leveraged to install and configure the Solution Kit modules and sample database on a feature basis. Only the BASE module is required to install the first time. The rest of modules can be installed on an as needed basis.

Solution Kit Command line installation, configuration, and management

The installation, configuration, and management of the Solution Kit can also be done via command line instead of through the WEB UI and Solution Installer. This is useful for continued control of solution kit features after the initial installation and configuration. It is also useful in operating systems where the solution installer is not supported.

Commands are available for CMAHOME and CMAINSTANCE which drives the individual solution kit modules. Each solution kit module provides its own command line with additional levels of control.

Preview Solution Kit installation, configuration, management

A preview mode is provided for the installation, configuration, and management of the Solution Kit. This allows you to see what will be executed before running it so you can review and make sure you organization’s standards are being followed.

Solution Kit Instance

The Solution Kit installation is separated into a CMAHOME directory and a CMAINSTANCE directory. The CMAHOME is for executable code, configuration information, and data that should not be changed after the initial installation. The CMAINSTANCE directory is for configuration and data specific to an individual instance of Capacity Management Analytics. The CMAHOME can be used by multiple CMAINSTANCE(s) each with its own directory and Capacity Management Analytics metadata database.

IBM Capacity Management AnalyticsIBM Capacity Management Analytics architecture can combine multiple data sources and other IBM database products to provide extensive capacity management reporting.architectureThe following diagram shows a typical core architecture for Capacity Management Analytics where a single warehouse is used to store all data. The Capacity Management Analytics solution runs on an IBM zEnterprise system and uses an IBM DB2 for z/OS database server. The core software components include the following:IBM Tivoli Decision Support for z/OSIBM SPSS Modeler ProfessionalIBM Cognos AnalyticsIBM ILOG CPLEX Optimization StudioIBM Capacity Management Analytics Solution InstallerIBM Capacity Management Analytics Solution KitA typical Capacity Management Analytics architecture on z systemsImage of a typical IBM Capacity Management Analytics architecture on z systemsServer-side architecture and operationsThe server-side components in the Capacity Management Analytics solution run on the IBM zEnterprise system. All related data for the system is stored in an IBM DB2 for z/OS database server.System Management Facility (SMF) and Resource Measurement Facility (RMF) data is generated by zEnterprise systems and loaded into a DB2 for z/OS database through Tivoli Decision Support for z/OS. Some SMF and RMF data is further processed by SPSS Modeler streams and loaded into the Capacity Management Analytics database. After the data is loaded, users can view the Capacity Management Analytics reports from the IBM Cognos Analytics portal.The following diagram shows the flow of data for the Capacity Management Analytics solution.Capacity Management Analytics data flowCapacity Management Analytics data flowThe Capacity Management Analytics Solution Kit provides the reports that are deployed into IBM Cognos Analytics and the SPSS Modeler streams.Client-side applicationsThe client side of the Capacity Management Analytics solution can run different types of components, such as IBM Cognos Framework Manager, the SPSS Modeler Client, and web browsers for consuming reports.The Capacity Management Analytics Solution Kit provides the Framework Manager model and the SPSS streams. Assumptions for successfully running Capacity Management AnalyticsNaming conventions are used within your datacenter to keep LPARs and SYSIDs are unique. Best practice installations, configurations, and performance tuning have been applied to the core software products.

IBM Capacity Management Analytics

IBM® Capacity Management Analytics architecture can combine multiple data sources and other IBM database products to provide extensive capacity management reporting.

The following diagram shows a typical core architecture for Capacity Management Analytics where a single warehouse is used to store all data. The Capacity Management Analytics solution runs on an IBM zEnterprise® system and uses an IBM DB2® for z/OS® database server. The core software components include the following:

IBM Tivoli® Decision Support for z/OS

IBM SPSS® Modeler Professional

IBM Cognos® Analytics

IBM ILOG® CPLEX® Optimization Studio

IBM Capacity Management Analytics Solution Installer

IBM Capacity Management Analytics Solution Kit

Figure 1. A typical Capacity Management Analytics architecture on z systems

Server-side architecture and operations

The server-side components in the Capacity Management Analytics solution run on the IBM zEnterprise system. All related data for the system is stored in an IBM DB2 for z/OS database server.

System Management Facility (SMF) and Resource Measurement Facility™ (RMF™) data is generated by zEnterprise systems and loaded into a DB2 for z/OS database through Tivoli Decision Support for z/OS. Some SMF and RMF data is further processed by SPSS Modeler streams and loaded into the Capacity Management Analytics database. After the data is loaded, users can view the Capacity Management Analytics reports from the IBM Cognos Analytics portal.

The following diagram shows the flow of data for the Capacity Management Analytics solution.

Figure 2. Capacity Management Analytics data flow

The Capacity Management Analytics Solution Kit provides the reports that are deployed into IBM Cognos Analytics and the SPSS Modeler streams.

Client-side applications

The client side of the Capacity Management Analytics solution can run different types of components, such as IBM Cognos Framework Manager, the SPSS Modeler Client, and web browsers for consuming reports.

The Capacity Management Analytics Solution Kit provides the Framework Manager model and the SPSS streams.

Assumptions for successfully running Capacity Management Analytics

Naming conventions are used within your datacenter to keep LPARs and SYSIDs are unique.

Best practice installations, configurations, and performance tuning have been applied to the core software products.

Downloading IBM Capacity Management Analytics 2.1.1After you have ensured your environment is set up correctly, you can download Capacity Management Analytics 2.1.1. First, decide whether you will purchase IBM Capacity Management Analytics for distributed platforms, or IBM Capacity Management Analytics on z/OS, which includes IBM Tivoli Decision Support for z/OS. Next, decide whether you will use the IBM Capacity Management Analytics Solution Installer to automatically install all distributed components or whether you will install each individual component manually. For more information about the software that you must download, see the IBM Capacity Management Analytics 2.1.1 download document (www.ibm.com/support/docview.wss?uid=swg24042285).Order Capacity Management Analytics 2.1.1 on Passport Advantage or Capacity Management Analytics 2.1.1 on z/OS on ShopZ .If you have ordered IBM Capacity Management Analytics 2.1.1 distributed systems, then follow these steps.Access the IBM Passport Advantage web site.If you receive an error, try using a different web browser to access Passport Advantage.Sign in and navigate to the software downloads page.Find the eImages for Capacity Management Analytics 2.1.1 and select the check box beside the name of the desired eImages to downloadIf you are installing using the solution installer, download the following:IBM Capacity Management Analytics Solution Installer 2.1.1 for the required platform (Linux on System z or x86-64).IBM Capacity Management Analytics Server Bundle 2.1.1 for the required platform (Linux on System z or x86-64).IBM Capacity Management Analytics Solution Kit 2.1.1 Multiplatform English.If you are installing manually, you must download each individual server component for the required system and platform.If you intend to use client side components, download each individual client component. The following are recommended: IBM Cognos for Microsoft Office Microsoft Windows MultilingualIBM Cognos Framework Manager Microsoft Windows MultilingualIBM SPSS Modeler Gold Client Keyless 64-bit Microsoft Windows MultilingualIBM SPSS Data Access Pack Multiplatform EnglishWhen the download is complete, a Download Complete message will be displayed. The location of the downloaded files will be displayed in the message window.Save the packages to the corresponding platforms.Follow the instructions of the Installing IBM Capacity Management AnalyticsUse the solution installer to deploy the IBM Capacity Management Analytics component programs on computers or virtual machines that are running 64-bit Red Hat Enterprise Linux Server Edition version 7 operating systems. For other operating systems, you must install the product manually. section to install and configure the Capacity Management Analytics Solution Kit.If you have ordered IBM Capacity Management Analytics on z/OS 2.1.1, follow these steps:Sign in to the ShopZ website and navigate to the software download page.Search using product ID 5698-CMA or description IBM Capacity Management Analytics under Package type z/OS – CBPDO and Group MVS – Application Development.Download from the z/OS (Z038) SREL: IBM Capacity Management Analytics Product Registration (HHCN211) and the required IBM Tivoli Decision Support for z/OS FMIDs and follow instructions in their respective program directories to complete their installation.Download Accessing IBM Capacity Management Analytics on z/OS DVDs (LCD7704900). Read and follow the instructions it provides to download the bundled products that install on distributed systems from the IBM Web Membership (IWM) website.Log in to the IBM Web Membership website with your IBM ID and the security key provided.If you will install using the solution installer, download the appropriate server package bundle for your platform from the following:IBM Capacity Management Analytics Server Package Bundle with Solution Installer for Linux on z Systems (recommended)IBM Capacity Management Analytics Server Package Bundle with Solution Installer for Linux x86-64.If you are installing manually, you must download each individual server component for the required system and platform for the product bundles in the IBM Web Membership website.If you intend to use client side components, download the IBM Capacity Management Analytics Client Package Bundle (recommended) or each individual client component. Save the packages to the corresponding platforms.Follow the instructions to install the software.

Downloading IBM Capacity Management Analytics 2.1.1

After you have ensured your environment is set up correctly, you can download Capacity Management Analytics 2.1.1.

First, decide whether you will purchase IBM® Capacity Management Analytics for distributed platforms, or IBM Capacity Management Analytics on z/OS®, which includes IBM Tivoli® Decision Support for z/OS.

Next, decide whether you will use the IBM Capacity Management Analytics Solution Installer to automatically install all distributed components or whether you will install each individual component manually.

For more information about the software that you must download, see the IBM Capacity Management Analytics 2.1.1 download document (www.ibm.com/support/docview.wss?uid=swg24042285).

Procedure

Order Capacity Management Analytics 2.1.1 on Passport Advantage® or Capacity Management Analytics 2.1.1 on z/OS on ShopZ .

If you have ordered IBM Capacity Management Analytics 2.1.1 distributed systems, then follow these steps.

Access the IBM Passport Advantage web site.

Tip: If you receive an error, try using a different web browser to access Passport Advantage.

Sign in and navigate to the software downloads page.

Find the eImages for Capacity Management Analytics 2.1.1 and select the check box beside the name of the desired eImages to download

If you are installing using the solution installer, download the following:

IBM Capacity Management Analytics Solution Installer 2.1.1 for the required platform (Linux on System z or x86-64).

IBM Capacity Management Analytics Server Bundle 2.1.1 for the required platform (Linux on System z or x86-64).

IBM Capacity Management Analytics Solution Kit 2.1.1 Multiplatform English.

If you are installing manually, you must download each individual server component for the required system and platform.

If you intend to use client side components, download each individual client component. The following are recommended:

IBM Cognos® for Microsoft Office Microsoft Windows Multilingual

IBM Cognos Framework Manager Microsoft Windows Multilingual

IBM SPSS® Modeler Gold Client Keyless 64-bit Microsoft Windows Multilingual

IBM SPSS Data Access Pack Multiplatform English

When the download is complete, a Download Complete message will be displayed. The location of the downloaded files will be displayed in the message window.

Save the packages to the corresponding platforms.

Follow the instructions of the Installing IBM Capacity Management Analytics section to install and configure the Capacity Management Analytics Solution Kit.

If you have ordered IBM Capacity Management Analytics on z/OS 2.1.1, follow these steps:

Sign in to the ShopZ website and navigate to the software download page.

Search using product ID 5698-CMA or description IBM Capacity Management Analytics under Package type z/OS – CBPDO and Group MVS™ – Application Development.

Download from the z/OS (Z038) SREL: IBM Capacity Management Analytics Product Registration (HHCN211) and the required IBM Tivoli Decision Support for z/OS FMIDs and follow instructions in their respective program directories to complete their installation.

Download Accessing IBM Capacity Management Analytics on z/OS DVDs (LCD7704900). Read and follow the instructions it provides to download the bundled products that install on distributed systems from the IBM Web Membership (IWM) website.

Log in to the IBM Web Membership website with your IBM ID and the security key provided.

If you will install using the solution installer, download the appropriate server package bundle for your platform from the following:

IBM Capacity Management Analytics Server Package Bundle with Solution Installer for Linux on z Systems (recommended)

IBM Capacity Management Analytics Server Package Bundle with Solution Installer for Linux x86-64.

If you are installing manually, you must download each individual server component for the required system and platform for the product bundles in the IBM Web Membership website.

If you intend to use client side components, download the IBM Capacity Management Analytics Client Package Bundle (recommended) or each individual client component.

Save the packages to the corresponding platforms.

Follow the instructions to install the software.

Preparation of your environmentBefore you can install IBM Capacity Management Analytics, you must ensure that your environment is set up. Preparing your environment includes applying settings for Tivoli Decision Support for z/OS, identifying the Tivoli Decision Support for z/OS schemas that Capacity Management Analytics requires, creating users and groups, and configuring IBM SPSS Modeler for Capacity Management Analytics.Software, hardware, and operating system requirementsEnsure your environment is prepared with the correct software, hardware, and operating systems.Prerequisite software for installing Capacity Management AnalyticsTo run IBM Capacity Management Analytics, you must have certain IBM software already installed.System Management Facility and Resource Measurement Facility recordsIBM Capacity Management Analytics uses the following System Management Facility (SMF) and Resource Measurement Facility (RMF) records.Settings for Tivoli Decision Support for z/OSFor IBM Capacity Management Analytics reports, you must apply certain settings for your IBM Tivoli Decision Support for z/OS data store.Users and groups for installation and configurationThe IBM Capacity Management Analytics solution is designed to be used and shared among several users.Modifying the sudoers file for the user who runs the installationTo install IBM IBM Capacity Management Analytics by using the solution installer, you must be either the Capacity Management Analytics administrator, root user or be a user with sudo permissions. To run the installation as a user with sudo permissions, you must add that user to the sudoers file.Configuring the administrator and user profilesBefore you can use the IBM Capacity Management Analytics IDs, you must set them up correctly. Granting permissions to the data storeDB2 permissions are needed for theIBM Capacity Management Analytics users to access the IBM Tivoli Decision Support for z/OS data store for Capacity Management Analytics and to create and use the Capacity Management Analytics data store.Setting ulimit values on the node computersBefore you install IBM Capacity Management Analytics on Linux operating systems, you must ensure that you have appropriate ulimit values on each node.Server side network configurationServer side network configuration includes host file configuration and firewall configuration.

Preparation of your environment

Before you can install IBM® Capacity Management Analytics, you must ensure that your environment is set up. Preparing your environment includes applying settings for Tivoli® Decision Support for z/OS, identifying the Tivoli Decision Support for z/OS schemas that Capacity Management Analytics requires, creating users and groups, and configuring IBM SPSS® Modeler for Capacity Management Analytics.

Software, hardware, and operating system requirements
Ensure your environment is prepared with the correct software, hardware, and operating systems.

Prerequisite software for installing Capacity Management Analytics
To run IBM Capacity Management Analytics, you must have certain IBM software already installed.

System Management Facility and Resource Measurement Facility records
IBM Capacity Management Analytics uses the following System Management Facility (SMF) and Resource Measurement Facility™ (RMF™) records.

Settings for Tivoli Decision Support for z/OS
For IBM Capacity Management Analytics reports, you must apply certain settings for your IBM Tivoli Decision Support for z/OS® data store.

Users and groups for installation and configuration
The IBM Capacity Management Analytics solution is designed to be used and shared among several users.

Modifying the sudoers file for the user who runs the installation
To install IBM IBM Capacity Management Analytics by using the solution installer, you must be either the Capacity Management Analytics administrator, root user or be a user with sudo permissions. To run the installation as a user with sudo permissions, you must add that user to the sudoers file.

Configuring the administrator and user profiles
Before you can use the IBM Capacity Management Analytics IDs, you must set them up correctly.

Granting permissions to the data store
DB2 permissions are needed for theIBM Capacity Management Analytics users to access the IBM Tivoli Decision Support for z/OS data store for Capacity Management Analytics and to create and use the Capacity Management Analytics data store.

Setting ulimit values on the node computers
Before you install IBM Capacity Management Analytics on Linux operating systems, you must ensure that you have appropriate ulimit values on each node.

Server side network configuration
Server side network configuration includes host file configuration and firewall configuration.

Software, hardware, and operating system requirementsEnsure your environment is prepared with the correct software, hardware, and operating systems.operating systemsoftware and hardwareTo review an up-to-date list of environments that are supported by Capacity Management Analytics, see the IBM Software Product Compatibility Reports (www.ibm.com/support/docview.wss?uid=swg27045028).For the list of required fix packs and APARs for the products that are used with Capacity Management Analytics, see the Required fix packs and APARs for Capacity Management Analytics page (www.ibm.com/support/docview.wss?uid=swg27046400).Recommended RedHat 7 or later on Linux System z is recommended for all distributed components, including IBM Cognos Analytics and IBM SPSS Modeler Premium. It is further recommended that the solution installer be used to install all of the server components and the solution kit.Operating system requirements The Capacity Management Analytics Solution Installer must be on 64bit servers that are running either Red Hat Enterprise Linux Server Edition version 6.7 or version 7.2 operating system for either z Systems or x86-64 hardware. If you use the solution installer, the operating system for the computer where you run the solution installer must be set to use English as the language. The solution installer does not run on other languages. The Capacity Management Analytics Solution Kit can be installed on 64bit servers that are running either Red Hat Enterprise Linux Server Edition version 6.7 or version 7.2 operating system or SUSE Linux Enterprise Server 12.0 for either z Systems or x86-64 hardware. The Capacity Management Analytics client components must be on 64bit computers that are running either Microsoft Windows 7 or Microsoft Windows 8 operating systems.Hardware requirements The server must have at minimum:2 processors, with 4 recommended8GB memory, with 12GB recommended60 GB of free hard disk spacePreparation of your environmentBefore you can install IBM Capacity Management Analytics, you must ensure that your environment is set up. Preparing your environment includes applying settings for Tivoli Decision Support for z/OS, identifying the Tivoli Decision Support for z/OS schemas that Capacity Management Analytics requires, creating users and groups, and configuring IBM SPSS Modeler for Capacity Management Analytics.

Software, hardware, and operating system requirements

Ensure your environment is prepared with the correct software, hardware, and operating systems.

To review an up-to-date list of environments that are supported by Capacity Management Analytics, see the IBM® Software Product Compatibility Reports (www.ibm.com/support/docview.wss?uid=swg27045028).

For the list of required fix packs and APARs for the products that are used with Capacity Management Analytics, see the Required fix packs and APARs for Capacity Management Analytics page (www.ibm.com/support/docview.wss?uid=swg27046400).

Recommended

RedHat 7 or later on Linux System z is recommended for all distributed components, including IBM Cognos® Analytics and IBM SPSS® Modeler Premium. It is further recommended that the solution installer be used to install all of the server components and the solution kit.

Operating system requirements

The Capacity Management Analytics Solution Installer must be on 64bit servers that are running either Red Hat Enterprise Linux Server Edition version 6.7 or version 7.2 operating system for either z Systems or x86-64 hardware.

Note: If you use the solution installer, the operating system for the computer where you run the solution installer must be set to use English as the language. The solution installer does not run on other languages.

The Capacity Management Analytics Solution Kit can be installed on 64bit servers that are running either Red Hat Enterprise Linux Server Edition version 6.7 or version 7.2 operating system or SUSE Linux Enterprise Server 12.0 for either z Systems or x86-64 hardware.

The Capacity Management Analytics client components must be on 64bit computers that are running either Microsoft Windows 7 or Microsoft Windows 8 operating systems.

Hardware requirements

The server must have at minimum:

2 processors, with 4 recommended

8GB memory, with 12GB recommended

60 GB of free hard disk space

Parent topic: Preparation of your environment

Prerequisite software for installing Capacity Management AnalyticsTo run IBM Capacity Management Analytics, you must have certain IBM software already installed.It is recommended to use the solution installer for all prerequisite software that is supported by the solution installer including Cognos, SPSS, CPLEX, and DB2 LUW. Use the documentation for the respective products to install the software that you wish to install yourself without the solution installer or is not supported by the solution installer. The solution installer does not support the installation of any z/OS software.The prerequisite software that is listed here are the minimum versions that are required for Capacity Management Analytics. It is recommended to always use the most recent version or fix pack of the prerequisite software.installationprerequisitesJava Java is included. Do not use external or unapproved Java versions.Tivoli Decision Support for z/OS 1.8.2 Tivoli Decision Support for z/OS version 1.8.2 is required for Capacity Management Analytics. You must apply fixes PI37340 and PI41870 for Tivoli Decision Support for z/OS version 1.8.2 for Capacity Management Analytics. For information about upgrading to Tivoli Decision Support for z/OS version 1.8.2, see Migration from TDSz 1.8.0 and 1.8.1 to 1.8.2 (www.ibm.com/support/docview.wss?uid=swg21699114). The need for more Tivoli Decision Support for z/OS components is determined by your environment and reporting requirements. The following Tivoli Decision Support for z/OS components are required for all installations: z/OS Performance Management (MVSPM)System statisticsWorkload statisticsGlobal storage statisticsVirtual storage statisticsDevice statisticsTCP/IP for z/OSRMF XP Distributed The following Tivoli Decision Support for z/OS components are required for the Capacity Management Analytics Application Analytics feature: Key Performance Metrics - z/OS Address Space Statistics (SMF type 30) If Customer Information and Control System (CICS) is installed, the following Tivoli Decision Support for z/OS components are also recommended: CICS Monitoring Partitioned ComponentCICS OMEGAMON Monitoring ComponentCICS Statistics Partitioned ComponentCICS Transaction and UOW Analysis Component (required by the Anomaly Detection feature)CICS Monitoring (required by the Application Analytics feature)CICS Transaction Gateway Component Additionally, the following Tivoli Decision Support for z/OS components are recommended for all installations: z/OS System (MVS) componentz/OS Interval Job/Step Accounting componentz/OS Availability componentData Set component For DB2, the Tivoli Decision Support for z/OS DB2 components are also recommended. For more information about Tivoli Decision Support for z/OS, see IBM Knowledge Center (www.ibm.com/support/knowledgecenter/SSH53X_1.8.2).IBM DB2 for z/OS 11 This database is required for IBM DB2 Analytics Accelerator (IDAA). For more information about IBM DB2 for z/OS, see IBM Knowledge Center DB2 for z/OS welcome page (www.ibm.com/support/knowledgecenter/SSEPEK_11.0.0/home/src/tpc/db2z_11_prodhome.html).IBM DB2 for z/OS 10 For more information about IBM DB2 for z/OS, see IBM Knowledge Center DB2 for z/OS welcome page (www.ibm.com/support/knowledgecenter/SSEPEK_10.0.0/com.ibm.db2z10.doc/src/alltoc/db2z_10_prodhome.html).IBM System Director Platform Agent and RMF XP For the distributed platform monitoring capability of Capacity Management Analytics, you must use RMF XP on z/OS and install the latest version (6.3.3 or later) of IBM System Director Platform Agent on distributed operating systems. For the Redhat xLinux 64-bit platform, install Platform Agent for RHEL 6 for Linux on IBM X86-64 on KVM. For more information about IBM System Director Platform Agent, see the IBM System Director introduction (www.ibm.com/systems/director/). For more information about RMF XP, see the Cross platform monitoring with RMF XP (www.ibm.com/support/knowledgecenter/SSLTBW_2.1.0/com.ibm.zos.v2r1.erbb200/gpm4cim.htm) topic on IBM Knowledge Center. RMF APAR OA45331 is required for the distributed platform monitoring capability of Capacity Management Analytics. Important: Additional prerequisite software is required if the Capacity Management Analytics solution is installed by not using the Solution Installer.IBM Cognos Analytics 11 For information about installing IBM Cognos Analytics, see the Installation and Configuration Guide at IBM Cognos Analytics 11.0 documentation (http://www.ibm.com/support/knowledgecenter/SSEP7J_11.0.0/com.ibm.swg.ba.cognos.cbi.doc/welcome.html). You must also install the IBM Cognos Analytics Software Development Kit component before you install Capacity Management Analytics.IBM SPSS Modeler Server 17.1 SPSS Modeler Server must be configured to use the DB2 ODBC driver connection. You cannot use Data Direct. For more information, see Configuring IBM SPSS Modeler Server with DB2 CLI/ODBC on Linux on System z operating systems (Configuring IBM SPSS Modeler Server with DB2 CLI/ODBC on Linux operating systems) The SPSS Modeler Server installation must include Modeler Batch. For more information, see IBM SPSS Modeler V17.1.0 documentation For information about the required ports for SPSS Modeler, see the maximum and minimum server port information IBM SPSS Modeler V17.1.0 documentationIBM ILOG CPLEX Optimization Studio 12.6.3 For more information about IBM ILOG CPLEX Optimization Studio, see IBM ILOG CPLEX Optimization Studio V12.6.3 documentationIBM DB2 Enterprise Server 10.5 For more information about IBM Data Server Client, see IBM DB2 10.5 for Linux, Unix and Windows documentation Important: Your license for DB2 must include connectivity to DB2 for z/OS.IBM DB2 Analytics Accelerator (IDAA) IDAA with Capacity Management Analytics is described here: Use IBM DB2 Analytics Accelerator (IDAA) with IBM Capacity Management AnalyticsTo use IBM DB2 Analytics Accelerator (IDAA) with IBM Capacity Management Analytics, you can either configure ZPARMs in your DB2 configuration or enable IDAA in IBM Cognos Business Intelligence and IBM SPSS Modeler.DB2 package requirements for SLES and RHEL distributions The library libstdc++.so.6 is required for DB2 database servers and clients. For more information, see IBM DB2 10.5 for Linux, Unix and Windows documentationPreparation of your environmentBefore you can install IBM Capacity Management Analytics, you must ensure that your environment is set up. Preparing your environment includes applying settings for Tivoli Decision Support for z/OS, identifying the Tivoli Decision Support for z/OS schemas that Capacity Management Analytics requires, creating users and groups, and configuring IBM SPSS Modeler for Capacity Management Analytics.

Prerequisite software for installing Capacity Management Analytics

To run IBM® Capacity Management Analytics, you must have certain IBM software already installed.

It is recommended to use the solution installer for all prerequisite software that is supported by the solution installer including Cognos®, SPSS®, CPLEX®, and DB2® LUW. Use the documentation for the respective products to install the software that you wish to install yourself without the solution installer or is not supported by the solution installer.

Note: The solution installer does not support the installation of any z/OS® software.

The prerequisite software that is listed here are the minimum versions that are required for Capacity Management Analytics. It is recommended to always use the most recent version or fix pack of the prerequisite software.

Java

Java is included. Do not use external or unapproved Java versions.

Tivoli Decision Support for z/OS 1.8.2

Tivoli® Decision Support for z/OS version 1.8.2 is required for Capacity Management Analytics.

Note: You must apply fixes PI37340 and PI41870 for Tivoli Decision Support for z/OS version 1.8.2 for Capacity Management Analytics.

For information about upgrading to Tivoli Decision Support for z/OS version 1.8.2, see Migration from TDSz 1.8.0 and 1.8.1 to 1.8.2 (www.ibm.com/support/docview.wss?uid=swg21699114).

The need for more Tivoli Decision Support for z/OS components is determined by your environment and reporting requirements. The following Tivoli Decision Support for z/OS components are required for all installations:

z/OS Performance Management (MVSPM)

System statistics

Workload statistics

Global storage statistics

Virtual storage statistics

Device statistics

TCP/IP for z/OS

RMF™ XP Distributed

The following Tivoli Decision Support for z/OS components are required for the Capacity Management Analytics Application Analytics feature:

Key Performance Metrics - z/OS

Address Space Statistics (SMF type 30)

If Customer Information and Control System (CICS®) is installed, the following Tivoli Decision Support for z/OS components are also recommended:

CICS Monitoring Partitioned Component

CICS OMEGAMON® Monitoring Component

CICS Statistics Partitioned Component

CICS Transaction and UOW Analysis Component (required by the Anomaly Detection feature)

CICS Monitoring (required by the Application Analytics feature)

CICS Transaction Gateway Component

Additionally, the following Tivoli Decision Support for z/OS components are recommended for all installations:

z/OS System (MVS™) component

z/OS Interval Job/Step Accounting component

z/OS Availability component

Data Set component

For DB2, the Tivoli Decision Support for z/OS DB2 components are also recommended.

For more information about Tivoli Decision Support for z/OS, see IBM Knowledge Center (www.ibm.com/support/knowledgecenter/SSH53X_1.8.2).

IBM DB2 for z/OS 11

This database is required for IBM DB2 Analytics Accelerator (IDAA). For more information about IBM DB2 for z/OS, see IBM Knowledge Center DB2 for z/OS welcome page (www.ibm.com/support/knowledgecenter/SSEPEK_11.0.0/home/src/tpc/db2z_11_prodhome.html).

IBM DB2 for z/OS 10

For more information about IBM DB2 for z/OS, see IBM Knowledge Center DB2 for z/OS welcome page (www.ibm.com/support/knowledgecenter/SSEPEK_10.0.0/com.ibm.db2z10.doc/src/alltoc/db2z_10_prodhome.html).

IBM System Director Platform Agent and RMF XP

For the distributed platform monitoring capability of Capacity Management Analytics, you must use RMF XP on z/OS and install the latest version (6.3.3 or later) of IBM System Director Platform Agent on distributed operating systems.

For the Redhat xLinux 64-bit platform, install Platform Agent for RHEL 6 for Linux on IBM X86-64 on KVM.

For more information about IBM System Director Platform Agent, see the IBM System Director introduction (www.ibm.com/systems/director/).

For more information about RMF XP, see the Cross platform monitoring with RMF XP (www.ibm.com/support/knowledgecenter/SSLTBW_2.1.0/com.ibm.zos.v2r1.erbb200/gpm4cim.htm) topic on IBM Knowledge Center.

Note: RMF APAR OA45331 is required for the distributed platform monitoring capability of Capacity Management Analytics.

Note: Important: Additional prerequisite software is required if the Capacity Management Analytics solution is installed by not using the Solution Installer.

IBM Cognos Analytics 11

For information about installing IBM Cognos Analytics, see the Installation and Configuration Guide at IBM Cognos Analytics 11.0 documentation (http://www.ibm.com/support/knowledgecenter/SSEP7J_11.0.0/com.ibm.swg.ba.cognos.cbi.doc/welcome.html).

Note: You must also install the IBM Cognos Analytics Software Development Kit component before you install Capacity Management Analytics.

IBM SPSS Modeler Server 17.1

SPSS Modeler Server must be configured to use the DB2 ODBC driver connection. You cannot use Data Direct. For more information, see Configuring IBM SPSS Modeler Server with DB2 CLI/ODBC on Linux on System z® operating systems (Configuring IBM SPSS Modeler Server with DB2 CLI/ODBC on Linux operating systems)

The SPSS Modeler Server installation must include Modeler Batch. For more information, see IBM SPSS Modeler V17.1.0 documentation

For information about the required ports for SPSS Modeler, see the maximum and minimum server port information IBM SPSS Modeler V17.1.0 documentation

IBM ILOG CPLEX Optimization Studio 12.6.3

For more information about IBM ILOG® CPLEX Optimization Studio, see IBM ILOG CPLEX Optimization Studio V12.6.3 documentation

IBM DB2 Enterprise Server 10.5

For more information about IBM Data Server Client, see IBM DB2 10.5 for Linux, Unix and Windows documentation

Note: Important: Your license for DB2 must include connectivity to DB2 for z/OS.

IBM DB2 Analytics Accelerator (IDAA)

IDAA with Capacity Management Analytics is described here: Use IBM DB2 Analytics Accelerator (IDAA) with IBM Capacity Management Analytics

DB2 package requirements for SLES and RHEL distributions

The library libstdc++.so.6 is required for DB2 database servers and clients. For more information, see IBM DB2 10.5 for Linux, Unix and Windows documentation

Parent topic: Preparation of your environment

System Management Facility and Resource Measurement Facility recordsIBM Capacity Management Analytics uses the following System Management Facility (SMF) and Resource Measurement Facility (RMF) records.System Management Facility recordsSMF recordsResource Measurement Facility recordsRMF recordsThe System Management Facility (SMF) is a z/OS component that provides the means for gathering and recording information for evaluating system usage, applications, security, error conditions, and hardware resources.The Resource Measurement Facility (RMF) provides performance and usage instrumentation of hardware resources such as processor, memory, disk, cache, workload, and virtual storage. RMF records are a subset of SMF records, in the range from SMF 70 to SMF 79.The SMF and RMF records are captured and stored in the Tivoli Decision Support for z/OS (TDSz) datastore in a DB2 for z/OS database. Capacity Management Analytics uses the Tivoli Decision Support for z/OS datastore as the source for the report data.A sample file is provided to help you load the SMF data into the TDSz datastore. The sample file is named HCMCOLL, and it is in the CMA_ROOT/scripts directory where you install the Capacity Management Analytics Solution Kit. On z/OS systems, the sample file is found in the samples directory in the SMP/E installation folder.Preparation of your environmentBefore you can install IBM Capacity Management Analytics, you must ensure that your environment is set up. Preparing your environment includes applying settings for Tivoli Decision Support for z/OS, identifying the Tivoli Decision Support for z/OS schemas that Capacity Management Analytics requires, creating users and groups, and configuring IBM SPSS Modeler for Capacity Management Analytics.SMF and RMF records by featureThe following information shows the SMF and RMF records that are used by the different Capacity Management Analytics features and the Capacity Management Analytics objects and database tables that the SMF and RMF records correlate to.SMF and RMF records by componentThe following information shows the SMF and RMF records that are used by the different Capacity Management Analytics components and the Capacity Management Analytics objects and database tables that the SMF and RMF records correlate to.

System Management Facility and Resource Measurement Facility records

IBM® Capacity Management Analytics uses the following System Management Facility (SMF) and Resource Measurement Facility™ (RMF™) records.

The System Management Facility (SMF) is a z/OS® component that provides the means for gathering and recording information for evaluating system usage, applications, security, error conditions, and hardware resources.

The Resource Measurement Facility (RMF) provides performance and usage instrumentation of hardware resources such as processor, memory, disk, cache, workload, and virtual storage. RMF records are a subset of SMF records, in the range from SMF 70 to SMF 79.

The SMF and RMF records are captured and stored in the Tivoli® Decision Support for z/OS (TDSz) datastore in a DB2® for z/OS database. Capacity Management Analytics uses the Tivoli Decision Support for z/OS datastore as the source for the report data.

Tip: A sample file is provided to help you load the SMF data into the TDSz datastore. The sample file is named HCMCOLL, and it is in the CMA_ROOT/scripts directory where you install the Capacity Management Analytics Solution Kit. On z/OS systems, the sample file is found in the samples directory in the SMP/E installation folder.

SMF and RMF records by feature
The following information shows the SMF and RMF records that are used by the different Capacity Management Analytics features and the Capacity Management Analytics objects and database tables that the SMF and RMF records correlate to.

SMF and RMF records by component
The following information shows the SMF and RMF records that are used by the different Capacity Management Analytics components and the Capacity Management Analytics objects and database tables that the SMF and RMF records correlate to.

Parent topic: Preparation of your environment

SMF and RMF records by featureThe following information shows the SMF and RMF records that are used by the different Capacity Management Analytics features and the Capacity Management Analytics objects and database tables that the SMF and RMF records correlate to.SMF 30SMF 70SMF 71SMF 72SMF 73SMF 74SMF 78SMF 89SMF 104SMF 110SMF 119SMF 30 SMF 30 record IDTDS TablesTDS TablespacesDescription and VolumeCMA featuresKPMZ_JOB_INT_HDRLSKZJ2Key Performance Indicators on address space High volumeApplication Analytics (AA)SMF 70 SMF 70 record IDTDS TablesTDS TablespacesDescription and VolumeCMA featuresMVSPM_LPAR_H MVSPM_SYSTEM_H MVSPM_LPAR_MSU_T MVSPM_CLUSTER_HDRLSMP14 DRLSMP26 DRLSMP4A DRLSMP40RMF processor activity Low volumeBase System Management & Optimization (SMO) Capacity Planning & Forecasts (CPF) Application Analytics (AA) Software Cost Analysis (SCA)SMF 71 SMF 71 record IDTDS TablesTDS TablespacesDescription and VolumeCMA featuresMVSPM_PAGING_HDRLSMP22RMF paging activity Low volumeSystem Management & Optimization (SMO)SMF 72 SMF 72 record IDTDS TablesTDS TablespacesDescription and VolumeCMA featuresMVSPM_GOAL_ACT_H MVSPM_WORKLOAD2_HDRLSMP13 DRLSMP35RMF workload activity and storage data High volumeSystem Management & Optimization (SMO) Capacity Planning & Forecasts (CPF) Application Analytics (AA)SMF 73 SMF 73 record IDTDS TablesTDS TablespacesDescription and VolumeCMA featuresMVSPM_CHANNEL_HDRLSMP07RMF channel path activity High volumeBase System Management & Optimization (SMO)SMF 74 SMF 74 record IDTDS TablesTDS TablespacesDescription and VolumeCMA featuresMVSPM_DEVICE_HDRLSMP11RMF activity of resources High volumeBase System Management & Optimization (SMO)SMF 78 SMF 78 record IDTDS TablesTDS TablespacesDescription and VolumeCMA featuresMVSPM_VS_CSASQA_HDRLSMP29RMF virtual storage and I/O queuing activity Low volumeSystem Management & Optimization (SMO)SMF 89 SMF 89 record IDTDS TablesTDS TablespacesDescription and VolumeCMA featuresMVSPM_PROD_T MVSPM_PROT_INT_TDRLSMP4B DRLSMP4CProduct MSU utilization MVSPM_ PROD_T: low to medium volume depending on how many Sub Capacity products are running MVSPM_ PROD_INT_T: medium to high volume depending on how many Sub Capacity products are running and the frequency of the SMF interval recordsSoftware Cost Analysis (SCA)SMF 104 SMF 104 record IDTDS TablesTDS TablespacesDescription and VolumeCMA featuresZ_OPERATING_SYS_T Z_OPERATING_SYS_H X_OPERATING_SYS_T X_OPERATING_SYS_H A_COMPUTER_SYS_T A_COMPUTER_SYS_H A_OPERATING_SYS_T A_MEMORY_T A_MEM_EXPANSION_T W_OPERATING_SYS_T W_OPERATING_SYS_H W_PROCESSOR_TS104*Distributed systems – CIM agents Low volumeApplication Analytics (AA) System Management & Optimization (SMO)SMF 110 SMF 110 record IDTDS TablesTDS TablespacesDescription and VolumeCMA featuresCICS_T_TRAN_T CICS_TRANSACTION_HDRLSCU01 DRLSC801CICS statistics High volumeApplication Analytics (AA) Problem Identification (PI)SMF 119 SMF 119 record IDTDS TablesTDS TablespacesDescription and VolumeCMA featuresTCP_TCPSERV_PORT_HDRLSTCPOHourly TCP/IP statistics about port connection activity High volumeSystem Management & Optimization (SMO)System Management Facility and Resource Measurement Facility recordsIBM Capacity Management Analytics uses the following System Management Facility (SMF) and Resource Measurement Facility (RMF) records.

SMF and RMF records by feature

The following information shows the SMF and RMF records that are used by the different Capacity Management Analytics features and the Capacity Management Analytics objects and database tables that the SMF and RMF records correlate to.

SMF 30

Table 1. SMF 30 record ID

TDS Tables
TDS Tablespaces
Description and Volume
CMA features

KPMZ_JOB_INT_H

DRLSKZJ2

Key Performance Indicators on address space

High volume

Application Analytics (AA)

SMF 70

Table 2. SMF 70 record ID

TDS Tables
TDS Tablespaces
Description and Volume
CMA features

MVSPM_LPAR_H

MVSPM_SYSTEM_H

MVSPM_LPAR_MSU_T

MVSPM_CLUSTER_H

DRLSMP14

DRLSMP26

DRLSMP4A

DRLSMP40

RMF™ processor activity

Low volume

Base

System Management & Optimization (SMO)

Capacity Planning & Forecasts (CPF)

Application Analytics (AA)

Software Cost Analysis (SCA)

SMF 71

Table 3. SMF 71 record ID

TDS Tables
TDS Tablespaces
Description and Volume
CMA features

MVSPM_PAGING_H

DRLSMP22

RMF paging activity

Low volume

System Management & Optimization (SMO)

SMF 72

Table 4. SMF 72 record ID

TDS Tables
TDS Tablespaces
Description and Volume
CMA features

MVSPM_GOAL_ACT_H

MVSPM_WORKLOAD2_H

DRLSMP13

DRLSMP35

RMF workload activity and storage data

High volume

System Management & Optimization (SMO)

Capacity Planning & Forecasts (CPF)

Application Analytics (AA)

SMF 73

Table 5. SMF 73 record ID

TDS Tables
TDS Tablespaces
Description and Volume
CMA features

MVSPM_CHANNEL_H

DRLSMP07

RMF channel path activity

High volume

Base

System Management & Optimization (SMO)

SMF 74

Table 6. SMF 74 record ID

TDS Tables
TDS Tablespaces
Description and Volume
CMA features

MVSPM_DEVICE_H

DRLSMP11

RMF activity of resources

High volume

Base

System Management & Optimization (SMO)

SMF 78

Table 7. SMF 78 record ID

TDS Tables
TDS Tablespaces
Description and Volume
CMA features

MVSPM_VS_CSASQA_H

DRLSMP29

RMF virtual storage and I/O queuing activity

Low volume

System Management & Optimization (SMO)

SMF 89

Table 8. SMF 89 record ID

TDS Tables
TDS Tablespaces
Description and Volume
CMA features

MVSPM_PROD_T

MVSPM_PROT_INT_T

DRLSMP4B

DRLSMP4C

Product MSU utilization

MVSPM_ PROD_T: low to medium volume depending on how many Sub Capacity products are running

MVSPM_ PROD_INT_T: medium to high volume depending on how many Sub Capacity products are running and the frequency of the SMF interval records

Software Cost Analysis (SCA)

SMF 104

Table 9. SMF 104 record ID

TDS Tables
TDS Tablespaces
Description and Volume
CMA features

Z_OPERATING_SYS_T

Z_OPERATING_SYS_H

X_OPERATING_SYS_T

X_OPERATING_SYS_H

A_COMPUTER_SYS_T

A_COMPUTER_SYS_H

A_OPERATING_SYS_T

A_MEMORY_T

A_MEM_EXPANSION_T

W_OPERATING_SYS_T

W_OPERATING_SYS_H

W_PROCESSOR_T

S104*

Distributed systems – CIM agents

Low volume

Application Analytics (AA)

System Management & Optimization (SMO)

SMF 110

Table 10. SMF 110 record ID

TDS Tables
TDS Tablespaces
Description and Volume
CMA features

CICS_T_TRAN_T

CICS_TRANSACTION_H

DRLSCU01

DRLSC801

CICS® statistics

High volume

Application Analytics (AA)

Problem Identification (PI)

SMF 119

Table 11. SMF 119 record ID

TDS Tables
TDS Tablespaces
Description and Volume
CMA features

TCP_TCPSERV_PORT_H

DRLSTCPO

Hourly TCP/IP statistics about port connection activity

High volume

System Management & Optimization (SMO)

Parent topic: System Management Facility and Resource Measurement Facility records

SMF and RMF records by componentThe following information shows the SMF and RMF records that are used by the different Capacity Management Analytics components and the Capacity Management Analytics objects and database tables that the SMF and RMF records correlate to.SMF 30SMF 70SMF 71SMF 72SMF 73SMF 74SMF 78SMF 89SMF 104SMF 110SMF 119Base components Tivoli Decision Support control and lookup tablesMVS_MIPS_TMVSPM_TIME_RESDAY_OF_WEEKPERIOD_PLANSPECIAL_DAYFor more information about the control and lookup tables, see Update Tivoli Decision Support for z/OS control and lookup tablesIBM Tivoli Decision Support for z/OS control and lookup tables provide information about your environment..CMA streamsdistribution_procedure.strmvspm_lpar_clone.strmvspm_lpar_insert.strmvspm_lpar_refresh_full.strmvspm_lpar_refresh_roll.strmvspm_lpar_update.strSources tablesDRL.MVSPM_LPAR_HDRL.MVSPM_SYSTEM_HHCM.MVSPM_LPARSMF source tablesMVSPM_LPAR_HMVSPM_SYSTEM_HMVSPM_CHANNEL_HMVSPM_DEVICE_HSMF recordsSMF 70SMF 73SMF 74System management and optimization components The following list shows the Capacity Management Analytics streams with the source tables and SMF records that they use. CMA streamschannel_top_etl.strchannel_top_etl_refresh_full.strchannel_top_etl_refresh_roll.strdevice_lpar_agg_etl.strdevice_lpar_agg_etl_refresh_full.strdevice_lpar_agg_etl_refresh_roll.strdevice_top_etl.strdevice_top_etl_refresh_full.strdevice_top_etl_refresh_roll.str lwo.jarSources tablesDRL.MVSPM_LPAR_HDRL.MVSPM_SYSTEM_HDRL.MVSPM_CHANNEL_HDRL.MVSPM_DEVICE_HDRL.MVSPM_WORKLOAD2_HHCM.MVSPM_LPARHCM.MVSPM_CHANNELHCM.MVSPM_DEVICESMF source tablesMVSPM_LPAR_HMVSPM_SYSTEM_HMVSPM_WORKLOAD2_HMVSPM_CHANNEL_HMVSPM_DEVICE_HSMF recordsSMF 70SMF 72SMF 73SMF 74 The following list shows the Capacity Management Analytics reports with the source tables and SMF records that they use. CMA reportsCPU: AIX - 10 Highest/Lowest CPU Usage PeaksCPU: AIX - CPU UsageCPU: Latent DemandCPU: Linux for System X - 10 Highest/Lowest CPU Usage PeaksCPU: Linux for System X - CPU UsageCPU: Linux for System Z - 10 Highest/Lowest CPU Usage PeaksCPU: Linux for System Z - CPU UsageCPU: MIPS Used - LPAR Level by WLM ImportanceCPU: MIPS Used - Service Class Period LevelCPU: MIPS Used - System Level (Captured vs Uncaptured)CPU: MIPS Used - zServer/LPAR LevelCPU: Over/Under Shared Weight - CPC by LPARCPU: LPAR Weight Optimization Run ResultCPU: Percentage of Servers with High/Low UsageCPU: Servers Reporting by Operating System - Enterprise LevelCPU: Windows - 10 Highest/Lowest CPU Usage PeaksCPU: Windows - CPU UsageCPU: z/OS - 10 Highest/Lowest CPU Usage PeaksMemory: AIX - Memory UsagesMemory: CSA/ECSA/SQA/ESQA UtilizationMemory: Linux for System X - Memory UsageMemory: Linux for System Z - Memory UsageMemory: Windows - Memory UsageWLM: Delays by Importance LevelWLM: Delays by Service Class PeriodWorkspace: Enterprise DashboardWorkspace: zServer Monitoring DashboardSources tablesDRL.MVSPM_LPAR_HDRL.MVSPM_SYSTEM_HDRL.MVSPM_PAGING_HDRL.MVSPM_WORKLOAD2_HDRL.MVSPM_GOAL_ACT_HDRL.MVSPM_CHANNEL_HDRL.MVSPM_DEVICE_HDRL.MVSPM_VS_CSASQA_HDRL.X_OPERATING_SYS_HDRL.X_OPERATING_SYS_TDRL.Z_OPERATING_SYS_HDRL.Z_OPERATING_SYS_TDRL.W_OPERATING_SYS_HDRL.W_OPERATING_SYS_TDRL.W_PROCESSOR_TDRL.A_COMPUTER_SYS_HDRL.A_COMPUTER_SYS_TDRL.A_COMPUTER_SYS_TDRL.A_OPERATING_SYS_TDRL.A_MEMORY_TDRL.A_MEM_EXPANSION_TDRL.TCP_TCPSERV_PORT_HHCM.MVSPM_LPARHCM.LPAR_WEIGHT_OPTIMIZATIONHCM.OPTIMIZATION_METADATAHCM.LPAR_WEIGHT_OPTIMIZATION_PARAMETERSHCM.LPAR_WEIGHT_OPTIMIZATION_DETAILHCM.TIMEZONESHCM.DIM_DATEHCM.MVSPM_LPAR_CPU_BUSY_MIPS_PREDHCM.MVSPM_CPC_CPU_BUSY_MIPS_PREDHCM.FORECAST_METEDATAHCM.MVSMP_DEVICESMF source tablesMVSPM_LPAR_HMVSPM_SYSTEM_HMVSPM_PAGING_HMVSPM_WORKLOAD2_HMVSPM_GOAL_ACT_HMVSPM_CHANNEL_HMVSPM_DEVICE_HX_OPERATING_SYS_HX_OPERATING_SYS_TZ_OPERATING_SYS_HZ_OPERATING_SYS_TW_OPERATING_SYS_HW_OPERATING_SYS_TW_PROCESSOR_TA_COMPUTER_SYS_HA_COMPUTER_SYS_TTCP_TCPSERV_PORT_H SMF recordsSMF 70SMF 71SMF 72SMF 73SMF 74SMF 78SMF 104SMF 119Capacity planning and forecast component The following list shows the Capacity Management Analytics streams with the source tables and SMF records that they use. CMA streamsmvspm-lpar-daily-forecast-timeseries.strmvspm-lpar-hourly-forecast-timeseries.strmvspm-lpar-monthly-forecast-timeseries.strmvspm-lpar-daily-peak-forecast-timeseries.strmvspm-cpc-daily-peak-forecast-timeseries.strSources tablesHCM.MVSPM_LPARSMF source tablesMVSPM_LPAR_HSMF recordsSMF 70 The following list shows the Capacity Management Analytics reports with the source tables and SMF records that they use. CMA reportsCPU: MIPS Used - LPAR Level by WLM ImportanceCPU: MIPS Used - zServer/LPAR Level w/ForecastWorkspace: zIIP/zAAP What ifs - LPAR LevelSources tablesDRL.MVSPM_LPAR_HDRL MVSPM_SYSTEM_HDRL MVSPM_WORKLOAD2_HHCM.MVSPM_LPARHCM.MVSPM_LPAR_CPU_BUSY_MIPS_PREDHCM.FORECAST_METADATAHCM.MVSPM_CPC_CPU_BUSY_MIPS_PRED HCM.LPAR_WEIGHT_OPTIMIZATIONSMF source tablesMVSPM_LPAR_HMVSPM_SYSTEM_HMVSPM_WORKLOAD2_HSMF recordsSMF 70SMF 72Application analytics component The following list shows the Capacity Management Analytics streams with the source tables and SMF records that they use. CMA streamsappl_capture_ratio.strappl_lob.strappl_lob_z.strappl_avg_daily_forecast_timeseries.strappl_avg_hourly_forecast_timeseries.strappl_peak_daily_forecast_timeseries.strSources tablesDRL.X_OPERATING_SYS_HDRL.A_OPERATING_SYS_HDRL.Z_OPERATING_SYS_HDRL.W_OPERATING_SYS_HDRL.CICS_TRANSACTION_HDRL.KPMZ_JOB_INT_HHCM.APPL_MAPPINGHCM.APPL_UTIL_ZOS_VIEWHCM.APPL_DIST_MAPPINGHCM.MIPS_CAPACITYSMF source tablesKPMZ_JOB_INT_HMVSPM_WORKLOAD2_HX_OPERATING_SYS_HA_OPERATING_SYS_HZ_OPERATING_SYS_HW_OPERATING_SYS_HCICS_TRANSACTION_HSMF recordsSMF 30SMF 72SMF 104SMF 110 The following list shows the Capacity Management Analytics reports with the source tables and SMF records that they use. CMA reportsApplication Analytics: Distributed CPU Usage - ApplicationsApplication Analytics: Distributed CPU Usage - Application SummaryApplication Analytics: MIPS Used - ApplicationApplication Analytics: MIPS Used - Application by FunctionApplication Analytics: MIPS Used - Application by LPAR Application Analytics: MIPS Used - Report Class by Job NamesSources tablesDRL.KPMZ_JOB_INT_HDRL. MVSPM_LPAR_HDRL. MVSPM_SYSTEM_HDRL.MVSPM_WORKLOAD2_HDRL. X_OPERATING_SYS_HDRL. Z_OPERATING_SYS_HDRL.W_OPERATING_SYS_HDRL. A_COMPUTER_SYS_HHCM.APPL_DIST_MAPPINGHCM.TIMEZONESHCM.MVSPM_LPARHCM.DIM_TIMEHCM.DIM_DATEHCM.APPL_MAPPINGHCM.MIPS_CAPACITYSMF source tablesKPMZ_JOB_INT_HMVSPM_LPAR_HMVSPM_SYSTEM_HMVSPM_WORKLOAD2_HX_OPERATING_SYS_HZ_OPERATING_SYS_HW_OPERATING_SYS_HA_COMPUTER_SYS_HSMF recordsSMF 30SMF 72SMF 104SMF 110Software cost analysis component The following list shows the Capacity Management Analytics streams with the source tables and SMF records that they use. CMA streamssca_no89.strsca_pricing.strsca_subcap.strsca_tiers.strmsu_gssp_forecast_timeseries.strmsu_lpar_forecast_timeseries.strmsu_prod_forecast_timeseries.strprod_bill_msu_calculate.strprod_bill_msu_optimization.strSources tablesDRL.MVSPM_CLUSTER_HHCM.PARENT_PROGRAMSHCM.SUBCAP_PROGRAMSHCM.MSU_TIERSHCM.VUE_TIERSHCM.CUSTOMER_PRICEHCM.MVSPM_LPARHCM.NO89_PRODUCTSHCM.MVSPM_PROD_VIEWHCM.LPAR_MSU_FORECASTHCM.PROD_MSU_FORECASTSMF source tablesMVSPM_LPAR_MSU_TMVSPM_CLUSTER_HMVSPM_LPAR_HMVSPM_PROD_INT_TMVSPM_PROD_TSMF recordsSMF 70SMF 89 The following list shows the Capacity Management Analytics reports with the source tables and SMF records that they use. CMA reportsSCA: LPAR MSU UtilizationSCA: NO89 Product MSU and PriceSCA: NO89 Products MatrixSCA: Product MSU and PricesSCA: Registered Products MatrixWorkspace: SCA: Software Cost Analysis SummarySources tablesDRL.MVSPM_LPAR_HDRL.MVSPM_SYSTEM_HDRL.MVSPM_LPAR_MSU_TDRL.MVSPM_PROD_TDRL.MVSPM_PROD_INT_THCM.MVSPM_LPARHCM.PARENT_PROGRAMSHCM.SUBCAP_PROGRAMSHCM.PROD_MSU_FORECASTHCM.PROD_MSU_OPTIMIZATIONHCM.LPAR_MSU_FORECASTHCM.LPAR_MSU_OPTIMIZATIONHCM.FORECAST_BILLABLEHCM.OPTIMIZATION_BILLABLEHCM.OPTIMIZATION_MOVEMENTHCM.NO89_PRODUCTSHCM.CUSTOMER_PRICEHCM.TIMEZONESSMF source tablesMVSPM_LPAR_HMVSPM_SYSTEM_HMVSPM_LPAR_MSU_TMVSPM_PROD_TMVSPM_PROD_INT_TSMF recordsSMF 70SMF 89Problem identification component The following list shows the Capacity Management Analytics streams with the source tables and SMF records that they use. CMA streamsanomaly_classification.stranomaly_clean_up.stranomaly_detect_building_c.stranomaly_detect_building_r.stranomaly_detect_scoring.stranomaly_detect_treebased.strSources tablesDRL.CICS_T_TRAN_THCM.ANOMALIES_CICSSMF source tablesCICS_T_TRAN_TSMF recordsSMF 110 The following list shows the Capacity Management Analytics reports with the source tables and SMF records that they use. CMA reportsAnomaly Detection Analytics: CICS Anomaly DetailsSources tablesDRL.CICS_T_TRAN_THCM.ANOMALIES_CICSHCM.DIM_MINUTESMF source tablesCICS_T_TRAN_TSMF recordsSMF 110System Management Facility and Resource Measurement Facility recordsIBM Capacity Management Analytics uses the following System Management Facility (SMF) and Resource Measurement Facility (RMF) records.

SMF and RMF records by component

The following information shows the SMF and RMF records that are used by the different Capacity Management Analytics components and the Capacity Management Analytics objects and database tables that the SMF and RMF records correlate to.

Base components

Tivoli Decision Support control and lookup tables

MVS_MIPS_T

MVSPM_TIME_RES

DAY_OF_WEEK

PERIOD_PLAN

SPECIAL_DAY

For more information about the control and lookup tables, see Update Tivoli Decision Support for z/OS control and lookup tables.

CMA streams

distribution_procedure.str

mvspm_lpar_clone.str

mvspm_lpar_insert.str

mvspm_lpar_refresh_full.str

mvspm_lpar_refresh_roll.str

mvspm_lpar_update.str

Sources tables

DRL.MVSPM_LPAR_H

DRL.MVSPM_SYSTEM_H

HCM.MVSPM_LPAR

SMF source tables

MVSPM_LPAR_H

MVSPM_SYSTEM_H

MVSPM_CHANNEL_H

MVSPM_DEVICE_H

SMF records

SMF 70

SMF 73

SMF 74

System management and optimization components

The following list shows the Capacity Management Analytics streams with the source tables and SMF records that they use.

CMA streams

channel_top_etl.str

channel_top_etl_refresh_full.str

channel_top_etl_refresh_roll.str

device_lpar_agg_etl.str

device_lpar_agg_etl_refresh_full.str

device_lpar_agg_etl_refresh_roll.str

device_top_etl.str

device_top_etl_refresh_full.str

device_top_etl_refresh_roll.str

lwo.jar

Sources tables

DRL.MVSPM_LPAR_H

DRL.MVSPM_SYSTEM_H

DRL.MVSPM_CHANNEL_H

DRL.MVSPM_DEVICE_H

DRL.MVSPM_WORKLOAD2_H

HCM.MVSPM_LPAR

HCM.MVSPM_CHANNEL

HCM.MVSPM_DEVICE

SMF source tables

MVSPM_LPAR_H

MVSPM_SYSTEM_H

MVSPM_WORKLOAD2_H

MVSPM_CHANNEL_H

MVSPM_DEVICE_H

SMF records

SMF 70

SMF 72

SMF 73

SMF 74

The following list shows the Capacity Management Analytics reports with the source tables and SMF records that they use.

CMA reports

CPU: AIX - 10 Highest/Lowest CPU Usage Peaks

CPU: AIX - CPU Usage

CPU: Latent Demand

CPU: Linux for System X - 10 Highest/Lowest CPU Usage Peaks

CPU: Linux for System X - CPU Usage

CPU: Linux for System Z - 10 Highest/Lowest CPU Usage Peaks

CPU: Linux for System Z - CPU Usage

CPU: MIPS Used - LPAR Level by WLM Importance

CPU: MIPS Used - Service Class Period Level

CPU: MIPS Used - System Level (Captured vs Uncaptured)

CPU: MIPS Used - zServer/LPAR Level

CPU: Over/Under Shared Weight - CPC by LPAR

CPU: LPAR Weight Optimization Run Result

CPU: Percentage of Servers with High/Low Usage

CPU: Servers Reporting by Operating System - Enterprise Level

CPU: Windows - 10 Highest/Lowest CPU Usage Peaks

CPU: Windows - CPU Usage

CPU: z/OS - 10 Highest/Lowest CPU Usage Peaks

Memory: AIX - Memory Usages

Memory: CSA/ECSA/SQA/ESQA Utilization

Memory: Linux for System X - Memory Usage

Memory: Linux for System Z - Memory Usage

Memory: Windows - Memory Usage

WLM: Delays by Importance Level

WLM: Delays by Service Class Period

Workspace: Enterprise Dashboard

Workspace: zServer Monitoring Dashboard

Sources tables

DRL.MVSPM_LPAR_H

DRL.MVSPM_SYSTEM_H

DRL.MVSPM_PAGING_H

DRL.MVSPM_WORKLOAD2_H

DRL.MVSPM_GOAL_ACT_H

DRL.MVSPM_CHANNEL_H

DRL.MVSPM_DEVICE_H

DRL.MVSPM_VS_CSASQA_H

DRL.X_OPERATING_SYS_H

DRL.X_OPERATING_SYS_T

DRL.Z_OPERATING_SYS_H

DRL.Z_OPERATING_SYS_T

DRL.W_OPERATING_SYS_H

DRL.W_OPERATING_SYS_T

DRL.W_PROCESSOR_T

DRL.A_COMPUTER_SYS_H

DRL.A_COMPUTER_SYS_T

DRL.A_COMPUTER_SYS_T

DRL.A_OPERATING_SYS_T

DRL.A_MEMORY_T

DRL.A_MEM_EXPANSION_T

DRL.TCP_TCPSERV_PORT_H

HCM.MVSPM_LPAR

HCM.LPAR_WEIGHT_OPTIMIZATION

HCM.OPTIMIZATION_METADATA

HCM.LPAR_WEIGHT_OPTIMIZATION_PARAMETERS

HCM.LPAR_WEIGHT_OPTIMIZATION_DETAIL

HCM.TIMEZONES

HCM.DIM_DATE

HCM.MVSPM_LPAR_CPU_BUSY_MIPS_PRED

HCM.MVSPM_CPC_CPU_BUSY_MIPS_PRED

HCM.FORECAST_METEDATA

HCM.MVSMP_DEVICE

SMF source tables

MVSPM_LPAR_H

MVSPM_SYSTEM_H

MVSPM_PAGING_H

MVSPM_WORKLOAD2_H

MVSPM_GOAL_ACT_H

MVSPM_CHANNEL_H

MVSPM_DEVICE_H

X_OPERATING_SYS_H

X_OPERATING_SYS_T

Z_OPERATING_SYS_H

Z_OPERATING_SYS_T

W_OPERATING_SYS_H

W_OPERATING_SYS_T

W_PROCESSOR_T

A_COMPUTER_SYS_H

A_COMPUTER_SYS_T

TCP_TCPSERV_PORT_H

SMF records

SMF 70

SMF 71

SMF 72

SMF 73

SMF 74

SMF 78

SMF 104

SMF 119

Capacity planning and forecast component

The following list shows the Capacity Management Analytics streams with the source tables and SMF records that they use.

CMA streams

mvspm-lpar-daily-forecast-timeseries.str

mvspm-lpar-hourly-forecast-timeseries.str

mvspm-lpar-monthly-forecast-timeseries.str

mvspm-lpar-daily-peak-forecast-timeseries.str

mvspm-cpc-daily-peak-forecast-timeseries.str

Sources tables

HCM.MVSPM_LPAR

SMF source tables

MVSPM_LPAR_H

SMF records

SMF 70

The following list shows the Capacity Management Analytics reports with the source tables and SMF records that they use.

CMA reports

CPU: MIPS Used - LPAR Level by WLM Importance

CPU: MIPS Used - zServer/LPAR Level w/Forecast

Workspace: zIIP/zAAP What ifs - LPAR Level

Sources tables

DRL.MVSPM_LPAR_H

DRL MVSPM_SYSTEM_H

DRL MVSPM_WORKLOAD2_H

HCM.MVSPM_LPAR

HCM.MVSPM_LPAR_CPU_BUSY_MIPS_PRED

HCM.FORECAST_METADATA

HCM.MVSPM_CPC_CPU_BUSY_MIPS_PRED

HCM.LPAR_WEIGHT_OPTIMIZATION

SMF source tables

MVSPM_LPAR_H

MVSPM_SYSTEM_H

MVSPM_WORKLOAD2_H

SMF records

SMF 70

SMF 72

Application analytics component

The following list shows the Capacity Management Analytics streams with the source tables and SMF records that they use.

CMA streams

appl_capture_ratio.str

appl_lob.strappl_lob_z.str

appl_avg_daily_forecast_timeseries.str

appl_avg_hourly_forecast_timeseries.str

appl_peak_daily_forecast_timeseries.str

Sources tables

DRL.X_OPERATING_SYS_H

DRL.A_OPERATING_SYS_H

DRL.Z_OPERATING_SYS_H

DRL.W_OPERATING_SYS_H

DRL.CICS_TRANSACTION_H

DRL.KPMZ_JOB_INT_H

HCM.APPL_MAPPING

HCM.APPL_UTIL_ZOS_VIEW

HCM.APPL_DIST_MAPPING

HCM.MIPS_CAPACITY

SMF source tables

KPMZ_JOB_INT_H

MVSPM_WORKLOAD2_H

X_OPERATING_SYS_H

A_OPERATING_SYS_H

Z_OPERATING_SYS_H

W_OPERATING_SYS_H

CICS_TRANSACTION_H

SMF records

SMF 30

SMF 72

SMF 104

SMF 110

The following list shows the Capacity Management Analytics reports with the source tables and SMF records that they use.

CMA reports

Application Analytics: Distributed CPU Usage - Applications

Application Analytics: Distributed CPU Usage - Application Summary

Application Analytics: MIPS Used - Application

Application Analytics: MIPS Used - Application by Function

Application Analytics: MIPS Used - Application by LPAR

Application Analytics: MIPS Used - Report Class by Job Names

Sources tables

DRL.KPMZ_JOB_INT_H

DRL. MVSPM_LPAR_H

DRL. MVSPM_SYSTEM_H

DRL.MVSPM_WORKLOAD2_H

DRL. X_OPERATING_SYS_H

DRL. Z_OPERATING_SYS_H

DRL.W_OPERATING_SYS_H

DRL. A_COMPUTER_SYS_H

HCM.APPL_DIST_MAPPING

HCM.TIMEZONES

HCM.MVSPM_LPAR

HCM.DIM_TIME

HCM.DIM_DATE

HCM.APPL_MAPPING

HCM.MIPS_CAPACITY

SMF source tables

KPMZ_JOB_INT_H

MVSPM_LPAR_H

MVSPM_SYSTEM_H

MVSPM_WORKLOAD2_H

X_OPERATING_SYS_H

Z_OPERATING_SYS_H

W_OPERATING_SYS_H

A_COMPUTER_SYS_H

SMF records

SMF 30

SMF 72

SMF 104

SMF 110

Software cost analysis component

The following list shows the Capacity Management Analytics streams with the source tables and SMF records that they use.

CMA streams

sca_no89.str

sca_pricing.str

sca_subcap.str

sca_tiers.str

msu_gssp_forecast_timeseries.str

msu_lpar_forecast_timeseries.str

msu_prod_forecast_timeseries.str

prod_bill_msu_calculate.str

prod_bill_msu_optimization.str

Sources tables

DRL.MVSPM_CLUSTER_H

HCM.PARENT_PROGRAMS

HCM.SUBCAP_PROGRAMS

HCM.MSU_TIERS

HCM.VUE_TIERS

HCM.CUSTOMER_PRICE

HCM.MVSPM_LPAR

HCM.NO89_PRODUCTS

HCM.MVSPM_PROD_VIEW

HCM.LPAR_MSU_FORECAST

HCM.PROD_MSU_FORECAST

SMF source tables

MVSPM_LPAR_MSU_T

MVSPM_CLUSTER_H

MVSPM_LPAR_H

MVSPM_PROD_INT_T

MVSPM_PROD_T

SMF records

SMF 70

SMF 89

The following list shows the Capacity Management Analytics reports with the source tables and SMF records that they use.

CMA reports

SCA: LPAR MSU Utilization

SCA: NO89 Product MSU and Price

SCA: NO89 Products Matrix

SCA: Product MSU and Prices

SCA: Registered Products Matrix

Workspace: SCA: Software Cost Analysis Summary

Sources tables

DRL.MVSPM_LPAR_H

DRL.MVSPM_SYSTEM_H

DRL.MVSPM_LPAR_MSU_T

DRL.MVSPM_PROD_T

DRL.MVSPM_PROD_INT_T

HCM.MVSPM_LPAR

HCM.PARENT_PROGRAMS

HCM.SUBCAP_PROGRAMS

HCM.PROD_MSU_FORECAST

HCM.PROD_MSU_OPTIMIZATION

HCM.LPAR_MSU_FORECAST

HCM.LPAR_MSU_OPTIMIZATION

HCM.FORECAST_BILLABLE

HCM.OPTIMIZATION_BILLABLE

HCM.OPTIMIZATION_MOVEMENT

HCM.NO89_PRODUCTS

HCM.CUSTOMER_PRICE

HCM.TIMEZONES

SMF source tables

MVSPM_LPAR_H

MVSPM_SYSTEM_H

MVSPM_LPAR_MSU_T

MVSPM_PROD_T

MVSPM_PROD_INT_T

SMF records

SMF 70

SMF 89

Problem identification component

The following list shows the Capacity Management Analytics streams with the source tables and SMF records that they use.

CMA streams

anomaly_classification.str

anomaly_clean_up.str

anomaly_detect_building_c.str

anomaly_detect_building_r.str

anomaly_detect_scoring.str

anomaly_detect_treebased.str

Sources tables

DRL.CICS_T_TRAN_T

HCM.ANOMALIES_CICS

SMF source tables

CICS_T_TRAN_T

SMF records

SMF 110

The following list shows the Capacity Management Analytics reports with the source tables and SMF records that they use.

CMA reports

Anomaly Detection Analytics: CICS Anomaly Details

Sources tables

DRL.CICS_T_TRAN_T

HCM.ANOMALIES_CICS

HCM.DIM_MINUTE

SMF source tables

CICS_T_TRAN_T

SMF records

SMF 110

Parent topic: System Management Facility and Resource Measurement Facility records

Settings for Tivoli Decision Support for z/OSFor IBM Capacity Management Analytics reports, you must apply certain settings for your IBM Tivoli Decision Support for z/OS data store.Tivoli Decision Support for z/OSdata store settingsThe default tablespaces DRLSMP07 and DRLSMP11 are segments. If there are some DASD and CHANNEL devices in your z/OS environment, tablespace DRLSMP07 and DRLSMP11 can be updated to partitioned tablespaces by running the Tivoli Decision Support for z/OS component installation. For more information, see Introducing Tivoli Decision Support for z/OS on IBM Knowledge Center (www.ibm.com/support/knowledgecenter/SSH53X/welcome).The Tivoli Decision Support for z/OS tables that IBM Capacity Management Analytics uses usually have hourly, daily, and monthly tables. IBM Capacity Management Analytics uses the data from the hourly table. Often the table name ends with "_H".The settings for your Tivoli Decision Support for z/OS data store are as follows:Enable DB2 New Function mode. For more information, see IBM Knowledge Center (www.ibm.com/support/knowledgecenter/SSH53X_1.8.1/com.ibm.tivoli.dszos.doc_1.8.1/drl5ba1337.htm%23a02pres).DRL and DRLSYS are the default schema names that are used by Capacity Management Analytics. If you use different schema names, you must update references to those objects in the Capacity Management Analytics Solution Kit with the appropriate schemas. If you use different schema names, you must update the references in the Framework Manager models (CMA.cpf and CMA DW\CMA DW.cpf), and then republish the packages to IBM Cognos Connection. For more information, see Changing the default schema names in Framework Manager modelsThe IBM Capacity Management Analytics Framework Manager models use default schema names for the data sources. If you use different schema names, you must update the references to the appropriate schema names in the Framework Manager models. .Configure your Tivoli Decision Support for z/OS data store with the appropriate buffer pool. Ensure that you use separate buffer pools for the Capacity Management Analytics objects and the Tivoli Decision Support for z/OS objects. For your buffer pool settings, for page sizes of 4 and 8 K, you can start with 2000 pages. For page sizes of 16 and 32 K, start with 5000 pages. Then, you can use Tivoli OMEGAMON to review the pages in use and adjust the sizes as needed.The forecasting that is done with predictive analytics is most reliable with bigger sets of data. Try to keep at least two years worth of data in the Tivoli Decision Support for z/OS data store. However, if that volume of data is not allowed in your organization, you must check the prediction results to ensure that they are within your tolerated accuracy range.Capacity Management Analytics provides flexible self-service reporting that allows for different information intervals.Collect and store System Management Facility (SMF) data at an interval that allows the highest range of use. Ideally, collect data at the smallest possible interval, which is 1 minute. However, that interval might not be realistic for certain types of data, such as storage devices. In some cases, intervals of 15 or 30 minutes might be more appropriate. You must select an interval that is most appropriate for your organization and requirements.Setting the interval must be done in SMF and in Tivoli Decision Support for z/OS.The default interval for Tivoli Decision Support for z/OS is 1 hour. You can change the interval in the MVSPM_TIME_RES lookup table. Ensure that the Tivoli Decision Support for z/OS interval is not smaller than the SMF interval.Preparation of your environmentBefore you can install IBM Capacity Management Analytics, you must ensure that your environment is set up. Preparing your environment includes applying settings for Tivoli Decision Support for z/OS, identifying the Tivoli Decision Support for z/OS schemas that Capacity Management Analytics requires, creating users and groups, and configuring IBM SPSS Modeler for Capacity Management Analytics.Update Tivoli Decision Support for z/OS control and lookup tablesIBM Tivoli Decision Support for z/OS control and lookup tables provide information about your environment.

Settings for Tivoli Decision Support for z/OS

For IBM® Capacity Management Analytics reports, you must apply certain settings for your IBM Tivoli® Decision Support for z/OS® data store.

Note: The default tablespaces DRLSMP07 and DRLSMP11 are segments. If there are some DASD and CHANNEL devices in your z/OS environment, tablespace DRLSMP07 and DRLSMP11 can be updated to partitioned tablespaces by running the Tivoli Decision Support for z/OS component installation. For more information, see Introducing Tivoli Decision Support for z/OS on IBM Knowledge Center (www.ibm.com/support/knowledgecenter/SSH53X/welcome).

The Tivoli Decision Support for z/OS tables that IBM Capacity Management Analytics uses usually have hourly, daily, and monthly tables. IBM Capacity Management Analytics uses the data from the hourly table. Often the table name ends with "_H".

The settings for your Tivoli Decision Support for z/OS data store are as follows:

Enable DB2® New Function mode.

For more information, see IBM Knowledge Center (www.ibm.com/support/knowledgecenter/SSH53X_1.8.1/com.ibm.tivoli.dszos.doc_1.8.1/drl5ba1337.htm%23a02pres).

DRL and DRLSYS are the default schema names that are used by Capacity Management Analytics. If you use different schema names, you must update references to those objects in the Capacity Management Analytics Solution Kit with the appropriate schemas.

If you use different schema names, you must update the references in the Framework Manager models (CMA.cpf and CMA DW\CMA DW.cpf), and then republish the packages to IBM Cognos® Connection.

For more information, see Changing the default schema names in Framework Manager models.

Configure your Tivoli Decision Support for z/OS data store with the appropriate buffer pool.

Ensure that you use separate buffer pools for the Capacity Management Analytics objects and the Tivoli Decision Support for z/OS objects.

For your buffer pool settings, for page sizes of 4 and 8 K, you can start with 2000 pages. For page sizes of 16 and 32 K, start with 5000 pages. Then, you can use Tivoli OMEGAMON® to review the pages in use and adjust the sizes as needed.

The forecasting that is done with predictive analytics is most reliable with bigger sets of data. Try to keep at least two years worth of data in the Tivoli Decision Support for z/OS data store. However, if that volume of data is not allowed in your organization, you must check the prediction results to ensure that they are within your tolerated accuracy range.

Capacity Management Analytics provides flexible self-service reporting that allows for different information intervals.

Important: Collect and store System Management Facility (SMF) data at an interval that allows the highest range of use. Ideally, collect data at the smallest possible interval, which is 1 minute. However, that interval might not be realistic for certain types of data, such as storage devices. In some cases, intervals of 15 or 30 minutes might be more appropriate. You must select an interval that is most appropriate for your organization and requirements.

Setting the interval must be done in SMF and in Tivoli Decision Support for z/OS.

The default interval for Tivoli Decision Support for z/OS is 1 hour. You can change the interval in the MVSPM_TIME_RES lookup table. Ensure that the Tivoli Decision Support for z/OS interval is not smaller than the SMF interval.

Update Tivoli Decision Support for z/OS control and lookup tables
IBM Tivoli Decision Support for z/OS control and lookup tables provide information about your environment.

Parent topic: Preparation of your environment

Update Tivoli Decision Support for z/OS control and lookup tablesIBM Tivoli Decision Support for z/OS control and lookup tables provide information about your environment.Tivoli Decision Support for z/OScontrol and lookup tablesThe following tables are the core control and lookup tables that need to be updated in the Tivoli Decision Support for z/OS ISPF Administration screens to reflect your specific environment. The updates must be applied before you load data into the Tivoli Decision Support for z/OS data store.For more information about these tables, see the Tivoli Decision Support for z/OS System Performance Feature Reference guide (www.ibm.com/support/knowledgecenter/SSH53X_1.8.2/com.ibm.tivoli.dszos.doc.1.8.2/SysRef1/DRL5FT16.pdf).MVS_MIPS_T tableMVS_MIPS_T tableThis lookup table must contain the MIPS ratings for the systems that you want to analyze in IBM Capacity Management Analytics, whether currently running or historical.For example, if you are collecting data into the IBM Tivoli Decision Support for z/OS database for the past two years, then you need entries in the MVS_MIPS_T table for all systems that were installed over the past two years, both active and historical data.MVSPM_TIME_RES tableMVSPM_TIME_RES tableThis lookup table defines the time resolution to use for each row of data that is stored in a set of MVSPM tables. This lets you specify that data should be recorded for a time period other than the 1-hour default for MVSPM tables. The default TIME_RESOLUTION value must be equal to your RMF recording interval to analyze and report at the most granular level.For Software Cost Analysis, SMF type 70 records are collected to the MVSPM_LPAR_MSU_T table, and SMF type 89 records are collected to the MVSPM_PROD_T and MVSPM_PROD_INT_T tables. Data is collected by SMF interval to these tables. The aggregation interval is not determined by the MVSPM_TIME_RES lookup table, as is the case with other MVSPM_* tables.The following tables tend to be high volume, so a 60-minute time resolution is appropriate in the interest of keeping table sizes manageable:MVSPM_CACHE_ESS_H MVSPM_CACHE_H MVSPM_DEVICE_H MVSPM_DEVICE_AP_H MVSPM_CHANNEL_H MVSPM_LCU_IO_HIf your RMF recording interval is 15 minutes for all systems, then the following settings store data in the MVSPM tables at the most granular level of 15 minutes. The exceptions to this are the six tables that are previously identified as requiring a 60-minute time resolution.MVSPM_TIME_RES table with 15-minute resolutionExample of the MVSPM_TIME_RES table that is configured for 15-minute resolution.If the RMF recording interval is not the same on all systems, then the following settings take into account these differences. In this example, systems MVS1 and MVS2 have a 30-minute recording interval while all other systems have a 15-minute recording interval.MVSPM_TIME_RES table with varied resolutionExample of the MVSPM_TIME_RES table that is configured for varied resolution.If you are using data from tables that are at different time intervals, you must normalize or roll up the lowest granularity to the highest before you can join the data. For example, Table 1 has an interval of 30 minutes and Table 2 has an interval of 60 minutes. Before you join the data, you must ensure that Table 1 aggregates two 30-minute intervals to equal one 60-minute interval so that it matches the rows in Table 2.DAY_OF_WEEK tableDAY_OF_WEEK tableThis control table defines the day type to be returned by the DAYTYPE function for each day of the week.The day type is used as a key in the PERIOD_PLAN and SCHEDULE control tables. The settings in this example assign Sunday to be Day 1 of the week. DAY_OF_WEEK tableThe DAY_OF_WEEK table with Sunday set as Day 1.PERIOD_PLAN tablePERIOD_PLAN tableThis control table defines the periods that are returned by the PERIOD function.A period plan defines periods (such as shifts) for each day type that is defined by the DAY_OF_WEEK and SPECIAL_DAY control tables.In this example, the PRIME period is defined as 6 a.m. to 9 p.m Monday through Friday, and the OFFPRIME period is defined as 9 p.m. to 6:00 a.m Monday through Friday. Both WEEKEND and HOLIDAY periods are also defined. The ranges should be >=START_TIME and <END_TIME. For example, in row 2, MON 06.00.00 21.00.00 PRIME means that all records on Monday from 6 am until 8:59:59 pm belong to the PRIME period.PERIOD_PLAN tablePERIOD_PLAN table that is showing multiple period definitions.SPECIAL_DAY tableSPECIAL_DAY tableThis control table defines the day type to be returned by the DAYTYPE function for special dates such as holidays.The day type is used as a key in the PERIOD_PLAN and SCHEDULE control tables.This example defines company holidays for 2012 and 2013.SPECIAL_DAY tableThe SPECIAL_DAY table defines company holidays.Settings for Tivoli Decision Support for z/OSFor IBM Capacity Management Analytics reports, you must apply certain settings for your IBM Tivoli Decision Support for z/OS data store.

Update Tivoli Decision Support for z/OS control and lookup tables

IBM® Tivoli® Decision Support for z/OS® control and lookup tables provide information about your environment.

The following tables are the core control and lookup tables that need to be updated in the Tivoli Decision Support for z/OS ISPF Administration screens to reflect your specific environment. The updates must be applied before you load data into the Tivoli Decision Support for z/OS data store.

For more information about these tables, see the Tivoli Decision Support for z/OS System Performance Feature Reference guide (www.ibm.com/support/knowledgecenter/SSH53X_1.8.2/com.ibm.tivoli.dszos.doc.1.8.2/SysRef1/DRL5FT16.pdf).

MVS_MIPS_T table

This lookup table must contain the MIPS ratings for the systems that you want to analyze in IBM Capacity Management Analytics, whether currently running or historical.

For example, if you are collecting data into the IBM Tivoli Decision Support for z/OS database for the past two years, then you need entries in the MVS_MIPS_T table for all systems that were installed over the past two years, both active and historical data.

MVSPM_TIME_RES table

This lookup table defines the time resolution to use for each row of data that is stored in a set of MVSPM tables. This lets you specify that data should be recorded for a time period other than the 1-hour default for MVSPM tables. The default TIME_RESOLUTION value must be equal to your RMF recording interval to analyze and report at the most granular level.

For Software Cost Analysis, SMF type 70 records are collected to the MVSPM_LPAR_MSU_T table, and SMF type 89 records are collected to the MVSPM_PROD_T and MVSPM_PROD_INT_T tables. Data is collected by SMF interval to these tables. The aggregation interval is not determined by the MVSPM_TIME_RES lookup table, as is the case with other MVSPM_* tables.

The following tables tend to be high volume, so a 60-minute time resolution is appropriate in the interest of keeping table sizes manageable:

MVSPM_CACHE_ESS_H

MVSPM_CACHE_H

MVSPM_DEVICE_H

MVSPM_DEVICE_AP_H

MVSPM_CHANNEL_H

MVSPM_LCU_IO_H

If your RMF recording interval is 15 minutes for all systems, then the following settings store data in the MVSPM tables at the most granular level of 15 minutes. The exceptions to this are the six tables that are previously identified as requiring a 60-minute time resolution.

Figure 3. MVSPM_TIME_RES table with 15-minute resolution

If the RMF recording interval is not the same on all systems, then the following settings take into account these differences. In this example, systems MVS1 and MVS2 have a 30-minute recording interval while all other systems have a 15-minute recording interval.

Figure 4. MVSPM_TIME_RES table with varied resolution

Important: If you are using data from tables that are at different time intervals, you must normalize or roll up the lowest granularity to the highest before you can join the data. For example, Table 1 has an interval of 30 minutes and Table 2 has an interval of 60 minutes. Before you join the data, you must ensure that Table 1 aggregates two 30-minute intervals to equal one 60-minute interval so that it matches the rows in Table 2.

DAY_OF_WEEK table

This control table defines the day type to be returned by the DAYTYPE function for each day of the week.

The day type is used as a key in the PERIOD_PLAN and SCHEDULE control tables.

The settings in this example assign Sunday to be Day 1 of the week.

Figure 5.  DAY_OF_WEEK table

PERIOD_PLAN table

This control table defines the periods that are returned by the PERIOD function.

A period plan defines periods (such as shifts) for each day type that is defined by the DAY_OF_WEEK and SPECIAL_DAY control tables.

In this example, the PRIME period is defined as 6 a.m. to 9 p.m Monday through Friday, and the OFFPRIME period is defined as 9 p.m. to 6:00 a.m Monday through Friday. Both WEEKEND and HOLIDAY periods are also defined. The ranges should be >=START_TIME and <END_TIME. For example, in row 2, MON 06.00.00 21.00.00 PRIME means that all records on Monday from 6 am until 8:59:59 pm belong to the PRIME period.

Figure 6. PERIOD_PLAN table

SPECIAL_DAY table

This control table defines the day type to be returned by the DAYTYPE function for special dates such as holidays.

The day type is used as a key in the PERIOD_PLAN and SCHEDULE control tables.

This example defines company holidays for 2012 and 2013.

Figure 7. SPECIAL_DAY table

Parent topic: Settings for Tivoli Decision Support for z/OS

Users and groups for installation and configurationThe IBM Capacity Management Analytics solution is designed to be used and shared among several users.configurationusers and groupsinstallationusers and groupsPreparation of your environmentBefore you can install IBM Capacity Management Analytics, you must ensure that your environment is set up. Preparing your environment includes applying settings for Tivoli Decision Support for z/OS, identifying the Tivoli Decision Support for z/OS schemas that Capacity Management Analytics requires, creating users and groups, and configuring IBM SPSS Modeler for Capacity Management Analytics.Groups for installation and configurationThe IBM Capacity Management Analytics solution is designed to be used and shared among several users.Users for installation and configurationThe IBM Capacity Management Analytics solution is designed to be used and shared among several users.

Users and groups for installation and configuration

The IBM® Capacity Management Analytics solution is designed to be used and shared among several users.

Groups for installation and configuration
The IBM Capacity Management Analytics solution is designed to be used and shared among several users.

Users for installation and configuration
The IBM Capacity Management Analytics solution is designed to be used and shared among several users.

Parent topic: Preparation of your environment

Groups for installation and configurationThe IBM Capacity Management Analytics solution is designed to be used and shared among several users.configurationgroupsinstallationgroupsTo configure your group for installation and configuration, a group on Linux needs to be created. All Capacity Management Analytics administrators and users will need to be part of this group.This group will have read, write, and execute access on the Solution Installer directory, CMAHOME directories, and the CMAINSTANCE directories.To create a group, run these commands: groupadd <group name> => groupadd cmagrp.Users and groups for installation and configurationThe IBM Capacity Management Analytics solution is designed to be used and shared among several users.

Groups for installation and configuration

The IBM® Capacity Management Analytics solution is designed to be used and shared among several users.

To configure your group for installation and configuration, a group on Linux needs to be created. All Capacity Management Analytics administrators and users will need to be part of this group.

This group will have read, write, and execute access on the Solution Installer directory, CMAHOME directories, and the CMAINSTANCE directories.

To create a group, run these commands: groupadd <group name> => groupadd cmagrp.

Parent topic: Users and groups for installation and configuration

Users for installation and configurationThe IBM Capacity Management Analytics solution is designed to be used and shared among several users.configurationusersinstallationusersUser IDs will need to be set up for the Capacity Management Analytics administrator and users. All Capacity Management Analytics work will be done by these user IDs on both Linux and DB2 z/OS.The Capacity Management Analytics administrator ID is used to install, uninstall, instantiate a Capacity Management Analytics instance, execute features of the Capacity Management Analytics solution, start and stop the Capacity Management Analytics component products (Cognos, SPSS, and so on). The administrator ID needs to have sudo with no password set up on Linux.To create the Capacity Management Analytics administrator ID, issue this sample command:useradd –m –g <groupname> <username> => useradd –m –g cmagrp cmaadm This command needs to be issued by sudo or root.A generic Capacity Management Analytics user ID is used to instantiate, execute features, run SPSS streams and connect to DB2 zOS. The same user ID must also be created in z/OS to be used for DB2 connections and the password must be maintained the same on both Linux and z/OS. This ID does not need sudo:useradd –m –g <groupname> <username> => useradd –m –g cmagrp cmauserSubsquent other Capacity Management Analytics user IDs can be added in the same manner. If IDs already exist, they can be added to the Capacity Management Analytics group with the usermod –g cmagrp or usermod –G cmagrp commands.Users and groups for installation and configurationThe IBM Capacity Management Analytics solution is designed to be used and shared among several users.

Users for installation and configuration

The IBM® Capacity Management Analytics solution is designed to be used and shared among several users.

User IDs will need to be set up for the Capacity Management Analytics administrator and users. All Capacity Management Analytics work will be done by these user IDs on both Linux and DB2® z/OS®.

The Capacity Management Analytics administrator ID is used to install, uninstall, instantiate a Capacity Management Analytics instance, execute features of the Capacity Management Analytics solution, start and stop the Capacity Management Analytics component products (Cognos®, SPSS®, and so on). The administrator ID needs to have sudo with no password set up on Linux.

To create the Capacity Management Analytics administrator ID, issue this sample command:

useradd –m –g <groupname> <username> => useradd –m –g cmagrp cmaadm

Note: This command needs to be issued by sudo or root.

A generic Capacity Management Analytics user ID is used to instantiate, execute features, run SPSS streams and connect to DB2 zOS. The same user ID must also be created in z/OS to be used for DB2 connections and the password must be maintained the same on both Linux and z/OS. This ID does not need sudo:

useradd –m –g <groupname> <username> => useradd –m –g cmagrp cmauser

Subsquent other Capacity Management Analytics user IDs can be added in the same manner. If IDs already exist, they can be added to the Capacity Management Analytics group with the usermod –g cmagrp or usermod –G cmagrp commands.

Parent topic: Users and groups for installation and configuration

Modifying the sudoers file for the user who runs the installationTo install IBM IBM Capacity Management Analytics by using the solution installer, you must be either the Capacity Management Analytics administrator, root user or be a user with sudo permissions. To run the installation as a user with sudo permissions, you must add that user to the sudoers file.Log in as the Capacity Management Analytics administrator or and ID with sudo or root.Open the sudoers file for editing.For example, enter visudo -f /etc/sudoers.Add the following line for your user:username ALL=(ALL) ALLComment out the TTY requirement. #Defaults requiretty.Save and close the file.Preparation of your environmentBefore you can install IBM Capacity Management Analytics, you must ensure that your environment is set up. Preparing your environment includes applying settings for Tivoli Decision Support for z/OS, identifying the Tivoli Decision Support for z/OS schemas that Capacity Management Analytics requires, creating users and groups, and configuring IBM SPSS Modeler for Capacity Management Analytics.

Modifying the sudoers file for the user who runs the installation

To install IBM IBM® Capacity Management Analytics by using the solution installer, you must be either the Capacity Management Analytics administrator, root user or be a user with sudo permissions. To run the installation as a user with sudo permissions, you must add that user to the sudoers file.

Procedure

Log in as the Capacity Management Analytics administrator or and ID with sudo or root.

Open the sudoers file for editing. For example, enter visudo -f /etc/sudoers.

Add the following line for your user:

username ALL=(ALL) ALL

Comment out the TTY requirement.

#Defaults requiretty.

Save and close the file.

Parent topic: Preparation of your environment

Configuring the administrator and user profilesBefore you can use the IBM Capacity Management Analytics IDs, you must set them up correctly. The CMAHOME and CMAINSTANCE directory values will be used in the Solution Installer and various other Capacity Management Analytics programs. It must be consistent everywhere.Preparation of your environmentBefore you can install IBM Capacity Management Analytics, you must ensure that your environment is set up. Preparing your environment includes applying settings for Tivoli Decision Support for z/OS, identifying the Tivoli Decision Support for z/OS schemas that Capacity Management Analytics requires, creating users and groups, and configuring IBM SPSS Modeler for Capacity Management Analytics.Configuring the administrator and user profilesFollow this procedure to configure your IBM Capacity Management Analytics administrator and user profiles.

Configuring the administrator and user profiles

Before you can use the IBM® Capacity Management Analytics IDs, you must set them up correctly.

Note: The CMAHOME and CMAINSTANCE directory values will be used in the Solution Installer and various other Capacity Management Analytics programs. It must be consistent everywhere.

Configuring the administrator and user profiles
Follow this procedure to configure your IBM Capacity Management Analytics administrator and user profiles.

Parent topic: Preparation of your environment

Configuring the administrator and user profilesFollow this procedure to configure your IBM Capacity Management Analytics administrator and user profiles.What follows is a sample configuration.Copy and paste the contents of the following example into a file in your home directory called: hcm.profile using your preferred method.Open your profile file.For RHEL, this is usually .bash_profileFor SLES, this is usually .profileAppend this line at the very bottom of the profile: “. hcm.profile”#!/bin/sh ############################################################################# # Licensed Materials - Property of IBM # IBM Products: IBM Capacity Management Analytics # (C) Copyright IBM Corp. 2013, 2015 # US Government Users Restricted Rights # Use, duplication or disclosure restricted by # GSA ADP Schedule Contract with IBM Corp. ############################################################################## # NAME: hcm.profile # # DESCRIPTION: IBM Capacity Management Analytics # Sample Unix/Linux profile. This profile should be # used by both the CMA_ADM and HCM_USER defined user IDs. # # Notes: # - Either rename this file to .profile or source it from your existing .profile # - Replace the values for the environment variables as needed # - ulimits: verify with your admin what settings are appropriate for your system # #unlimited may not always be available # # Set: # - ulimit settings # - DISPLAY to the IP of your desktop # - JAVAHOME to the JAVA home directory # - CMAHOME to the CMA home directory # - CMAINSTANCE to the CMA instance directory # ############################################################################# TRUE=1 FALSE=0IS_LINUX=${FALSE} OS=`echo \`uname\` | tr '[A-Z]' '[a-z]'` #Operating System in lowercase echo "OS=${OS}" case "$OS" in "linux") IS_LINUX=${TRUE} ;; *) "ERROR: Cannot determine this Operating System" exit 1 ;; esac #Execute DB2 profile if [ -f /home/db2inst1/sqllib/db2profile ] ; then . /home/db2inst1/sqllib/db2profile export IBM_DB_HOME=$DB2_HOME fi #vars common to all platforms #The CMA_ADM and HCM_USER users must be in the same group #The instance directories created by CMA_ADM must have rwx for group umask 002 #Give owner rwx, group rwx and give other rx export EDITOR=vi export TMPDIR=${HOME}/temp export TERM=vt100 export LANG=En_US set -o vi #export PATH="${PATH}:/usr/sbin:/usr/local/bin:/usr/bin:/bin:" export CMAHOME=/opt/ibm/Capacity_Management_Analytics/v2.1.1 export CMAINSTANCE=/opt/ibm/Capacity_Management_Analytics/v2.1.1_instance export PYTHONPATH=$CMAHOME/bin:$CMAHOME/lib:$PYTHONPATH #export JAVA_HOME=/opt/IBM/SPSS/ModelerServer/17.1/jre #export PATH=$JAVA_HOME/bin:$PYTHONPATH:$PATH export PATH=$PYTHONPATH:$PATH #set common ulimits ulimit -t unlimited ulimit -d unlimited ulimit -f unlimited if [ -f .envfile ] ; then . .envfile fiConfiguring the administrator and user profilesBefore you can use the IBM Capacity Management Analytics IDs, you must set them up correctly.

Configuring the administrator and user profiles

Follow this procedure to configure your IBM® Capacity Management Analytics administrator and user profiles.

About this task

What follows is a sample configuration.

Procedure

Copy and paste the contents of the following example into a file in your home directory called: hcm.profile using your preferred method.

Open your profile file.

For RHEL, this is usually .bash_profile

For SLES, this is usually .profile

Append this line at the very bottom of the profile: “. hcm.profile”

Example

#!/bin/sh
#############################################################################
#  Licensed Materials - Property of IBM
#  IBM Products: IBM Capacity Management Analytics
#  (C) Copyright IBM Corp. 2013, 2015
#  US Government Users Restricted Rights
#  Use, duplication or disclosure restricted by
#  GSA ADP Schedule Contract with IBM Corp.
##############################################################################
# NAME: hcm.profile
#
# DESCRIPTION: IBM Capacity Management Analytics
#              Sample Unix/Linux profile. This profile should be
#              used by both the CMA_ADM and HCM_USER defined user IDs.
#
# Notes:
#    - Either rename this file to .profile or source it from your existing .profile
#    - Replace the values for the environment variables as needed
#    - ulimits: verify with your admin what settings are appropriate for your system
#      #unlimited may not always be available
#
# Set:
#    - ulimit settings
#    - DISPLAY to the IP of your desktop
#    - JAVAHOME to the JAVA home directory
#    - CMAHOME to the CMA home directory
#    - CMAINSTANCE to the CMA instance directory
#
#############################################################################

TRUE=1
FALSE=0IS_LINUX=${FALSE}

OS=`echo \`uname\` | tr '[A-Z]' '[a-z]'`     #Operating System in lowercase
echo "OS=${OS}"

case "$OS" in
    "linux")
        IS_LINUX=${TRUE}
    ;;
    *)
        "ERROR: Cannot determine this Operating System"
        exit 1
    ;;
esac

#Execute DB2 profile
if [ -f /home/db2inst1/sqllib/db2profile ] ; then
  . /home/db2inst1/sqllib/db2profile
  export IBM_DB_HOME=$DB2_HOME
fi

#vars common to all platforms
#The CMA_ADM and HCM_USER users must be in the same group
#The instance directories created by CMA_ADM must have rwx for group
umask 002      #Give owner rwx, group rwx and give other rx

export EDITOR=vi
export TMPDIR=${HOME}/temp
export TERM=vt100
export LANG=En_US
set -o vi

#export PATH="${PATH}:/usr/sbin:/usr/local/bin:/usr/bin:/bin:"

export CMAHOME=/opt/ibm/Capacity_Management_Analytics/v2.1.1
export CMAINSTANCE=/opt/ibm/Capacity_Management_Analytics/v2.1.1_instance

export PYTHONPATH=$CMAHOME/bin:$CMAHOME/lib:$PYTHONPATH

#export JAVA_HOME=/opt/IBM/SPSS/ModelerServer/17.1/jre
#export PATH=$JAVA_HOME/bin:$PYTHONPATH:$PATH
export PATH=$PYTHONPATH:$PATH

#set common ulimits
ulimit -t unlimited
ulimit -d unlimited
ulimit -f unlimited

if [ -f .envfile ] ; then
    . .envfile
fi

Parent topic: Configuring the administrator and user profiles

Granting permissions to the data storeDB2 permissions are needed for theIBM Capacity Management Analytics users to access the IBM Tivoli Decision Support for z/OS data store for Capacity Management Analytics and to create and use the Capacity Management Analytics data store. You do not have to manually apply these grants. Capacity Management Analytics 2.1.1 separates the access to DB2 into privileged and user role based security. It requires a DB2 privileged user to create the Capacity Management Analytics user role, trusted context, databases, and to grant the requires access to resources for the Capacity Management Analytics role created. All SQL files for the Capacity Management Analytics instance are in the $CMAINSTANCE/sql directory. The SQL files beginning with admin* are the ones that are run with the DB2 privileged user. Look for the SQL files with grant in the name to see the specific grants that are done.Preparation of your environmentBefore you can install IBM Capacity Management Analytics, you must ensure that your environment is set up. Preparing your environment includes applying settings for Tivoli Decision Support for z/OS, identifying the Tivoli Decision Support for z/OS schemas that Capacity Management Analytics requires, creating users and groups, and configuring IBM SPSS Modeler for Capacity Management Analytics.

Granting permissions to the data store

DB2 permissions are needed for theIBM® Capacity Management Analytics users to access the IBM Tivoli® Decision Support for z/OS® data store for Capacity Management Analytics and to create and use the Capacity Management Analytics data store.

You do not have to manually apply these grants. Capacity Management Analytics 2.1.1 separates the access to DB2 into privileged and user role based security. It requires a DB2 privileged user to create the Capacity Management Analytics user role, trusted context, databases, and to grant the requires access to resources for the Capacity Management Analytics role created.

All SQL files for the Capacity Management Analytics instance are in the $CMAINSTANCE/sql directory. The SQL files beginning with admin* are the ones that are run with the DB2 privileged user. Look for the SQL files with grant in the name to see the specific grants that are done.

Parent topic: Preparation of your environment

Setting ulimit values on the node computersBefore you install IBM Capacity Management Analytics on Linux operating systems, you must ensure that you have appropriate ulimit values on each node.Log in to the computer as the root user or as a user with sudo permissions.Go to the /etc/security directory.Open the limits.conf file for editing.Add the following lines to the file:@root soft nofile 10240 @root hard nofile 16384 * soft nofile 10240 * hard nofile 16384Save and close the file.Restart the computer for the changes to take effect.Preparation of your environmentBefore you can install IBM Capacity Management Analytics, you must ensure that your environment is set up. Preparing your environment includes applying settings for Tivoli Decision Support for z/OS, identifying the Tivoli Decision Support for z/OS schemas that Capacity Management Analytics requires, creating users and groups, and configuring IBM SPSS Modeler for Capacity Management Analytics.

Setting ulimit values on the node computers

Before you install IBM® Capacity Management Analytics on Linux operating systems, you must ensure that you have appropriate ulimit values on each node.

Procedure

Log in to the computer as the root user or as a user with sudo permissions.

Go to the /etc/security directory.

Open the limits.conf file for editing.

Add the following lines to the file:

@root soft nofile 10240
@root hard nofile 16384
* soft nofile 10240
* hard nofile 16384

Save and close the file.

Restart the computer for the changes to take effect.

Parent topic: Preparation of your environment

Server side network configurationServer side network configuration includes host file configuration and firewall configuration.network configurationPreparation of your environmentBefore you can install IBM Capacity Management Analytics, you must ensure that your environment is set up. Preparing your environment includes applying settings for Tivoli Decision Support for z/OS, identifying the Tivoli Decision Support for z/OS schemas that Capacity Management Analytics requires, creating users and groups, and configuring IBM SPSS Modeler for Capacity Management Analytics.Configuring the host fileIf you are installing on a Linux operating system by using the solution installer, you must ensure proper communication.Configuring the firewallConfiguring your firewall is a necessary part of your network configuration.Configuring RedHat6You must configure the ports on RedHat 6 as part of your network configuration.Configuring RedHat7You must configure the ports on RedHat 7 as part of your network configuration.RedHat Linux (RHEL) utilitiesYou must install these utilities to use RedHat.

Server side network configuration

Server side network configuration includes host file configuration and firewall configuration.

Configuring the host file
If you are installing on a Linux operating system by using the solution installer, you must ensure proper communication.

Configuring the firewall
Configuring your firewall is a necessary part of your network configuration.

Configuring RedHat6
You must configure the ports on RedHat 6 as part of your network configuration.

Configuring RedHat7
You must configure the ports on RedHat 7 as part of your network configuration.

RedHat Linux (RHEL) utilities
You must install these utilities to use RedHat.

Parent topic: Preparation of your environment

Configuring the host fileIf you are installing on a Linux operating system by using the solution installer, you must ensure proper communication.On the node computer, open the /etc/hosts file.Ensure that the node computer is listed in the file. For example, ensure that your hosts files contain entries in the following pattern:127.0.0.1 localhost ##.##.##.## yourhost.domain.com yourhostSave and close the file.Server side network configurationServer side network configuration includes host file configuration and firewall configuration.

Configuring the host file

If you are installing on a Linux operating system by using the solution installer, you must ensure proper communication.

Procedure

On the node computer, open the /etc/hosts file.

Ensure that the node computer is listed in the file. For example, ensure that your hosts files contain entries in the following pattern:

127.0.0.1 localhost ##.##.##.## yourhost.domain.com yourhost

Save and close the file.

Parent topic: Server side network configuration

Configuring the firewallConfiguring your firewall is a necessary part of your network configuration.firewallconfiguringVarious ports for input and output need to be opened on the server for proper communication. The default ports to be opened are:Cognos: 9300SPSS Modeler: 28055Solution Installer UI: 8080Chef: 9683SSL: 443SSH: 22If the default ports are not available then the ports will need to be changed after installation.For information about the required ports for SPSS Modeler, see the Maximum and minimum server port information in the Performance and Optimization topic on IBM Knowledge Center (www.ibm.com/support/knowledgecenter/SS3RA7_17.0.0/ clementine/server/adminguidesource/admin_pem_performance.dita).Server side network configurationServer side network configuration includes host file configuration and firewall configuration.

Configuring the firewall

Configuring your firewall is a necessary part of your network configuration.

Various ports for input and output need to be opened on the server for proper communication. The default ports to be opened are:

Cognos®: 9300

SPSS® Modeler: 28055

Solution Installer UI: 8080

Chef: 9683

SSL: 443

SSH: 22

If the default ports are not available then the ports will need to be changed after installation.

For information about the required ports for SPSS Modeler, see the Maximum and minimum server port information in the Performance and Optimization topic on IBM® Knowledge Center (www.ibm.com/support/knowledgecenter/SS3RA7_17.0.0/ clementine/server/adminguidesource/admin_pem_performance.dita).

Parent topic: Server side network configuration

Configuring RedHat6You must configure the ports on RedHat 6 as part of your network configuration.redHat 6configuringTo open the ports on RedHat 6, utilize the iptables command for your network zone.Run iptables -I INPUT -p tcp --dport 8080 -j ACCEPT iptables -I OUTPUT -p tcp --sport 8080 -m state --state RELATED,ESTABLISHED -j ACCEPT. For further information, contact your Linux administrator.Server side network configurationServer side network configuration includes host file configuration and firewall configuration.

Configuring RedHat6

You must configure the ports on RedHat 6 as part of your network configuration.

To open the ports on RedHat 6, utilize the iptables command for your network zone.

Run iptables -I INPUT -p tcp --dport 8080 -j ACCEPT iptables -I OUTPUT -p tcp --sport 8080 -m state --state RELATED,ESTABLISHED -j ACCEPT. For further information, contact your Linux administrator.

Parent topic: Server side network configuration

Configuring RedHat7You must configure the ports on RedHat 7 as part of your network configuration.redHat 7configuringTo open the ports on RedHat 7 utilize the firewalld command for your network zone. For instance, firewall-cmd --permanent --zone=<your zone> --add-port=9683/tcp. For further information, contact your Linux administrator.Server side network configurationServer side network configuration includes host file configuration and firewall configuration.

Configuring RedHat7

You must configure the ports on RedHat 7 as part of your network configuration.

To open the ports on RedHat 7 utilize the firewalld command for your network zone.

For instance, firewall-cmd --permanent --zone=<your zone> --add-port=9683/tcp. For further information, contact your Linux administrator.

Parent topic: Server side network configuration

RedHat Linux (RHEL) utilitiesYou must install these utilities to use RedHat.redHatutilitiesInstall the following utilities by running sudo yum install wget zip unzip file. Install Wget. Wget, meaning web get, is a command-line utility that downloads files over a network.Install Zip. Zip is a compression and file packaging utility for Linux and Unix.Install UnZip. UnZip will list, test, or extract files from Zip archive files.Install File. File determines the file type.Verify these utilities were installed successfully by running sudo yum whatprovides /usr/bin/wgetServer side network configurationServer side network configuration includes host file configuration and firewall configuration.

RedHat Linux (RHEL) utilities

You must install these utilities to use RedHat.

About this task

Procedure

Install the following utilities by running sudo yum install wget zip unzip file.

Install Wget. Wget, meaning web get, is a command-line utility that downloads files over a network.

Install Zip. Zip is a compression and file packaging utility for Linux and Unix.

Install UnZip. UnZip will list, test, or extract files from Zip archive files.

Install File. File determines the file type.

Verify these utilities were installed successfully by running sudo yum whatprovides /usr/bin/wget

Parent topic: Server side network configuration

Installing IBM Capacity Management AnalyticsUse the solution installer to deploy the IBM Capacity Management Analytics component programs on computers or virtual machines that are running 64-bit Red Hat Enterprise Linux Server Edition version 7 operating systems. For other operating systems, you must install the product manually. The solution installer runs on a Red Hat Enterprise Linux version 7 operating system. It provides a web interface that you use to define, validate, and install your environment. You define your environment by identifying node computers, and dragging the component programs onto the nodes. The solution installer provides guidance for where you can add components and you can use it to validate the environment that you define. If you want to deploy the IBM Capacity Management Analytics component programs on other operating systems, you must use a manual installation. For more information, see Manually installing IBM Capacity Management Analytics 2.1.1If you want to run IBM Capacity Management Analyticson a platform other than 64-bit Red Hat Enterprise Linux Server Edition version 6 and 7, you must install the product manually.. Update manual installation links for CMA when available. If you want to customize the installation of certain components, such as distributing IBM Cognos Analytics on different computers, you can use the manual installation instructions. For more information, see the installation documentation for the component. Also, if you want to configure multiple nodes of IBM SPSS Collaboration and Deployment Services, you must use a manual installation of IBM Capacity Management Analytics. The solution installer allows only one instance of IBM SPSS Collaboration and Deployment Services in a configuration. For more information about manually installing IBM Predictive Customer Intelligence, see Manually install IBM Predictive Customer IntelligenceWhen you manually install IBM Predictive Customer Intelligence, you install each component individually by using the installation program that is provided with each component.. If you use the solution installer, the operating system for the computer where you run the solution installer must be set to use English as the language. The solution installer does not run on other languages. Also, the client computers must also be using English as the language.Migration considerationsMigrating IBM Capacity Management Analytics involves upgrading software components and deploying new Capacity Management Analytics report content.Installing and configuring the Capacity Management Analytics solutionCapacity Management Analytics 2.1.1 has a new WEB UI to manage and drive automated installation and configuration of the Capacity Management Analytics 2.1.1 operational intelligence platform components as well as the Solution Kit expert-based application modules. It provides a web interface that you use to define, validate, and install your environment.Post installationAfter a successful installation of IBM Capacity Management Analytics, restart Cognos.Using production dataWhen using IBM Capacity Management Analytics with your production data for the first time, further input is required for proper execution. Various data files for each feature that is pending installation will need to be filled out accordingly.Using the RUNSTATS utility to improve performanceThe RUNSTATS online utility of DB2 gathers summary information about the characteristics of data in tablespaces, indexes, and partitions. These JCL jobs can be executed under z/OS TSO.Maintaining Capacity Management AnalyticsAfter you install and configure the Capacity Management Analytics Solution Kit, data is loaded into the Capacity Management Analytics data store that you can run reports from. However, as your systems run, SMF and RMF data is continually being collected in the Tivoli Decision Support for z/OS database. To ensure that the Capacity Management Analytics reports continue to reflect your environment and provide useful insights, you must continue to load data into the Capacity Management Analytics datastore and run forecasts on that data.

Installing IBM Capacity Management Analytics

Use the solution installer to deploy the IBM® Capacity Management Analytics component programs on computers or virtual machines that are running 64-bit Red Hat Enterprise Linux Server Edition version 7 operating systems. For other operating systems, you must install the product manually.

The solution installer runs on a Red Hat Enterprise Linux version 7 operating system. It provides a web interface that you use to define, validate, and install your environment.

You define your environment by identifying node computers, and dragging the component programs onto the nodes. The solution installer provides guidance for where you can add components and you can use it to validate the environment that you define.

If you want to deploy the IBM Capacity Management Analytics component programs on other operating systems, you must use a manual installation. For more information, see Manually installing IBM Capacity Management Analytics 2.1.1.

Note: If you want to customize the installation of certain components, such as distributing IBM Cognos® Analytics on different computers, you can use the manual installation instructions. For more information, see the installation documentation for the component.

Also, if you want to configure multiple nodes of IBM SPSS® Collaboration and Deployment Services, you must use a manual installation of IBM Capacity Management Analytics. The solution installer allows only one instance of IBM SPSS Collaboration and Deployment Services in a configuration.

For more information about manually installing IBM Predictive Customer Intelligence, see Manually install IBM Predictive Customer Intelligence.

Important: If you use the solution installer, the operating system for the computer where you run the solution installer must be set to use English as the language. The solution installer does not run on other languages. Also, the client computers must also be using English as the language.

Migration considerations
Migrating IBM Capacity Management Analytics involves upgrading software components and deploying new Capacity Management Analytics report content.

Installing and configuring the Capacity Management Analytics solution
Capacity Management Analytics 2.1.1 has a new WEB UI to manage and drive automated installation and configuration of the Capacity Management Analytics 2.1.1 operational intelligence platform components as well as the Solution Kit expert-based application modules. It provides a web interface that you use to define, validate, and install your environment.

Post installation
After a successful installation of IBM Capacity Management Analytics, restart Cognos.

Using production data
When using IBM Capacity Management Analytics with your production data for the first time, further input is required for proper execution. Various data files for each feature that is pending installation will need to be filled out accordingly.

Using the RUNSTATS utility to improve performance
The RUNSTATS online utility of DB2® gathers summary information about the characteristics of data in tablespaces, indexes, and partitions. These JCL jobs can be executed under z/OS® TSO.

Maintaining Capacity Management Analytics
After you install and configure the Capacity Management Analytics Solution Kit, data is loaded into the Capacity Management Analytics data store that you can run reports from. However, as your systems run, SMF and RMF™ data is continually being collected in the Tivoli® Decision Support for z/OS database. To ensure that the Capacity Management Analytics reports continue to reflect your environment and provide useful insights, you must continue to load data into the Capacity Management Analytics datastore and run forecasts on that data.

Migration considerationsMigrating IBM Capacity Management Analytics involves upgrading software components and deploying new Capacity Management Analytics report content. Due to the software components changing and the changes to the underlying architecture, it is recommended that a new Capacity Management Analytics installation be made regardless of the prior version. However, if TDSz Version 1.8.2 is already in use then no migration needs to be performed for Capacity Management Analytics. If you wish to keep the same database and schema names from the prior Capacity Management Analytics datastore, then the old one will need to be purged before doing so. The prior version of Capacity Management Analytics can exist in parallel with the new installation as long as the new Capacity Management Analytics database is not sharing the prior database and schema names and TDSz is Version 1.8.2. It is recommended that you install and validate the new Capacity Management Analytics installation before removing the prior version. When you migrate, install the new software and deploy the report content to a test environment where you can verify the report outputs. Then, after you are satisfied that the new product is working as expected, you can migrate your production environment. When you migrate from an existing version, all of the report content that is deployed to IBM Cognos Analytics is overwritten. If you created new reports or modified any reports in the Capacity Management Analytics Solution Kit, you must take extra steps to preserve your new or modified reports.IBM Cognos Analytics reports In IBM Cognos Business Intelligence, you should back up your new and modified reports before you deploy Capacity Management Analytics 2.1.1. You can redeploy the reports, but you should do so to a new location in IBM Cognos Business Intelligence. For more information about upgrading or migrating IBM Cognos BI, see the upgrading documentation on IBM Knowledge Center (www.ibm.com/support/knowledgecenter/SSEP7J_10.2.2/com.ibm.swg.ba.cognos.inst_cr_winux.10.2.2.doc/c_upgradingtocognos8.html%23UpgradingtoCognos8)Framework Manager models Also, if you modified the IBM Cognos Framework Manager models, you must apply your modifications to the Capacity Management Analytics 2.1.1 version of the models. If you want to continue to use the previous version of the model, you should rename the models and store them in a new location. You must also remap the reports in IBM Cognos Business Intelligence to use the previous version package as the path will have changed.Report templates If you want to keep the previous version report templates, you can copy the entire template folder into Capacity Management Analytics 2.1.1, but ensure that you use a new name for the folder in IBM Cognos Business Intelligence. Additionally, you must remap objects as the path will have changed due to the new name. To migrate any report objects that you created, you should rename the model, report, template, or workspace folders in the previous version before you copy them to the new version.SPSS Modeler streams If you have created any IBM SPSS Modeler streams in your previous version, you will have to modify the streams to use in the new version as the database tables and column names have changed. Also, if you have modified any streams, the table and column names have changed in the new version, so you must manually update any streams that you have modified.Capacity Management Analytics database Before you migrate your Capacity Management Analytics ensure that you back up your Capacity Management Analytics database. Specifically, ensure that you back up all tables that have HCM as the prefix and all of the tablespaces that have IDHTS as the prefix. If you use any Capacity Management Analytics database tables as source data for any applications, you must change the names in the new version as the table names will have changed.Tivoli Decision Support for z/OS database It is also recommended that you back up the Tivoli Decision Support for z/OS database. For more information, see IBM Knowledge Center (www.ibm.com/support/knowledgecenter/SSH53X_1.8.2/com.ibm.tivoli.dszos.doc.1.8.2/Admin/abackup.dita).Installing IBM Capacity Management AnalyticsUse the solution installer to deploy the IBM Capacity Management Analytics component programs on computers or virtual machines that are running 64-bit Red Hat Enterprise Linux Server Edition version 7 operating systems. For other operating systems, you must install the product manually.

Migration considerations

Migrating IBM® Capacity Management Analytics involves upgrading software components and deploying new Capacity Management Analytics report content.

Due to the software components changing and the changes to the underlying architecture, it is recommended that a new Capacity Management Analytics installation be made regardless of the prior version. However, if TDSz Version 1.8.2 is already in use then no migration needs to be performed for Capacity Management Analytics. If you wish to keep the same database and schema names from the prior Capacity Management Analytics datastore, then the old one will need to be purged before doing so.

The prior version of Capacity Management Analytics can exist in parallel with the new installation as long as the new Capacity Management Analytics database is not sharing the prior database and schema names and TDSz is Version 1.8.2. It is recommended that you install and validate the new Capacity Management Analytics installation before removing the prior version.

When you migrate, install the new software and deploy the report content to a test environment where you can verify the report outputs. Then, after you are satisfied that the new product is working as expected, you can migrate your production environment.

When you migrate from an existing version, all of the report content that is deployed to IBM Cognos® Analytics is overwritten. If you created new reports or modified any reports in the Capacity Management Analytics Solution Kit, you must take extra steps to preserve your new or modified reports.

IBM Cognos Analytics reports

In IBM Cognos Business Intelligence, you should back up your new and modified reports before you deploy Capacity Management Analytics 2.1.1. You can redeploy the reports, but you should do so to a new location in IBM Cognos Business Intelligence.

For more information about upgrading or migrating IBM Cognos BI, see the upgrading documentation on IBM Knowledge Center (www.ibm.com/support/knowledgecenter/SSEP7J_10.2.2/com.ibm.swg.ba.cognos.inst_cr_winux.10.2.2.doc/c_upgradingtocognos8.html%23UpgradingtoCognos8)

Framework Manager models

Also, if you modified the IBM Cognos Framework Manager models, you must apply your modifications to the Capacity Management Analytics 2.1.1 version of the models.

If you want to continue to use the previous version of the model, you should rename the models and store them in a new location. You must also remap the reports in IBM Cognos Business Intelligence to use the previous version package as the path will have changed.

Report templates

If you want to keep the previous version report templates, you can copy the entire template folder into Capacity Management Analytics 2.1.1, but ensure that you use a new name for the folder in IBM Cognos Business Intelligence. Additionally, you must remap objects as the path will have changed due to the new name.

To migrate any report objects that you created, you should rename the model, report, template, or workspace folders in the previous version before you copy them to the new version.

SPSS Modeler streams

If you have created any IBM SPSS® Modeler streams in your previous version, you will have to modify the streams to use in the new version as the database tables and column names have changed. Also, if you have modified any streams, the table and column names have changed in the new version, so you must manually update any streams that you have modified.

Capacity Management Analytics database

Before you migrate your Capacity Management Analytics ensure that you back up your Capacity Management Analytics database. Specifically, ensure that you back up all tables that have HCM as the prefix and all of the tablespaces that have IDHTS as the prefix.

If you use any Capacity Management Analytics database tables as source data for any applications, you must change the names in the new version as the table names will have changed.

Tivoli Decision Support for z/OS database

It is also recommended that you back up the Tivoli® Decision Support for z/OS® database. For more information, see IBM Knowledge Center (www.ibm.com/support/knowledgecenter/SSH53X_1.8.2/com.ibm.tivoli.dszos.doc.1.8.2/Admin/abackup.dita).

Parent topic: Installing IBM Capacity Management Analytics

Installing and configuring the Capacity Management Analytics solutionCapacity Management Analytics 2.1.1 has a new WEB UI to manage and drive automated installation and configuration of the Capacity Management Analytics 2.1.1 operational intelligence platform components as well as the Solution Kit expert-based application modules. It provides a web interface that you use to define, validate, and install your environment.installationconfigurationIf you want to deploy the Capacity Management Analytics component programs on a platform other than 64-bit Red Hat Enterprise Linux Server Edition version 6 and 7, you must install the product manually. For more information, see Manually installing IBM Capacity Management Analytics 2.1.1If you want to run IBM Capacity Management Analyticson a platform other than 64-bit Red Hat Enterprise Linux Server Edition version 6 and 7, you must install the product manually..Manual installation also allows you to customize the installation of certain components, such as distributing IBM Cognos Business Intelligence on different computers.If you use the Solution Installer, the operating system where you run the installer must be set to use English as the language. The Solution Installer does not run on other languages. Client computers must also be set to English.Installing IBM Capacity Management AnalyticsUse the solution installer to deploy the IBM Capacity Management Analytics component programs on computers or virtual machines that are running 64-bit Red Hat Enterprise Linux Server Edition version 7 operating systems. For other operating systems, you must install the product manually.Uploading Capacity Management AnalyticsAfter you have installed all of your prerequisite software and downloaded IBM Capacity Management Analytics, you are ready to upload the solution.Starting the solution installerAfter the solution installer is running, you can access the installer interface from another computer by using a web browser and start defining the environment.Using the solution installerYou use the solution installer to define your IBM Capacity Management Analytics environment you want to install.Starting the installationThe IBM Capacity Management Analytics solution installer installs the component products. Before you start the installation, you can validate the configuration.RedHat 6 systemsFor RedHat 6 systems, you must manually catalog your database connection to DB2 z/OS once you have installed DB2.Testing and validating the installationAfter the installation of the IBM Capacity Management Analytics is complete, you should test the installation.

Installing and configuring the Capacity Management Analytics solution

Capacity Management Analytics 2.1.1 has a new WEB UI to manage and drive automated installation and configuration of the Capacity Management Analytics 2.1.1 operational intelligence platform components as well as the Solution Kit expert-based application modules. It provides a web interface that you use to define, validate, and install your environment.

If you want to deploy the Capacity Management Analytics component programs on a platform other than 64-bit Red Hat Enterprise Linux Server Edition version 6 and 7, you must install the product manually. For more information, see Manually installing IBM Capacity Management Analytics 2.1.1.

Note: Manual installation also allows you to customize the installation of certain components, such as distributing IBM Cognos Business Intelligence on different computers.

Note: If you use the Solution Installer, the operating system where you run the installer must be set to use English as the language. The Solution Installer does not run on other languages. Client computers must also be set to English.

Uploading Capacity Management Analytics
After you have installed all of your prerequisite software and downloaded IBM Capacity Management Analytics, you are ready to upload the solution.

Starting the solution installer
After the solution installer is running, you can access the installer interface from another computer by using a web browser and start defining the environment.

Using the solution installer
You use the solution installer to define your IBM Capacity Management Analytics environment you want to install.

Starting the installation
The IBM Capacity Management Analytics solution installer installs the component products. Before you start the installation, you can validate the configuration.

RedHat 6 systems
For RedHat 6 systems, you must manually catalog your database connection to DB2® z/OS® once you have installed DB2.

Testing and validating the installation
After the installation of the IBM Capacity Management Analytics is complete, you should test the installation.

Parent topic: Installing IBM Capacity Management Analytics

Uploading Capacity Management AnalyticsAfter you have installed all of your prerequisite software and downloaded IBM Capacity Management Analytics, you are ready to upload the solution.uploadingNavigate to the location where you downloaded the eImages for Capacity Management Analytics.Upload the eImages to your designated Capacity Management Analytics server in the Capacity Management Analytics administrator ID home directory.Extract the cma_sol_ins_2.1.1.0_lxz_en.tar.gz for Linux on System z or cma_sol_ins_2.1.1.0_lx86_en.tar.gz, Linux x86, file depending on your server with command tar xvf filename.tar.gz If you are using RedHat 6, use the command tar --no-same-owner --no-same-permissions -xvf filesname.tar.gz with command tar xvf filename.tar.gz. If you are using RedHat6, use the command tar --no-same-owner --no-same-permissions -xvf filesname.tar.gz. The file will be extracted into a directory named SolutionInstaller.Copy the cma_srv_bndl_2.1.1.0_lxz_en.tar.gz or cma_srv_bndl_2.1.1.0_lx86_en.tar.gz file to the SolutionInstaller/ NodeRoot/Downloads/Software directory. Go back to your SolutionInstaller folder, run command ./cmaupdate.py. You can also run the command ./cmaupdate.py -d SolutionInstallerDirectory. You can find the log for cmaupdate.py in the SolutionInstaller/log folder.Copy the cma_sol_kit_2.1.1.0_mp_en.tar.gz to theSolutionInstaller/NodeRoot/Downloads/Software/CMA folder. Installing and configuring the Capacity Management Analytics solutionCapacity Management Analytics 2.1.1 has a new WEB UI to manage and drive automated installation and configuration of the Capacity Management Analytics 2.1.1 operational intelligence platform components as well as the Solution Kit expert-based application modules. It provides a web interface that you use to define, validate, and install your environment.

Uploading Capacity Management Analytics

After you have installed all of your prerequisite software and downloaded IBM® Capacity Management Analytics, you are ready to upload the solution.

Procedure

Navigate to the location where you downloaded the eImages for Capacity Management Analytics.

Upload the eImages to your designated Capacity Management Analytics server in the Capacity Management Analytics administrator ID home directory.

Extract the cma_sol_ins_2.1.1.0_lxz_en.tar.gz for Linux on System z or cma_sol_ins_2.1.1.0_lx86_en.tar.gz, Linux x86, file depending on your server with command tar xvf filename.tar.gz If you are using RedHat 6, use the command tar --no-same-owner --no-same-permissions -xvf filesname.tar.gz with command tar xvf filename.tar.gz. If you are using RedHat6, use the command tar --no-same-owner --no-same-permissions -xvf filesname.tar.gz. The file will be extracted into a directory named SolutionInstaller.

Copy the cma_srv_bndl_2.1.1.0_lxz_en.tar.gz or cma_srv_bndl_2.1.1.0_lx86_en.tar.gz file to the SolutionInstaller/ NodeRoot/Downloads/Software directory.

Go back to your SolutionInstaller folder, run command ./cmaupdate.py. You can also run the command ./cmaupdate.py -d SolutionInstallerDirectory. You can find the log for cmaupdate.py in the SolutionInstaller/log folder.

Copy the cma_sol_kit_2.1.1.0_mp_en.tar.gz to theSolutionInstaller/NodeRoot/Downloads/Software/CMA folder.

Parent topic: Installing and configuring the Capacity Management Analytics solution

Starting the solution installerAfter the solution installer is running, you can access the installer interface from another computer by using a web browser and start defining the environment. Ensure that you copy the solution installer files to a directory in which you have permissions to execute files. Ensure that the operating system on which you run the solution installer is set to use English as the language. The solution installer does not run on other languages. Also, the client computers must also be using English as the language.Log on to the computer where you decompressed the installation files as the administrator or as a user with sudo or root.Go to the SolutionInstaller directory where you decompressed the solution installer files.Enter the following command: setup.sh.After the solution installer starts, open a web browser on another computer, and go to the solution installer URL: https://servername:8080/UI/index.html. Do not use a web browser on the computer on which you are running the solution installer or any computer on which a IBM Capacity Management Analytics component is being installed. You must use a web browser on another computer. The computer on which you are using the browser to access the solution installer must have a screen resolution that is greater than 1024 by 760. The solution installer runs on Google Chrome 44, or later, or Mozilla Firefox 38 or later, web browsers. It does not run on Internet Explorer. Ensure that you are using an English language web browser to access the solution installer URL.Validation:Node:Run command ps –ef | grep nodeYou should see the node is running as follows: root 5811 1 0 13:35 ? 00:00:00 /opt/ibm/nodejs1.2.0.1/bin/node /opt/ibm/nodejs1.2.0.1/lib/node_modules/forever/bin/monitor NodeJS/server.jsroot 5816 5811 0 13:35 ? 00:00:00 /opt/ibm/nodejs1.2.0.1/bin/node /home/nthuy/sandbox-local/si0003/Capacity_Management_Analytics/v2.1.1/SolutionInstaller/ NodeJS/server.jsChef client:run command rpm –qa | grep chef. You should see the chef client rpm chef-12.1.2-1.el6.s390x.Cookbook. Open cookbook in cd <your_home_directory>/sandbox-local/si0003/Capacity_Management_Analytics/v2.1.1/SolutionInstaller/chef-repo/cookbooks to ensure all cookbooks have been copied.Installing and configuring the Capacity Management Analytics solutionCapacity Management Analytics 2.1.1 has a new WEB UI to manage and drive automated installation and configuration of the Capacity Management Analytics 2.1.1 operational intelligence platform components as well as the Solution Kit expert-based application modules. It provides a web interface that you use to define, validate, and install your environment.

Starting the solution installer

After the solution installer is running, you can access the installer interface from another computer by using a web browser and start defining the environment.

Note: Ensure that you copy the solution installer files to a directory in which you have permissions to execute files.

Important: Ensure that the operating system on which you run the solution installer is set to use English as the language. The solution installer does not run on other languages. Also, the client computers must also be using English as the language.

Procedure

Log on to the computer where you decompressed the installation files as the administrator or as a user with sudo or root.

Go to the SolutionInstaller directory where you decompressed the solution installer files.

Enter the following command: setup.sh.

After the solution installer starts, open a web browser on another computer, and go to the solution installer URL: https://servername:8080/UI/index.html.

Important: Do not use a web browser on the computer on which you are running the solution installer or any computer on which a IBM® Capacity Management Analytics component is being installed. You must use a web browser on another computer.

The computer on which you are using the browser to access the solution installer must have a screen resolution that is greater than 1024 by 760.

The solution installer runs on Google Chrome 44, or later, or Mozilla Firefox 38 or later, web browsers. It does not run on Internet Explorer.

Ensure that you are using an English language web browser to access the solution installer URL.

Validation:

Node:

Run command ps –ef | grep node

You should see the node is running as follows:

root 5811 1 0 13:35 ? 00:00:00 /opt/ibm/nodejs1.2.0.1/bin/node
 /opt/ibm/nodejs1.2.0.1/lib/node_modules/forever/bin/monitor NodeJS/server.js

root      5816  5811  0 13:35 ?        00:00:00 /opt/ibm/nodejs1.2.0.1/bin/node
/home/nthuy/sandbox-local/si0003/Capacity_Management_Analytics/v2.1.1/SolutionInstaller/
NodeJS/server.js

Chef client:

run command rpm –qa | grep chef. You should see the chef client rpm chef-12.1.2-1.el6.s390x.

Cookbook. Open cookbook in cd <your_home_directory>/sandbox-local/si0003/Capacity_Management_Analytics/v2.1.1/SolutionInstaller/chef-repo/cookbooks to ensure all cookbooks have been copied.

Parent topic: Installing and configuring the Capacity Management Analytics solution

Using the solution installerYou use the solution installer to define your IBM Capacity Management Analytics environment you want to install. You can save your configurations. For more information, see Saving and opening configurationsYou can save IBM Capacity Management Analytics configurations and open existing configurations in the solution installer.. The solution installer runs on Google Chrome 44 or later or Mozilla Firefox 38 or later. It does not run on Internet Explorer. For more information about supported software, see the IBM Software Product Compatibility Reports (http://www-01.ibm.com/support/docview.wss?uid=swg24042285).Open the solution installer in a web browser.After the solution installer is running, you can access the URL from any computer from a Firefox or Chrome web browser. The URL is https://servername:8080/UI/index.html, where servername is the name of the computer where you ran the solution installer. Do not use a web browser on the computer on which you are running the solution installer or any computer on which a IBM Capacity Management Analytics component is being installed. You must use a web browser on another computer. Ensure that you are using an English language web browser to access the solution installer URL.On the license information page, click Accept.Declining the license allows you to uninstall the solution installer and remove all of the files from the solution installer directory.Select Predefined Configuration, select the CMA Single Node Configuration and click OK.RedHat 6 users must delete the CMA Node and install the solution using a two step process. This process entails the following: The configuration is displayed in the Configuration Editor pane. Icons are displayed on each component where configuration information is required. For instance, where you must provide user names or passwords. Image displaying the Configuration Editor pane and the Property Editor pane Ensure that you change the server names to an actual server in your environment. Default values are provided. Ensure all of the properties are valid for your environment for each of the components and the nodes in the configuration.In the Property Editor pane, select the node object, and enter the following information. For instance, in the OS Node: Enter a name for the node in Name, and select Enter.For example, enter BI node.Enter the server name in Host Name, and select Enter.Unless you are using a single node configuration, ensure that the host names or IP addresses are for different computers. Do not use the same computer for more than one Node object.Enter the user who has access to install the components in User Name, and press the Enter key.For example, enter root or a user with sudo permissions.Enter the user's password in User Password, and select Enter.Repeat these steps for each node object that you add.For example, if you are installing IBM Capacity Management Analytics on four nodes, you must repeat these steps for each of the four node objects.You must enter any mandatory information for nodes and other objects as indicated. Ensure that you review all of the properties to ensure that the values, both mandatory and optional, are valid. Ensure that you select Enter after you type a value for a property.From the Optional Software List, drag the objects to the node on which you want the installation files copied.As you build your environment, click Validate to identify errors or incomplete information.After all nodes are defined and each node is validated, click Run.After you click the Run button, do not close the browser window until the installation completes. If you close the browser, you cannot re-initiate the session.However, the installation does proceed even with the browser closed. But you cannot monitor the progress.If the browser window closes, do the following steps:Do nothing until enough time elapses to allow the installation to complete. Allow the installation at least 2 hours to complete.Open a new browser window, and open the solution installer again.Re-create the configuration that you used or open the saved session that you used. You must ensure that all of the values that you entered in the fields are the same as the configuration that you used when the browser window closed.Rerun the installation. The solution installer verifies that all of the components are installed.After the solution installer completes, verify that the components are working. For more information, see Testing and validating the installationAfter the installation of the IBM Capacity Management Analytics is complete, you should test the installation.. If you click Validate before all the mandatory content is added, a message indicates that the node is not valid. For example, if you validate the BI Node before you add the IBM DB2 component, then a message indicates that the node is not yet valid.Installing and configuring the Capacity Management Analytics solutionCapacity Management Analytics 2.1.1 has a new WEB UI to manage and drive automated installation and configuration of the Capacity Management Analytics 2.1.1 operational intelligence platform components as well as the Solution Kit expert-based application modules. It provides a web interface that you use to define, validate, and install your environment.Configuration properties for the solution installerYou must provide some configuration properties for each of the mandatory software components.Saving and opening configurationsYou can save IBM Capacity Management Analytics configurations and open existing configurations in the solution installer.

Using the solution installer

You use the solution installer to define your IBM® Capacity Management Analytics environment you want to install.

You can save your configurations. For more information, see Saving and opening configurations.

Important: The solution installer runs on Google Chrome 44 or later or Mozilla Firefox 38 or later. It does not run on Internet Explorer.

For more information about supported software, see the IBM Software Product Compatibility Reports (http://www-01.ibm.com/support/docview.wss?uid=swg24042285).

Procedure

Open the solution installer in a web browser.

After the solution installer is running, you can access the URL from any computer from a Firefox or Chrome web browser.

The URL is https://servername:8080/UI/index.html, where servername is the name of the computer where you ran the solution installer.

Important: Do not use a web browser on the computer on which you are running the solution installer or any computer on which a IBM Capacity Management Analytics component is being installed. You must use a web browser on another computer.

Ensure that you are using an English language web browser to access the solution installer URL.

On the license information page, click Accept.

CAUTION:

Declining the license allows you to uninstall the solution installer and remove all of the files from the solution installer directory.

Select Predefined Configuration, select the CMA Single Node Configuration and click OK.

Note: RedHat 6 users must delete the CMA Node and install the solution using a two step process. This process entails the following:

The configuration is displayed in the Configuration Editor pane. Icons are displayed on each component where configuration information is required. For instance, where you must provide user names or passwords.

Note: Ensure that you change the server names to an actual server in your environment. Default values are provided.

Ensure all of the properties are valid for your environment for each of the components and the nodes in the configuration.

In the Property Editor pane, select the node object, and enter the following information. For instance, in the OS Node:

Enter a name for the node in Name, and select Enter. For example, enter BI node.

Enter the server name in Host Name, and select Enter.

Important: Unless you are using a single node configuration, ensure that the host names or IP addresses are for different computers. Do not use the same computer for more than one Node object.

Enter the user who has access to install the components in User Name, and press the Enter key. For example, enter root or a user with sudo permissions.

Enter the user's password in User Password, and select Enter.

Repeat these steps for each node object that you add. For example, if you are installing IBM Capacity Management Analytics on four nodes, you must repeat these steps for each of the four node objects.

Important: You must enter any mandatory information for nodes and other objects as indicated. Ensure that you review all of the properties to ensure that the values, both mandatory and optional, are valid.

Ensure that you select Enter after you type a value for a property.

From the Optional Software List, drag the objects to the node on which you want the installation files copied.

As you build your environment, click Validate to identify errors or incomplete information.

After all nodes are defined and each node is validated, click Run.

Important: After you click the Run button, do not close the browser window until the installation completes. If you close the browser, you cannot re-initiate the session.

However, the installation does proceed even with the browser closed. But you cannot monitor the progress.

If the browser window closes, do the following steps:

Do nothing until enough time elapses to allow the installation to complete. Allow the installation at least 2 hours to complete.

Open a new browser window, and open the solution installer again.

Re-create the configuration that you used or open the saved session that you used. You must ensure that all of the values that you entered in the fields are the same as the configuration that you used when the browser window closed.

Rerun the installation. The solution installer verifies that all of the components are installed.

After the solution installer completes, verify that the components are working. For more information, see Testing and validating the installation.

Tip: If you click Validate before all the mandatory content is added, a message indicates that the node is not valid. For example, if you validate the BI Node before you add the IBM DB2 component, then a message indicates that the node is not yet valid.

Configuration properties for the solution installer
You must provide some configuration properties for each of the mandatory software components.

Saving and opening configurations
You can save IBM Capacity Management Analytics configurations and open existing configurations in the solution installer.

Parent topic: Installing and configuring the Capacity Management Analytics solution

Configuration properties for the solution installerYou must provide some configuration properties for each of the mandatory software components. If you select any components from the Optional Software List, you must provide a deployment location for the installation files. You must manually install any item that you select from the Optional Software List. For RedHat 6 systems, delete the CMA node.IBM Capacity Management Analytics Solution Kit properties Sample DatabaseA sample TDSz and CMA database is installed that can be used to verify all components of the CMA solution are working properly. Note: Once you decide to use your production data then the sample database must be dropped. Default: Yes.CMA Feature – BaseInstalls and configures the base of CMA providing the database setup and reports. For first time installations it must be installed. Default: Yes. CMA Feature – Application AnalyticsInstalls and configures this feature to provide insights to application usage. Default: Yes. CMA Feature – Capacity Planning & ForecastingInstalls and configures this feature to provide reports and models to plan and forecast for servers and systems usage. Default: Yes. CMA Feature – Problem Identification Installs and configures this feature to provide a top down view into systems to identify and resolve problems. Default: Yes. CMA Feature – Software Cost AnalysisInstalls and configures this feature to manage z/OS software costs. Default: Yes. CMA Feature – Systems Management & OptimizationInstalls and configures this feature to provide insights and optimization recommendations to servers and systems. Default: Yes. Run CMA InstanceA CMA Instance will be created and configured for all the previously listed features. If set to No, an instance will then need to be created manually. However, the instance folder and data files will still be copied over but no stream execution or database work will be done. Refer to cmainstance.py for details. If you want to use your production data first, instead of samples, then set to No. Default: YesYour DB2 administrator may want to review the SQL that is executed before installation occurs. Set this value to No to generate a preview of the SQL files. These files can be reviewed in the CMAINSTANCE/sql folder. VerboseProvides more information onto the console and log files. It can also be used to provide trace information. To run a trace, use numeric values 1 to 4. All verbose levels are inclusive.1 = Verbose mode is on2 = Provides informational messages about the function call stack3 = Provides some variable and informational messages4 = Deep tracing, more variables, and informational messagesDefault: Yes CMA Solution Kit Install DirectoryThis is the CMAHOME directory where all executable and configuration files reside. If you change this default you must update your CMA administrator and user Linux profiles to reflect this. Default: /opt/ibm/Capacity_Management_Analytics_/v2.1.1. CMA Instance DirectoryThis is the CMAINSTANCE directory where all data & configuration files for a specific instance resides. If you change this default you must update your CMA administrator and user Linux profiles to reflect this. Default: /opt/ibm/Capacity_Management_Analytics_/v2.1.1_instance. Cognos NamespaceOptional Cognos Namespace. Value unused. DB2 Privileged User NameCredentials for the DB2 z/OS user account with privilege to create databases and tables. This ID is not created for you and must exist before going further in the installation process. See Preparation of your environmentBefore you can install IBM Capacity Management Analytics, you must ensure that your environment is set up. Preparing your environment includes applying settings for Tivoli Decision Support for z/OS, identifying the Tivoli Decision Support for z/OS schemas that Capacity Management Analytics requires, creating users and groups, and configuring IBM SPSS Modeler for Capacity Management Analytics.. DB2 Privileged User PasswordPassword for the DB2 privileged user. DB2 Host NameDB2 zOS Server hostname. DB2 Database PortDB2 zOS Port number. CMA Database Sub-System IDThe database sub-system ID. DB2 Database Location NameThe DB2 z/OS Location Name CMA Database NameCMA Database Name. Default: HCMDB. CMA Database User NameCMA database user. This ID is not created for you and must exist before going further in the installation process. See Preparation of your environmentBefore you can install IBM Capacity Management Analytics, you must ensure that your environment is set up. Preparing your environment includes applying settings for Tivoli Decision Support for z/OS, identifying the Tivoli Decision Support for z/OS schemas that Capacity Management Analytics requires, creating users and groups, and configuring IBM SPSS Modeler for Capacity Management Analytics.. CMA Database User PasswordCMA database user password CMA Schema NameCMA schema name. Default: HCM. CMA Database Storage Group NameDefault: SYSDEFLT CMA Database CCSIDCMA Database encoding. Default: EBCDIC.Important: if this database is to be loaded into IDAA then the value must be EBCIDC. CMA Database BufferpoolDatabase bufferpool. It is recommended you use a 8K size bufferpool. Default: BP0. CMA Database Index BufferpoolIndex bufferpool. It is recommended you use a 8K size bufferpool. Default: BP1. DB2 for z/OS General WLM Environment NameWLM environment of DB2 for stored procedures. You must assign the Capacity Management Analytics stored procedure to one predefined WLM application environment to route the stored procedure work to the appropriate WLM-managed address space. Default: CMAWLM_GENERAL. TDSz Database NameThe Tivoli Decision Support for z/OS database name. TDSz must be installed before going any further in the installation process. Default: DRLDB. TDSz Schema NameTDSz schema name. Default: DRL. TDSz Database Storage Group NameTDSz database storage group name. Default: SYSDEFLT.IBM Cognos Analytics Server properties IBM Cognos BI Analytics Install DirectoryThe location where IBM Cognos BI Analytics Server is to be installed.Default: /opt/ibm/cognos/analyticsIBM DB2 Enterprise Server properties DB2 Server Install DirectoryThe location where IBM DB2 is to be installed on the data node computer.Default: /opt/ibm/db2/V10.5DB2 Database Port NumberThe port number that is used by IBM DB2.Default: 50000DB2 FCM Port NumberThe port number that is used by IBM DB2.Default: 60000DB2 Server Admin User NameThe user ID that runs the administration server.Default: dasusr1This user must not exist on the computer.DB2 Server Admin PasswordThe password for the user ID that runs the administration server.The password cannot contain more than 8 characters and it must be lowercase.DB2 Server Admin Home DirectoryThe home directory for the user ID that runs the administration server. This directory is on the data node computer's file system.Default: /home/dasusr1DB2 Instance Owner User NameThe user ID that controls the DB2 processes and owns the directories that are used by the database instance.Default: db2inst1This user must not exist on the computer.DB2 Instance Owner PasswordThe password for the DB2 instance owner user.The password cannot contain more than 8 characters and it must be lowercase.DB2 Instance Owner Home DirectoryThe home directory for the DB2 instance owner user. This directory is on the data node computer's file system.Default: /home/db2inst1DB2 Fenced User NameThe user ID that can run user-defined functions and store procedures.Default: db2fenc1This user must not exist on the computer.DB2 Fenced User PasswordThe password for the DB2 fenced user.The password cannot contain more than 8 characters and it must be lowercase.DB2 Fenced User Home DirectoryThe home directory for the DB2 fenced user.Default: /home/db2fenc1Database: Cognos Content Store Content Manager Database NameThe IBM Cognos content store database.This component must be contained within IBM DB2 Enterprise Server.Default: COG11CSIBM SPSS Modeler Server properties SPSS Modeler Server Install DirectoryThe location where IBM SPSS Modeler Server is to be installed.Default: /opt/IBM/SPSS/ModelerServer/17.1IBM SPSS Modeler Batch properties SPSS Modeler Batch Install DirectoryThe location where IBM SPSS Modeler Batch is to be installed.Default: /opt/ibm/SPSS/ModelerBatch/17.1IBM SPSS Data Access Pack properties SPSS Data Access Pack Install DirectoryThe location where IBM SPSS Data Access Pack is to be installed.Default: /opt/ibm/SPSS/SDAP711IBM ILOG CPLEX Optimization Studio properties ILOG CPLEX Optimization Studio Install DirectoryThe location where IBM ILOG CPLEX Optimization Studio is to be installed.Default: /opt/ibm/ILOG/CPLEX_Studio1263Using the solution installerYou use the solution installer to define your IBM Capacity Management Analytics environment you want to install.

Configuration properties for the solution installer

You must provide some configuration properties for each of the mandatory software components.

If you select any components from the Optional Software List, you must provide a deployment location for the installation files. You must manually install any item that you select from the Optional Software List.

Note: For RedHat 6 systems, delete the CMA node.

IBM Capacity Management Analytics Solution Kit properties

Sample Database

A sample TDSz and CMA database is installed that can be used to verify all components of the CMA solution are working properly. Note: Once you decide to use your production data then the sample database must be dropped. Default: Yes.

CMA Feature – Base

Installs and configures the base of CMA providing the database setup and reports. For first time installations it must be installed. Default: Yes.

CMA Feature – Application Analytics

Installs and configures this feature to provide insights to application usage. Default: Yes.

CMA Feature – Capacity Planning & Forecasting

Installs and configures this feature to provide reports and models to plan and forecast for servers and systems usage. Default: Yes.

CMA Feature – Problem Identification

Installs and configures this feature to provide a top down view into systems to identify and resolve problems. Default: Yes.

CMA Feature – Software Cost Analysis

Installs and configures this feature to manage z/OS software costs. Default: Yes.

CMA Feature – Systems Management & Optimization

Installs and configures this feature to provide insights and optimization recommendations to servers and systems. Default: Yes.

Run CMA Instance

A CMA Instance will be created and configured for all the previously listed features. If set to No, an instance will then need to be created manually. However, the instance folder and data files will still be copied over but no stream execution or database work will be done. Refer to cmainstance.py for details.

Note: If you want to use your production data first, instead of samples, then set to No. Default: Yes

Note: Your DB2 administrator may want to review the SQL that is executed before installation occurs. Set this value to No to generate a preview of the SQL files. These files can be reviewed in the CMAINSTANCE/sql folder.

Verbose

Provides more information onto the console and log files. It can also be used to provide trace information. To run a trace, use numeric values 1 to 4. All verbose levels are inclusive.

1 = Verbose mode is on

2 = Provides informational messages about the function call stack

3 = Provides some variable and informational messages

4 = Deep tracing, more variables, and informational messages

Default: Yes

CMA Solution Kit Install Directory

This is the CMAHOME directory where all executable and configuration files reside.

Note: If you change this default you must update your CMA administrator and user Linux profiles to reflect this. Default: /opt/ibm/Capacity_Management_Analytics_/v2.1.1.

CMA Instance Directory

This is the CMAINSTANCE directory where all data & configuration files for a specific instance resides.

Note: If you change this default you must update your CMA administrator and user Linux profiles to reflect this. Default: /opt/ibm/Capacity_Management_Analytics_/v2.1.1_instance.

Cognos Namespace

Optional Cognos Namespace. Value unused.

DB2 Privileged User Name

Credentials for the DB2 z/OS user account with privilege to create databases and tables. This ID is not created for you and must exist before going further in the installation process. See Preparation of your environment.

DB2 Privileged User Password

Password for the DB2 privileged user.

DB2 Host Name

DB2 zOS Server hostname.

DB2 Database Port

DB2 zOS Port number.

CMA Database Sub-System ID

The database sub-system ID.

DB2 Database Location Name

The DB2 z/OS Location Name

CMA Database Name

CMA Database Name. Default: HCMDB.

CMA Database User Name

CMA database user. This ID is not created for you and must exist before going further in the installation process. See Preparation of your environment.

CMA Database User Password

CMA database user password

CMA Schema Name

CMA schema name. Default: HCM.

CMA Database Storage Group Name

Default: SYSDEFLT

CMA Database CCSID

CMA Database encoding. Default: EBCDIC.

Note: Important: if this database is to be loaded into IDAA then the value must be EBCIDC.

CMA Database Bufferpool

Database bufferpool. It is recommended you use a 8K size bufferpool. Default: BP0.

CMA Database Index Bufferpool

Index bufferpool. It is recommended you use a 8K size bufferpool. Default: BP1.

DB2 for z/OS General WLM Environment Name

WLM environment of DB2 for stored procedures. You must assign the Capacity Management Analytics stored procedure to one predefined WLM application environment to route the stored procedure work to the appropriate WLM-managed address space. Default: CMAWLM_GENERAL.

TDSz Database Name

The Tivoli Decision Support for z/OS database name. TDSz must be installed before going any further in the installation process. Default: DRLDB.

TDSz Schema Name

TDSz schema name. Default: DRL.

TDSz Database Storage Group Name

TDSz database storage group name. Default: SYSDEFLT.

IBM Cognos Analytics Server properties

IBM Cognos® BI Analytics Install Directory

The location where IBM Cognos BI Analytics Server is to be installed.

Default: /opt/ibm/cognos/analytics

IBM DB2 Enterprise Server properties

DB2® Server Install Directory

The location where IBM DB2 is to be installed on the data node computer.

Default: /opt/ibm/db2/V10.5

DB2 Database Port Number

The port number that is used by IBM DB2.

Default: 50000

DB2 FCM Port Number

The port number that is used by IBM DB2.

Default: 60000

DB2 Server Admin User Name

The user ID that runs the administration server.

Default: dasusr1

This user must not exist on the computer.

DB2 Server Admin Password

The password for the user ID that runs the administration server.

The password cannot contain more than 8 characters and it must be lowercase.

DB2 Server Admin Home Directory

The home directory for the user ID that runs the administration server. This directory is on the data node computer's file system.

Default: /home/dasusr1

DB2 Instance Owner User Name

The user ID that controls the DB2 processes and owns the directories that are used by the database instance.

Default: db2inst1

This user must not exist on the computer.

DB2 Instance Owner Password

The password for the DB2 instance owner user.

The password cannot contain more than 8 characters and it must be lowercase.

DB2 Instance Owner Home Directory

The home directory for the DB2 instance owner user. This directory is on the data node computer's file system.

Default: /home/db2inst1

DB2 Fenced User Name

The user ID that can run user-defined functions and store procedures.

Default: db2fenc1

This user must not exist on the computer.

DB2 Fenced User Password

The password for the DB2 fenced user.

The password cannot contain more than 8 characters and it must be lowercase.

DB2 Fenced User Home Directory

The home directory for the DB2 fenced user.

Default: /home/db2fenc1

Database: Cognos Content Store

Content Manager Database Name

The IBM Cognos content store database.

This component must be contained within IBM DB2 Enterprise Server.

Default: COG11CS

IBM SPSS Modeler Server properties

SPSS Modeler Server Install Directory

The location where IBM SPSS Modeler Server is to be installed.

Default: /opt/IBM/SPSS/ModelerServer/17.1

IBM SPSS Modeler Batch properties

SPSS Modeler Batch Install Directory

The location where IBM SPSS Modeler Batch is to be installed.

Default: /opt/ibm/SPSS/ModelerBatch/17.1

IBM SPSS Data Access Pack properties

SPSS Data Access Pack Install Directory

The location where IBM SPSS Data Access Pack is to be installed.

Default: /opt/ibm/SPSS/SDAP711

IBM ILOG CPLEX Optimization Studio properties

ILOG® CPLEX® Optimization Studio Install Directory

The location where IBM ILOG CPLEX Optimization Studio is to be installed.

Default: /opt/ibm/ILOG/CPLEX_Studio1263

Parent topic: Using the solution installer

Saving and opening configurationsYou can save IBM Capacity Management Analytics configurations and open existing configurations in the solution installer.To save a configuration:In the solution installer, define your configuration.Enter a name in the Save Configuration box, and click the Save Configuration icon Save Configuration icon.To open a saved configuration:Click Open, and browse to the saved configuration file, and click Open.Using the solution installerYou use the solution installer to define your IBM Capacity Management Analytics environment you want to install.

Saving and opening configurations

You can save IBM® Capacity Management Analytics configurations and open existing configurations in the solution installer.

Procedure

To save a configuration:

In the solution installer, define your configuration.

Enter a name in the Save Configuration box, and click the Save Configuration icon Save Configuration icon.

To open a saved configuration:

Click Open, and browse to the saved configuration file, and click Open.

Parent topic: Using the solution installer

Starting the installationThe IBM Capacity Management Analytics solution installer installs the component products. Before you start the installation, you can validate the configuration.In the solution installer, open the configuration that you want to install.Click Validate.Any errors or missing information is displayed. You must correct the error or provide the information before you can run the installation.Click Run.Running times may vary depending on the amount of data loaded within your TDSz datastore and system loads. If just sample data is being created then running times should be within an hour.Installing and configuring the Capacity Management Analytics solutionCapacity Management Analytics 2.1.1 has a new WEB UI to manage and drive automated installation and configuration of the Capacity Management Analytics 2.1.1 operational intelligence platform components as well as the Solution Kit expert-based application modules. It provides a web interface that you use to define, validate, and install your environment.

Starting the installation

The IBM® Capacity Management Analytics solution installer installs the component products. Before you start the installation, you can validate the configuration.

Procedure

In the solution installer, open the configuration that you want to install.

Click Validate.

Any errors or missing information is displayed. You must correct the error or provide the information before you can run the installation.

Click Run.

Note: Running times may vary depending on the amount of data loaded within your TDSz datastore and system loads. If just sample data is being created then running times should be within an hour.

Parent topic: Installing and configuring the Capacity Management Analytics solution

RedHat 6 systemsFor RedHat 6 systems, you must manually catalog your database connection to DB2 z/OS once you have installed DB2.installationRedHat 6Run the following commands: db2 catalog tcpip node <nodename> remote <dbhostname> server <dbport>db2 catalog database <db2locname> as <dbalias> at node <nodename> authentication dcs"Proceed to Manually installing IBM Capacity Management Analytics 2.1.1If you want to run IBM Capacity Management Analyticson a platform other than 64-bit Red Hat Enterprise Linux Server Edition version 6 and 7, you must install the product manually.Installing and configuring the Capacity Management Analytics solutionCapacity Management Analytics 2.1.1 has a new WEB UI to manage and drive automated installation and configuration of the Capacity Management Analytics 2.1.1 operational intelligence platform components as well as the Solution Kit expert-based application modules. It provides a web interface that you use to define, validate, and install your environment.

RedHat 6 systems

For RedHat 6 systems, you must manually catalog your database connection to DB2® z/OS® once you have installed DB2.

Procedure

Run the following commands:

db2 catalog tcpip node <nodename> remote <dbhostname> server <dbport>

db2 catalog database <db2locname> as <dbalias> at node <nodename> authentication dcs"

What to do next

Proceed to Manually installing IBM Capacity Management Analytics 2.1.1

Parent topic: Installing and configuring the Capacity Management Analytics solution

Testing and validating the installationAfter the installation of the IBM Capacity Management Analytics is complete, you should test the installation.Verify that the products exist:/opt/ibm/Capacity_Management_Analytics/v2.1.1/opt/ibm/Capacity_Management_Analytics/v2.1.1_instance/opt/ibm/ILOG/opt/ibm/SPSS/ModelerServer/opt/ibm/SPSS/ModelerBatch/opt/ibm/SPSS/SDAP711If there are any issues, refer to the Troubleshooting section.Installing and configuring the Capacity Management Analytics solutionCapacity Management Analytics 2.1.1 has a new WEB UI to manage and drive automated installation and configuration of the Capacity Management Analytics 2.1.1 operational intelligence platform components as well as the Solution Kit expert-based application modules. It provides a web interface that you use to define, validate, and install your environment.Testing the database installation and configurationYou can test the database installation and configuration by logging in to the IBM Capacity Management Analytics data node computer and listing the databases on the server. You can also list the databases available on each node computer where the IBM DB2 client was installed.Testing the installation of the server components on a single node installationYou can test the installation of the IBM Capacity Management Analytics server components by accessing some of the component portals.

Testing and validating the installation

After the installation of the IBM® Capacity Management Analytics is complete, you should test the installation.

Verify that the products exist:

/opt/ibm/Capacity_Management_Analytics/v2.1.1

/opt/ibm/Capacity_Management_Analytics/v2.1.1_instance

/opt/ibm/ILOG

/opt/ibm/SPSS/ModelerServer

/opt/ibm/SPSS/ModelerBatch

/opt/ibm/SPSS/SDAP711

If there are any issues, refer to the Troubleshooting section.

Testing the database installation and configuration
You can test the database installation and configuration by logging in to the IBM Capacity Management Analytics data node computer and listing the databases on the server. You can also list the databases available on each node computer where the IBM DB2® client was installed.

Testing the installation of the server components on a single node installation
You can test the installation of the IBM Capacity Management Analytics server components by accessing some of the component portals.

Parent topic: Installing and configuring the Capacity Management Analytics solution

Testing the database installation and configurationYou can test the database installation and configuration by logging in to the IBM Capacity Management Analytics data node computer and listing the databases on the server. You can also list the databases available on each node computer where the IBM DB2 client was installed.Log on to the data node computer.Open a terminal window, and change to the database instance owner user.For example, su - db2inst1.Enter the following command to list the databases on the computer:db2 list database directory For an installation on a Red Hat Linux operating systems that used the solution installer, the following databases are listed: COG112CSCMA_<DBSID> or the DB2 alias you set using the command line interface.Testing and validating the installationAfter the installation of the IBM Capacity Management Analytics is complete, you should test the installation.

Testing the database installation and configuration

You can test the database installation and configuration by logging in to the IBM® Capacity Management Analytics data node computer and listing the databases on the server. You can also list the databases available on each node computer where the IBM DB2® client was installed.

Procedure

Log on to the data node computer.

Open a terminal window, and change to the database instance owner user. For example, su - db2inst1.

Enter the following command to list the databases on the computer:

db2 list database directory

For an installation on a Red Hat Linux operating systems that used the solution installer, the following databases are listed:

COG112CS

CMA_<DBSID> or the DB2 alias you set using the command line interface.

Parent topic: Testing and validating the installation

Testing the installation of the server components on a single node installationYou can test the installation of the IBM Capacity Management Analytics server components by accessing some of the component portals.For example, you can open the IBM Cognos BI portal. If the portal opens, IBM Cognos BI is installed and running. You can also access the SPSS configuration tool on the Analytics node computer.Open a web browser.In the address bar, type the address for the IBM Cognos Content Manager status page.The address is http://servername:9300/p2pd/servlet. The Content Manager status says Running.In the address bar, type the address for the IBM Cognos Analytics portal. You can access the IBM Cognos BI portal from the dispatcher URL: http://servername:9300/bi Navigate to Team Content > IBM Capacity Management Analytics > Models and verify that the date is your install date.To verify that SPSS Modeler Server is running: /opt/ibm/opt/ibm/SPSS/ModelerServer/17.1/modelersrv.sh list.Testing and validating the installationAfter the installation of the IBM Capacity Management Analytics is complete, you should test the installation.

Testing the installation of the server components on a single node installation

You can test the installation of the IBM® Capacity Management Analytics server components by accessing some of the component portals.

For example, you can open the IBM Cognos® BI portal. If the portal opens, IBM Cognos BI is installed and running. You can also access the SPSS® configuration tool on the Analytics node computer.

Procedure

Open a web browser.

In the address bar, type the address for the IBM Cognos Content Manager status page.

The address is http://servername:9300/p2pd/servlet.

The Content Manager status says Running.

In the address bar, type the address for the IBM Cognos Analytics portal. You can access the IBM Cognos BI portal from the dispatcher URL: http://servername:9300/bi

Navigate to Team Content > IBM Capacity Management Analytics > Models and verify that the date is your install date.

To verify that SPSS Modeler Server is running: /opt/ibm/opt/ibm/SPSS/ModelerServer/17.1/modelersrv.sh list.

Parent topic: Testing and validating the installation

Post installationAfter a successful installation of IBM Capacity Management Analytics, restart Cognos.See Stop and start solution software servicesIBM Capacity Management Analytics is an integrated solution that includes many products. If you must stop the services, you must do so in the correct order. The product services must also be started in the correct order.Installing IBM Capacity Management AnalyticsUse the solution installer to deploy the IBM Capacity Management Analytics component programs on computers or virtual machines that are running 64-bit Red Hat Enterprise Linux Server Edition version 7 operating systems. For other operating systems, you must install the product manually.

Post installation

After a successful installation of IBM® Capacity Management Analytics, restart Cognos.

See Stop and start solution software services

Parent topic: Installing IBM Capacity Management Analytics

Using production dataWhen using IBM Capacity Management Analytics with your production data for the first time, further input is required for proper execution. Various data files for each feature that is pending installation will need to be filled out accordingly.Refer to the corresponding feature chapter for details about filling out the data files correctly. To ensure and protect the data files from being overridden, take write capabilities off all the data files in the CMAINSTANCE/data folder. Run “chmod w-ugo *”.If you need to make changes to the file then write access will need to be granted again.Run the cmainstance.py program manually. See Manually installing IBM Capacity Management Analytics 2.1.1If you want to run IBM Capacity Management Analyticson a platform other than 64-bit Red Hat Enterprise Linux Server Edition version 6 and 7, you must install the product manually.. Installing IBM Capacity Management AnalyticsUse the solution installer to deploy the IBM Capacity Management Analytics component programs on computers or virtual machines that are running 64-bit Red Hat Enterprise Linux Server Edition version 7 operating systems. For other operating systems, you must install the product manually.

Using production data

When using IBM® Capacity Management Analytics with your production data for the first time, further input is required for proper execution. Various data files for each feature that is pending installation will need to be filled out accordingly.

Refer to the corresponding feature chapter for details about filling out the data files correctly.

To ensure and protect the data files from being overridden, take write capabilities off all the data files in the CMAINSTANCE/data folder. Run “chmod w-ugo *”.

Note: If you need to make changes to the file then write access will need to be granted again.

Run the cmainstance.py program manually. See Manually installing IBM Capacity Management Analytics 2.1.1.

Parent topic: Installing IBM Capacity Management Analytics

Using the RUNSTATS utility to improve performanceThe RUNSTATS online utility of DB2 gathers summary information about the characteristics of data in tablespaces, indexes, and partitions. These JCL jobs can be executed under z/OS TSO. The samples JCL jobs are in the CMAHOME/scripts directory. We recommend that you schedule this utility to run periodically.RUNSTATSHCMSTAT1HCMSTAT2HCMSTAT3sample filesHCMSTAT1sample filesHCMSTAT2sample filesHCMSTAT3On a Linux on System z operating system, upload HCMSTAT1, HCMSTAT2, and HCMSTAT3 JCL jobs to the z/OS machine and copy into one PDS dataset with RECFM=FB and LRECL=80.Within the JCL jobs, change parameters as described in the following table.JCL jobs and descriptionsJCL job nameDescriptionParameters to changeHCMSTAT1Executes RUNSTATS utility for all tables and indexes in Tivoli Decision Support for z/OS DRL and Capacity Management Analytics HCM databases.Change #DB2SID# to appropriate DB2 SYSTEM ID.Change #DB2LIB# to appropriate DB2 SDSNLOAD LIBRARY.Change #DRLDB# to appropriate Tivoli Decision Support for z/OS database name.Change #HCMDB# to appropriate Capacity Management Analytics HCM database name. HCMSTAT2Executes RUNSTATS utility for special tables and indexes in Tivoli Decision Support for z/OS DRL and Capacity Management Analytics HCM databases.Change #DB2SID# to appropriate DB2 SYSTEM ID.Change #DB2LIB# to appropriate DB2 SDSNLOAD LIBRARY.Change #DRLDB# to appropriate Tivoli Decision Support for z/OS database name.Change #HCMDB# to appropriate Capacity Management Analytics HCM database name. Change #DRL# to appropriate Tivoli Decision Support for z/OS schema name. Change #HCM# to appropriate Capacity Management Analytics HCM schema name.Change #HCM_ID# to the unique ID you used when creating the HCM tablespace.HCMSTAT3IF CMASTAT3 and CMASTAT2 are identical, why are both necessary? Does anything differentiate one from the other?Executes RUNSTATS utility for special tables and indexes in Tivoli Decision Support for z/OS DRL and Capacity Management Analytics HCM databases.Change #DB2SID# to appropriate DB2 SYSTEM ID.Change #DB2LIB# to appropriate DB2 SDSNLOAD LIBRARY.Change #DRLDB# to appropriate Tivoli Decision Support for z/OS database name.Change #HCMDB# to appropriate Capacity Management Analytics HCM database name. Change #DRL# to appropriate Tivoli Decision Support for z/OS schema name. Change #HCM# to appropriate Capacity Management Analytics HCM schema name.Change #HCM_ID# to the unique ID you used when creating the HCM tablespace.Submit these JCL jobs using HCMADM or other database user with STATS privilege for Tivoli Decision Support for z/OS and Capacity Management Analytics databases. Alternatively, you can submit the jobs as a user with DBADM, DBCTRL, or DBMAINT authority for these databases.Installing IBM Capacity Management AnalyticsUse the solution installer to deploy the IBM Capacity Management Analytics component programs on computers or virtual machines that are running 64-bit Red Hat Enterprise Linux Server Edition version 7 operating systems. For other operating systems, you must install the product manually.

Using the RUNSTATS utility to improve performance

The RUNSTATS online utility of DB2® gathers summary information about the characteristics of data in tablespaces, indexes, and partitions. These JCL jobs can be executed under z/OS® TSO.

The samples JCL jobs are in the CMAHOME/scripts directory.

We recommend that you schedule this utility to run periodically.

Procedure

On a Linux on System z® operating system, upload HCMSTAT1, HCMSTAT2, and HCMSTAT3 JCL jobs to the z/OS machine and copy into one PDS dataset with RECFM=FB and LRECL=80.

Within the JCL jobs, change parameters as described in the following table.

Table 12. . JCL jobs and descriptions

JCL job name
Description
Parameters to change

HCMSTAT1
Executes RUNSTATS utility for all tables and indexes in Tivoli® Decision Support for z/OS DRL and Capacity Management Analytics HCM databases.

Change #DB2SID# to appropriate DB2 SYSTEM ID.

Change #DB2LIB# to appropriate DB2 SDSNLOAD LIBRARY.

Change #DRLDB# to appropriate Tivoli Decision Support for z/OS database name.

Change #HCMDB# to appropriate Capacity Management Analytics HCM database name.

HCMSTAT2
Executes RUNSTATS utility for special tables and indexes in Tivoli Decision Support for z/OS DRL and Capacity Management Analytics HCM databases.

Change #DB2SID# to appropriate DB2 SYSTEM ID.

Change #DB2LIB# to appropriate DB2 SDSNLOAD LIBRARY.

Change #DRLDB# to appropriate Tivoli Decision Support for z/OS database name.

Change #HCMDB# to appropriate Capacity Management Analytics HCM database name.

Change #DRL# to appropriate Tivoli Decision Support for z/OS schema name.

Change #HCM# to appropriate Capacity Management Analytics HCM schema name.

Change #HCM_ID# to the unique ID you used when creating the HCM tablespace.

HCMSTAT3
Executes RUNSTATS utility for special tables and indexes in Tivoli Decision Support for z/OS DRL and Capacity Management Analytics HCM databases.

Change #DB2SID# to appropriate DB2 SYSTEM ID.

Change #DB2LIB# to appropriate DB2 SDSNLOAD LIBRARY.

Change #DRLDB# to appropriate Tivoli Decision Support for z/OS database name.

Change #HCMDB# to appropriate Capacity Management Analytics HCM database name.

Change #DRL# to appropriate Tivoli Decision Support for z/OS schema name.

Change #HCM# to appropriate Capacity Management Analytics HCM schema name.

Change #HCM_ID# to the unique ID you used when creating the HCM tablespace.

Submit these JCL jobs using HCMADM or other database user with STATS privilege for Tivoli Decision Support for z/OS and Capacity Management Analytics databases. Alternatively, you can submit the jobs as a user with DBADM, DBCTRL, or DBMAINT authority for these databases.

Parent topic: Installing IBM Capacity Management Analytics

Maintaining Capacity Management AnalyticsAfter you install and configure the Capacity Management Analytics Solution Kit, data is loaded into the Capacity Management Analytics data store that you can run reports from. However, as your systems run, SMF and RMF data is continually being collected in the Tivoli Decision Support for z/OS database. To ensure that the Capacity Management Analytics reports continue to reflect your environment and provide useful insights, you must continue to load data into the Capacity Management Analytics datastore and run forecasts on that data. If you want to extend or customize Capacity Management Analytics, you can use the Solution Kit reports, models, or streams as examples. However, you should not change any reports, models, or streams. You can make a copy of the report, model, or stream, and modify the copy. Do not modify any of the reports, models, or streams that are provided with the Capacity Management Analytics Solution Kit.The following are some general guidelines for TDSz loads:Data can be loaded into DB2 from TDSz daily, as frequently as your systems allow, or for your business needs. The RUNSTATS online utility of DB2 gathers summary information about the characteristics of data in tablespaces, indexes, and partitions of Capacity Management Analytics tables and Tivoli Decision Support for z/OS tables. For more information, see Using the RUNSTATS utility to improve performanceThe RUNSTATS online utility of DB2 gathers summary information about the characteristics of data in tablespaces, indexes, and partitions. These JCL jobs can be executed under z/OS TSO..Sample files are provided to help you do RUNSTATS. The sample files are named HCMSTAT1, HCMSTAT2, and HCMSTATS, and they are in the CMAHOME/samples directory where you install the Capacity Management Analytics Solution Kit. On z/OS systems, the sample files are found in the samples directory in the SMP/E installation folder.The following are some general guidelines for running DB2 RUNSTATS:RUNSTATS can be done weekly or if there is a noticeable performance issue or large amount of data loaded.Running the streams to load data and run forecasts is done by manually running the Capacity Management Analytics feature program. For more information, see Manually running features for IBM Capacity Management Analytics 2.1.1.The following are some general guidelines for loading data and running the Capacity Management Analytics feature program:The Capacity Management Analytics streams can modify the Capacity Management Analytics tables in the database. Always use the Capacity Management Analytics feature program to modify the tables unless it is necessary to use the DB2 SQL statement directly.The following are some general guidelines for loading data and running the Capacity Management Analytics feature program:Loading data can be done on a daily, weekly, or monthly basis, depending on your business needs and system toleration.Running the forecast streams can be done every 4 - 6 weeks or whenever there is enough volatility or fluctuation within the historical data or deviations between the forecasts and the historical data.All of the preceding steps can be automated through your own tooling. You can also use standard UNIX cronjobs to schedule the work.Cleaning up log and temporary files should be done every month. Log and temporary files are in the CMAHOME/log and CMAINSTANCE/log and CMAINSTANCE/temp directories.If you want to use any new features after installation, load new feature data with using cmahome.py to install new features, cmainstance.py to create an instance and the Capacity Management Analytics feature programs for further granular control.The following is a general guideline for Lpar Weight Optimization:Run the Lpar Weight Optimizations program for busy Lpars to determine optimized weight value. LPAR weight optimizationIBM Capacity Management Analytics uses IBM ILOG CPLEX Optimization Studio to provide optimal LPAR weight values for your environment.The following is a general guideline for Cognos reports:Batch repeat scheduling after loads & forecasts schedule in Cognos.For more Cognos scheduling information, see IBM Cognos Business Intelligence documentation.Installing IBM Capacity Management AnalyticsUse the solution installer to deploy the IBM Capacity Management Analytics component programs on computers or virtual machines that are running 64-bit Red Hat Enterprise Linux Server Edition version 7 operating systems. For other operating systems, you must install the product manually.

Maintaining Capacity Management Analytics

After you install and configure the Capacity Management Analytics Solution Kit, data is loaded into the Capacity Management Analytics data store that you can run reports from. However, as your systems run, SMF and RMF™ data is continually being collected in the Tivoli® Decision Support for z/OS® database. To ensure that the Capacity Management Analytics reports continue to reflect your environment and provide useful insights, you must continue to load data into the Capacity Management Analytics datastore and run forecasts on that data.

Tip: If you want to extend or customize Capacity Management Analytics, you can use the Solution Kit reports, models, or streams as examples. However, you should not change any reports, models, or streams. You can make a copy of the report, model, or stream, and modify the copy. Do not modify any of the reports, models, or streams that are provided with the Capacity Management Analytics Solution Kit.

The following are some general guidelines for TDSz loads:

Data can be loaded into DB2 from TDSz daily, as frequently as your systems allow, or for your business needs.

The RUNSTATS online utility of DB2 gathers summary information about the characteristics of data in tablespaces, indexes, and partitions of Capacity Management Analytics tables and Tivoli Decision Support for z/OS tables. For more information, see Using the RUNSTATS utility to improve performance.

Tip: Sample files are provided to help you do RUNSTATS. The sample files are named HCMSTAT1, HCMSTAT2, and HCMSTATS, and they are in the CMAHOME/samples directory where you install the Capacity Management Analytics Solution Kit. On z/OS systems, the sample files are found in the samples directory in the SMP/E installation folder.

The following are some general guidelines for running DB2 RUNSTATS:

RUNSTATS can be done weekly or if there is a noticeable performance issue or large amount of data loaded.

Running the streams to load data and run forecasts is done by manually running the Capacity Management Analytics feature program. For more information, see Manually running features for IBM Capacity Management Analytics 2.1.1.

The following are some general guidelines for loading data and running the Capacity Management Analytics feature program:

Important: The Capacity Management Analytics streams can modify the Capacity Management Analytics tables in the database. Always use the Capacity Management Analytics feature program to modify the tables unless it is necessary to use the DB2 SQL statement directly.

The following are some general guidelines for loading data and running the Capacity Management Analytics feature program:

Loading data can be done on a daily, weekly, or monthly basis, depending on your business needs and system toleration.

Running the forecast streams can be done every 4 - 6 weeks or whenever there is enough volatility or fluctuation within the historical data or deviations between the forecasts and the historical data.

Note: All of the preceding steps can be automated through your own tooling. You can also use standard UNIX cronjobs to schedule the work.

Cleaning up log and temporary files should be done every month. Log and temporary files are in the CMAHOME/log and CMAINSTANCE/log and CMAINSTANCE/temp directories.

If you want to use any new features after installation, load new feature data with using cmahome.py to install new features, cmainstance.py to create an instance and the Capacity Management Analytics feature programs for further granular control.

The following is a general guideline for Lpar Weight Optimization:

Run the Lpar Weight Optimizations program for busy Lpars to determine optimized weight value. LPAR weight optimization

The following is a general guideline for Cognos reports:

Batch repeat scheduling after loads & forecasts schedule in Cognos.

For more Cognos scheduling information, see IBM Cognos Business Intelligence documentation.

Parent topic: Installing IBM Capacity Management Analytics

Creating a sample databaseYou can install a sample TDSz and IBM Capacity Management Analytics database that can be used to verify that Capacity Management Analytics has been properly installed and configured.sample databasecreatingThe sample database is an optional selection for installation. When selected, it will create the sample TDSz database and load the data. Subsequent Solution Kit modules will use this data to allow for the verification of the streams and reports for those modules.Obtaining a sample database The sample database can be created automatically from the Property Editor pane in the Solution Installer. The default value is Yes. A sample database can also be created manually by running cmainstance.py or cmasampledb.py. See Manually installing IBM Capacity Management Analytics 2.1.1If you want to run IBM Capacity Management Analyticson a platform other than 64-bit Red Hat Enterprise Linux Server Edition version 6 and 7, you must install the product manually..Setting up the sample database After you have selected the sample database to be installed, the following procedure is performed automatically:TDSz database is created with a chosen schema name. The default is DRL.All TDSz tables are created. The sample DDLs can be found in <cma_instance_home>/sql.Sample data is inserted into these TDSz tables.The Capacity Management Analytics database is created with the chosen schema name. The default is HCM.All Capacity Management Analytics tables are created. The sample DDLs can be found in <cma_instance_home>/sql.SPSS streams are run based on the selected features to insert sample data into the Capacity Management Analytics tables. To verify the results, run the sql hcm_dates_rows.sql and drl_dates_rows.sql in <cma_instance_home>/sql. The command to run is: db2 –tf hcm_dates_rows.sql > hcm_dates_rows.log. This log will show the number of rows that were inserted into each table and the date ranges, minimum and maximum dates, that are used to run the sample reports.Best practices are separating schema, storegroup, bufferpool and database from production.Some features are not available for sample data or date ranges. Refer to the table in this section.Running reports with the sample databaseYou can run reports with the sample database in IBM Capacity Management Analytics.Dropping the sample databaseOnce you have used your production data, then the sample database must be dropped.

Creating a sample database

You can install a sample TDSz and IBM® Capacity Management Analytics database that can be used to verify that Capacity Management Analytics has been properly installed and configured.

The sample database is an optional selection for installation. When selected, it will create the sample TDSz database and load the data. Subsequent Solution Kit modules will use this data to allow for the verification of the streams and reports for those modules.

Obtaining a sample database

The sample database can be created automatically from the Property Editor pane in the Solution Installer. The default value is Yes.

A sample database can also be created manually by running cmainstance.py or cmasampledb.py. See Manually installing IBM Capacity Management Analytics 2.1.1.

Setting up the sample database

After you have selected the sample database to be installed, the following procedure is performed automatically:

TDSz database is created with a chosen schema name. The default is DRL.

All TDSz tables are created. The sample DDLs can be found in <cma_instance_home>/sql.

Sample data is inserted into these TDSz tables.

The Capacity Management Analytics database is created with the chosen schema name. The default is HCM.

All Capacity Management Analytics tables are created. The sample DDLs can be found in <cma_instance_home>/sql.

SPSS streams are run based on the selected features to insert sample data into the Capacity Management Analytics tables. To verify the results, run the sql hcm_dates_rows.sql and drl_dates_rows.sql in <cma_instance_home>/sql. The command to run is: db2 –tf hcm_dates_rows.sql > hcm_dates_rows.log. This log will show the number of rows that were inserted into each table and the date ranges, minimum and maximum dates, that are used to run the sample reports.

Best practices are separating schema, storegroup, bufferpool and database from production.

Note: Some features are not available for sample data or date ranges. Refer to the table in this section.

Running reports with the sample database
You can run reports with the sample database in IBM Capacity Management Analytics.

Dropping the sample database
Once you have used your production data, then the sample database must be dropped.

Running reports with the sample databaseYou can run reports with the sample database in IBM Capacity Management Analytics.sample databaserunning reportsThe table in this section contain the Capacity Management Analytics tables, the number of rows within each table, the features and the reports that are mapped with each table. Date ranges are also provided for the sample reports.Before running the reports, ensure all the packages of Framework Manager models are re-published with the correct schema. See Changing the default schema names in Framework Manager modelsThe IBM Capacity Management Analytics Framework Manager models use default schema names for the data sources. If you use different schema names, you must update the references to the appropriate schema names in the Framework Manager models. IBM Capacity Management Analytics Tables (HCM schema).CMA TABLES (HCM schema)Number of rowsFeatureReportDate Ranges (MIN/MAX dates) YYYY-MM-DDANOMALIES_CICS0PI CICS Anomaly DetailsN/A APPL_DIST_MAPPING0AAApplication Analytics: Distributed CPU usage - applicationsN/AAPPL_MAPPING92AAApplication Analytics: MIPS Used - Application2016-09-30 creation date of mapAPPL_ZOS_FORECAST54032SMOCPU: MIPS Used - LPAR Level by WLM Importance CPU: MIPS Used - zServer/LPAR Level2015-05-21 2015-05-23CUSTOMER_PRICE159SCASCA: NO89 Product MSU and Price1900-01-01 1900-01-01DIM_DATE13878SCACPU: AIX - 10 Highest/Lowest CPU Usage Peaks2015-01-01 2015-01-01DIM_MINUTE1440SMOAnomaly Detection Analytics: CICS Anomaly DetailsN/ADIM_TIME24PIApplication Analytics: MIPS Used - ApplicationN/A Distributed Server data from SMF 104AACPU:AIX - CPU Usage CPU:Linux for System X - CPU Usage CPU:Linux for System Z - CPU Usage2015-06-02 -2015-07-04 2015-07-31 – 2015-08-03 FORECAST_BILLABLE247350SCASCA: LPAR MSU UtilizationN/A FORECAST_METADATA4SMOCPU: MIPS Used - LPAR Level by WLM Importance CPU: MIPS Used - zServer/LPAR LevelNo forecast dataLPAR_MSU_FORECAST9373SCASCA: LPAR MSU Utilization SCA: NO89 Product MSU and Price SCA: Product MSU and Price SCA: Software Cost Analysis Summary2015-04-01 - 2015-05-03 Observed data 2015-04-01 – 2015-09-10 Forecast dataLPAR_MSU_ OPTIMIZATION1488SCASCA: LPAR MSU Utilization SCA: NO89 Product MSU and Price SCA: Product MSU and Price SCA: Software Cost Analysis Summary2015-05-02 - 2015-07-10LPAR_WEIGHT_ OPTIMIZATION2SMOCPU: LPAR Weight Optimization Run Result CPU: MIPS Used - LPAR Level by WLM Importance CPU: Over/Under Shared Weight - CPC by LPAR2015-05-21 – 2015-05-23LPAR_WEIGHT_ OPTIMIZATION_DETAIL144SMOCPU: LPAR Weight Optimization Run Result2015-05-21 - 2015-05-23LPAR_WEIGHT_ OPTIMIZATION_ PARAMETERS4SMOCPU: LPAR Weight Optimization Run ResultN/AMIPS_CAPACITY46AAApplication Analytics ReportsN/AMVSPM_CHANNEL240SMOzServer Monitoring Dashboards2015-05-21 - 2015-05-21MVSPM_DEVICE264SMOzServer Monitoring Dashboards2015-05-21 - 2015-05-21MVSPM_LPAR12432AA, CPF, SCA, SMOCPU: MIPS Used - LPAR Level by WLM Importance CPU: MIPS Used -Service Class Period Level CPU: MIPS Used -System Level (Captured vs Uncaptured) CPU: MIPS Used - zServer/LPAR Level2015-04-01 -2015-05-28MVSPM_LPAR continued12432AA, CPF, SCA, SMOCPU: Over/Under Shared Weight - CPC by LPAR Application Analytics: MIPS Used – Application Application Analytics: MIPS Used - Application by FunctionMVSPM_LPAR continued12432AA, CPF, SCA, SMOSCA: LPAR MSU Utilization SCA: NO89 Product MSU and Price SCA: Product MSU and PriceMVSPM_LPAR continued12432AA, CPF, SCA, SMOSCA: Software Cost Analysis Summary zIIP/zAAP What ifs - LPAR Level zServer Monitoring Dashboards2015-04-01 -2015-05-28KPMZ_JOB_INT_H - SMF 30 dataN/AApplication Analytics: MIPS Used - Report Class by Job NamesNo SMF 30 data available, only list of Report Classes 2015-05-21 – 2015-05-23MVSPM_WORKLOAD2_H from TDS/z, type 72 dataCPU: MIPS Used - LPAR Level by WLM Importance CPU: MIPS Used -Service Class Period Level Application Analytics: MIPS Used – Application Application Analytics: MIPS Used - Application by Function Application Analytics: MIPS Used - Application by LPAR2015-05-21 – 2015-05-23 for LPAR JB0MVSPM_LPAR_CPU_BUSY_ MIPS_PRED (hourly)28320SMOCPU: MIPS Used - LPAR Level by WLM Importance CPU: MIPS Used - zServer/LPAR LevelNo forecast data for WLM reporting 2015-04-01 – 2015-07-27 is available for zServer/LPAR level reporting of forecastMVSPM_LPAR_CPU_BUSY_ MIPS_PRED (daily)0SMOCPU: MIPS Used - LPAR Level by WLM Importance CPU: MIPS Used - zServer/LPAR LevelN/AMVSPM_LPAR_CPU_BUSY _MIPS_PRED (monthly)0SMOCPU: MIPS Used - LPAR Level by WLM Importance CPU: MIPS Used - zServer/LPAR LevelN/ANO89_PRODUCTS23SCASCA: NO89 Product MSU and Price SCA: NO89 Products Matrix1900-01-01 1900-01-01OPTIMIZATION_BILLABLE39312SCASCA: NO89 Product MSU and Price SCA: Product MSU and Price SCA: Registered Products Matrix SCA: Software Cost Analysis Summary2015-05-02 - 2015-07-02OPTIMIZATION_METADATA1SMOCPU: LPAR Weight Optimization Run Result CPU: Over/Under Shared Weight - CPC by LPAR2015-05-21 -2015-05-23OPTIMIZATION_ MOVEMENT0SCASCA ReportsN/APARENT_PROGRAMS29SCASCA ReportsN/APROD_MSU_FORECAST257074SCASCA: LPAR MSU Utilization SCA: Registered Products Matrix2015-04-01 - 2016-04-30 The MSU forecast can go to 2016-04-30PROD_MSU_OPTIMIZATION40802SCASCA: LPAR MSU Utilization SCA: Product MSU and Price SCA: Registered Products Matrix SCA: Software Cost Analysis Summary2015-05-02 - 2015-07-02SUBCAB_PROGRAMS467SCASCA: Product MSU and Price SCA: Registered Products Matrix SCA: Software Cost Analysis Summary SCA: NO89 Product MSU and Price2013-01-01 2013-01-01TIMEZONES31 AA, SCA, SMO CPU Reports, Application Analytics Reports, scar EPORTS, Memory ReportsN/ACreating a sample databaseYou can install a sample TDSz and IBM Capacity Management Analytics database that can be used to verify that Capacity Management Analytics has been properly installed and configured.

Running reports with the sample database

You can run reports with the sample database in IBM® Capacity Management Analytics.

The table in this section contain the Capacity Management Analytics tables, the number of rows within each table, the features and the reports that are mapped with each table. Date ranges are also provided for the sample reports.

Before running the reports, ensure all the packages of Framework Manager models are re-published with the correct schema. See Changing the default schema names in Framework Manager models

Table 13. IBM Capacity Management Analytics Tables (HCM schema).

CMA TABLES (HCM schema)
Number of rows
Feature
Report
Date Ranges (MIN/MAX dates) YYYY-MM-DD

ANOMALIES_CICS
0
PI
CICS Anomaly Details
N/A

APPL_DIST_MAPPING
0
AA
Application Analytics: Distributed CPU usage - applications
N/A

APPL_MAPPING
92
AA
Application Analytics: MIPS Used - Application
2016-09-30 creation date of map

APPL_ZOS_FORECAST
54032
SMO

CPU: MIPS Used - LPAR Level by WLM Importance

CPU: MIPS Used - zServer/LPAR Level

2015-05-21

2015-05-23

CUSTOMER_PRICE
159
SCA
SCA: NO89 Product MSU and Price

1900-01-01

1900-01-01

DIM_DATE
13878
SCA
CPU: AIX - 10 Highest/Lowest CPU Usage Peaks

2015-01-01

2015-01-01

DIM_MINUTE
1440
SMO
Anomaly Detection Analytics: CICS Anomaly Details
N/A

DIM_TIME
24
PI
Application Analytics: MIPS Used - Application
N/A

Distributed Server data from SMF 104

AA

CPU:AIX - CPU Usage

CPU:Linux for System X - CPU Usage

CPU:Linux for System Z - CPU Usage

2015-06-02 -2015-07-04

2015-07-31 – 2015-08-03

FORECAST_BILLABLE
247350
SCA
SCA: LPAR MSU Utilization
N/A

FORECAST_METADATA
4
SMO

CPU: MIPS Used - LPAR Level by WLM Importance

CPU: MIPS Used - zServer/LPAR Level

No forecast data

LPAR_MSU_FORECAST
9373
SCA

SCA: LPAR MSU Utilization

SCA: NO89 Product MSU and Price

SCA: Product MSU and Price

SCA: Software Cost Analysis Summary

2015-04-01 - 2015-05-03 Observed data

2015-04-01 – 2015-09-10 Forecast data

LPAR_MSU_

OPTIMIZATION

1488
SCA

SCA: LPAR MSU Utilization

SCA: NO89 Product MSU and Price

SCA: Product MSU and Price

SCA: Software Cost Analysis Summary

2015-05-02 - 2015-07-10

LPAR_WEIGHT_

OPTIMIZATION

2
SMO

CPU: LPAR Weight Optimization Run Result

CPU: MIPS Used - LPAR Level by WLM Importance

CPU: Over/Under Shared Weight - CPC by LPAR

2015-05-21 – 2015-05-23

LPAR_WEIGHT_

OPTIMIZATION_DETAIL

144
SMO
CPU: LPAR Weight Optimization Run Result

2015-05-21 - 2015-05-23

LPAR_WEIGHT_

OPTIMIZATION_

PARAMETERS

4
SMO
CPU: LPAR Weight Optimization Run Result
N/A

MIPS_CAPACITY
46
AA
Application Analytics Reports
N/A

MVSPM_CHANNEL
240
SMO

zServer Monitoring Dashboards

2015-05-21 - 2015-05-21

MVSPM_DEVICE
264
SMO
zServer Monitoring Dashboards
2015-05-21 - 2015-05-21

MVSPM_LPAR
12432
AA, CPF, SCA, SMO

CPU: MIPS Used - LPAR Level by WLM Importance

CPU: MIPS Used -Service Class Period Level

CPU: MIPS Used -System Level (Captured vs Uncaptured)

CPU: MIPS Used - zServer/LPAR Level

2015-04-01 -2015-05-28

MVSPM_LPAR continued
12432
AA, CPF, SCA, SMO

CPU: Over/Under Shared Weight - CPC by LPAR

Application Analytics: MIPS Used – Application

Application Analytics: MIPS Used - Application by Function

MVSPM_LPAR continued
12432
AA, CPF, SCA, SMO

SCA: LPAR MSU Utilization

SCA: NO89 Product MSU and Price

SCA: Product MSU and Price

MVSPM_LPAR continued
12432
AA, CPF, SCA, SMO

SCA: Software Cost Analysis Summary

zIIP/zAAP What ifs - LPAR Level

zServer Monitoring Dashboards

2015-04-01 -2015-05-28

KPMZ_JOB_INT_H

- SMF 30 data

N/A

Application Analytics: MIPS Used - Report Class by Job Names
No SMF 30 data available, only list of Report Classes 2015-05-21 – 2015-05-23

MVSPM_WORKLOAD2_H

from TDS/z, type 72 data

CPU: MIPS Used - LPAR Level by WLM Importance

CPU: MIPS Used -Service Class Period Level

Application Analytics: MIPS Used – Application

Application Analytics: MIPS Used - Application by Function

Application Analytics: MIPS Used - Application by LPAR

2015-05-21 – 2015-05-23 for LPAR JB0

MVSPM_LPAR_CPU_BUSY_

MIPS_PRED (hourly)

28320
SMO

CPU: MIPS Used - LPAR Level by WLM Importance

CPU: MIPS Used - zServer/LPAR Level

No forecast data for WLM reporting

2015-04-01 – 2015-07-27 is available for zServer/LPAR level reporting of forecast

MVSPM_LPAR_CPU_BUSY_

MIPS_PRED (daily)

0
SMO

CPU: MIPS Used - LPAR Level by WLM Importance

CPU: MIPS Used - zServer/LPAR Level

N/A

MVSPM_LPAR_CPU_BUSY

_MIPS_PRED (monthly)

0
SMO

CPU: MIPS Used - LPAR Level by WLM Importance

CPU: MIPS Used - zServer/LPAR Level

N/A

NO89_PRODUCTS
23
SCA

SCA: NO89 Product MSU and Price

SCA: NO89 Products Matrix

1900-01-01

1900-01-01

OPTIMIZATION_BILLABLE
39312
SCA

SCA: NO89 Product MSU and Price

SCA: Product MSU and Price

SCA: Registered Products Matrix

SCA: Software Cost Analysis Summary

2015-05-02 - 2015-07-02

OPTIMIZATION_METADATA
1
SMO

CPU: LPAR Weight Optimization Run Result

CPU: Over/Under Shared Weight - CPC by LPAR

2015-05-21 -2015-05-23

OPTIMIZATION_

MOVEMENT

0
SCA
SCA Reports
N/A

PARENT_PROGRAMS
29
SCA
SCA Reports
N/A

PROD_MSU_FORECAST
257074
SCA

SCA: LPAR MSU Utilization

SCA: Registered Products Matrix

2015-04-01 - 2016-04-30

The MSU forecast can go to 2016-04-30

PROD_MSU_OPTIMIZATION
40802
SCA

SCA: LPAR MSU Utilization

SCA: Product MSU and Price

SCA: Registered Products Matrix

SCA: Software Cost Analysis Summary

2015-05-02 - 2015-07-02

SUBCAB_PROGRAMS
467
SCA

SCA: Product MSU and Price

SCA: Registered Products Matrix

SCA: Software Cost Analysis Summary

SCA: NO89 Product MSU and Price

2013-01-01

2013-01-01

TIMEZONES
31
AA, SCA, SMO

CPU Reports, Application Analytics Reports, scar EPORTS, Memory Reports

N/A

Parent topic: Creating a sample database

Dropping the sample databaseOnce you have used your production data, then the sample database must be dropped.sample databasedroppingDrop the feature base first by running the command cmabase.py --dropdb --run.Drop the sample database by running the command cmasampledb.py –dropdb --run.Creating a sample databaseYou can install a sample TDSz and IBM Capacity Management Analytics database that can be used to verify that Capacity Management Analytics has been properly installed and configured.

Dropping the sample database

Once you have used your production data, then the sample database must be dropped.

Procedure

Drop the feature base first by running the command cmabase.py --dropdb --run.

Drop the sample database by running the command cmasampledb.py –dropdb --run.

Parent topic: Creating a sample database

Manually installing IBM Capacity Management Analytics 2.1.1If you want to run IBM Capacity Management Analyticson a platform other than 64-bit Red Hat Enterprise Linux Server Edition version 6 and 7, you must install the product manually.installationmanualEnsure you have all the prerequisite software installed before beginning your manual installation of Capacity Management Analytics. For operating systems other than 64-bit Red Hat Enterprise Linux Server Edition version 6 and 7, you cannot use the Solution Installer to run Capacity Management Analytics and must install the product manually.Extract the Capacity Management Analytics Solution Kit in your CMAHOME directory.Update your profile. You can use the sample profile hcm.profile in CMAHOME/samples.Run cmaconfig.py. cmaconfig.py configures Capacity Management Analytics to update the SPSS Modeler setup, Cognos setup, SDAP setup, and so on. This program must be run using your Capacity Management Analytics administrator ID and sudo.sudo CMAHOME=$CMAHOME PYTHONPATH=$PYTHONPATH ${CMAHOME}/bin/cmaconfig.py --COGNOSHOME=<COGNOSHOME> --SDAPHOME=<SDAPHOME> --SPSSHOME=<SPSSHOME> --CLEMBHOME=<CLEMBHOME> --JAVAHOME=<JAVA_HOME> --DB2_HOME=<DB2_HOME> --CPLEXHOME=<CPLEXHOME> --CMA_ADM=<CMA_ADM> --install--COGNOSHOME=COGNOSHOMECognos Home Directory--SDAPHOME=SDAPHOMESDAP Home Directory--SPSSHOME=SPSSHOMESPSS Modeler Server Home Directory--CLEMBHOME=CLEMBHOMESPSS Modeler Batch Home Directory--JAVAHOME=JAVAHOMEJAVA Home Directory--DB2_HOME=DB2_HOMEDB2 Home Directory--CPLEXHOME=CPLEXHOMECPLEX Home Directory--CMA_ADM=CMA_ADMCMA Admin ID--installInstall CMA Configuration[--verbose]Verbose mode[--trace=tracelevel]Level of trace, 1-4 To learn more about cmaconfig.py, run CMAHOME/bin/cmaconfig.py -h.Restart Cognos to apply your settings.Run cmahome.py. cmahome.py helps create your own configuration and installs Capacity Management Analytics features. It will create a cmainstance-config.js in the CMAHOME/configuration folder with your inputs. It will also install the Capacity Management Analytics features you have specified. This program must be run using your Capacity Management Analytics administrator ID.cmahome.py --CMADBHOST=<CMADBHOST> --HCM_INDEXBP=<HCM_INDEXBP> --HCM_BP=<HCM_BP> --CMADBPORT=<CMADBPORT> --CMADBSID=<CMADBSID> --CMADBLOCNAME=<CMADBLOCNAME> --HCM_CCSID=<HCM_CCSID> --HCM_STOGROUP=<HCM_STOGROUP> --WLM_GENERAL=<WLM_GENERAL> --HCM_DATABASE=<HCM_DATABASE> --HCM_SCHEMA=<HCM_SCHEMA> --DRL_DATABASE=<DRL_DATABASE> --DRL_SCHEMA=<DRL_SCHEMA> --DRL_STOGROUP=<DRL_STOGROUP> --HOST=<HOST> --DB2_HOME=<DB2_HOME> --CPLEXHOME=<CPLEXHOME> --SPSSHOME=<SPSSHOME> --CLEMBHOME=<CLEMBHOME> --SDAPHOME=<SDAPHOME> --COGNOSHOME=<COGNOSHOME> --COGNOSNAMESPACE=<COGNOSNAMESPACE> --JAVAHOME=<JAVAHOME> --CMA_ADM=<CMA_ADM> --CMA_PWD=<CMA_PWD> --CMADBAUSR=<CMADBAUSR> --CMADBAPWD=<CMADBAPWD> --DB2LUW_ADM=<DB2LUW_ADM> --DB2LUW_PWD=<DB2LUW_PWD> --HCM_USER=<HCM_USER> --HCM_PWD=<HCM_PWD> --CMADBALIAS=<CMADBALIAS> --feature=<FEATURE> --sample --install –run--CMADBHOST=CMADBHOSTCMA Database Host Name--HCM_INDEXBP=HCM_INDEXBPCMA Database Index Buffer Pool--HCM_BP=HCM_BPCMA Database Buffer Pool--CMADBPORT=CMADBPORTCMA Database Port--CMADBSID=CMADBSIDCMA Database Sub System ID--CMADBLOCNAME=CMADBLOCNAMECMA Database Location Name--HCM_CCSID=HCM_CCSIDCMA Database CCSID Encoding--HCM_STOGROUP=HCM_STOGROUPCMA Database Storage Group Name--WLM_GENERAL=WLM_GENERALDB2 for z/OS General WLM Environment Name--HCM_DATABASE=HCM_DATABASECMA Database Name--HCM_SCHEMA=HCM_SCHEMACMA Database Schema Name--DRL_DATABASE=DRL_DATABASETDSz Database Name--DRL_SCHEMA=DRL_SCHEMATDSz Database Schema Name--DRL_STOGROUP=DRL_STOGROUPTDSz Database Storage Group--HOST=HOSTHost Name where Cognos / SPSS / CPLEX is installed--DB2_HOME=DB2_HOMEDB2 Database Path--CPLEXHOME=CPLEXHOMECPLEX Home Directory--SPSSHOME=SPSSHOMESPSS Modeler Server Home Directory--CLEMBHOME=CLEMBHOMESPSS Modeler Batch Home Directory--SDAPHOME=SDAPHOMESDAP Home Directory--COGNOSHOME=COGNOSHOME Cognos Home Directory--COGNOSNAMESPACE=COGNOSNAMESPACECognos Security Namespace--JAVAHOME=JAVAHOMEJava Path--CMA_ADM=CMA_ADM CMA Admin User--CMA_PWD=CMA_PWDCMA Admin Password--CMADBAUSR=CMADBAUSRCMA Database Admin User--CMADBAPWD=CMADBAPWDCMA Database Admin Password--DB2LUW_ADM=DB2LUW_ADMDB2LUW Admin User--DB2LUW_PWD=DB2LUW_PWDDB2LUW Admin Password--HCM_USER=HCM_USER CMA User Name--HCM_PWD=HCM_PWDCMA User PasswordHCM_USER and HCM_PWD use the zlinux machine and z/OS database. Their usernames and passwords should match. Passwords are case sensitive.--CMADBALIAS=CMADBALIASName of CMA Database Alias--feature=FEATUREInstall CMA OR CMA specific features into your home. BASE (CMA base), AA (Application Analytics),PI (Anomaly Detection), CPF (Capacity Planning and Forecasting), SCA (Software Cost Analysis), SMO (Systems Management and Optimizations) Example: AA, CPF, SMO. Default is ALL--installInstall CMA--run Run/Execute the generated commands for a feature--sampleWill install the sample database feature[--trace=tracelevel]Trace Level, 1-4[--verbose]Verbose mode To learn more about cmahome.py, run CMAHOME/bin/cmahome.py -h.Run cmainstance. cmainstance.py creates your own instance and installs Capacity Management Analytics features. You can also use cmainstance to create a database, set up Cognos, run SPSS Modeler streams and the CPLEX optimization program. All Capacity Management Analytics users, including the administrator, can use cmainstance to create and manage their instance. cmainstance will use the cmainstance-config.js in the CMAHOME/configuration folder and change the parameters according to what you specify on the command line and will save the file in your CMAINSTANCE/configuration directory. You can always update your configuration file by executing cmainstance.py. For instance, cmainstance.py --HCM_SCHEMA=<HCM_SCHEMA> .cmainstance.py --CMAINSTANCE=<CMAINSTANCE> --instance --run --sample --feature=<FEATURE>--feature=FEATURE Install CMA OR CMA specific features into your instance. BASE (CMA base), AA (Application Analytics),PI (Anomaly Detection), CPF (Capacity Planning and Forecasting), SCA (Software Cost Analysis), SMO (Systems Management and Optimizations) Example: AA, CPF, SMO. Default is ALL.--instanceCreate CMA instance--run Run and execute the generated commands for a feature--sampleWill create the sample database[--trace=tracelevel]Trace Level, 1-4[--verbose]Verbose mode The command will create your instance, install Capacity Management Analytics features into your instance, set up Cognos reports, create a sample database and a Capacity Management Analytics database. It will also execute streams with the sample data. You will the results in the Capacity Management Analyticsdatabase and Cognos reports. If you want to use your own data instead of the sample data, you can execute cmainstance.py --instance first to create your instance and install Capacity Management Analytics features in your instance. Then review and make changes corresponding to your data and run the individual feature.py program to do feature work. To learn more about cmahome.py, run CMAHOME/bin/cmainstance.py -h.Completing Capacity Management Analytics input filesThis section will provide you with details on how to fill in your IBM Capacity Management Analytics input files.

Manually installing IBM Capacity Management Analytics 2.1.1

If you want to run IBM® Capacity Management Analyticson a platform other than 64-bit Red Hat Enterprise Linux Server Edition version 6 and 7, you must install the product manually.

Before you begin

Ensure you have all the prerequisite software installed before beginning your manual installation of Capacity Management Analytics.

About this task

For operating systems other than 64-bit Red Hat Enterprise Linux Server Edition version 6 and 7, you cannot use the Solution Installer to run Capacity Management Analytics and must install the product manually.

Procedure

Extract the Capacity Management Analytics Solution Kit in your CMAHOME directory.

Update your profile. You can use the sample profile hcm.profile in CMAHOME/samples.

Run cmaconfig.py. cmaconfig.py configures Capacity Management Analytics to update the SPSS Modeler setup, Cognos setup, SDAP setup, and so on. This program must be run using your Capacity Management Analytics administrator ID and sudo.

sudo CMAHOME=$CMAHOME PYTHONPATH=$PYTHONPATH ${CMAHOME}/bin/cmaconfig.py --COGNOSHOME=<COGNOSHOME> --SDAPHOME=<SDAPHOME> --SPSSHOME=<SPSSHOME> --CLEMBHOME=<CLEMBHOME> --JAVAHOME=<JAVA_HOME> --DB2_HOME=<DB2_HOME> --CPLEXHOME=<CPLEXHOME> --CMA_ADM=<CMA_ADM> --install

--COGNOSHOME=COGNOSHOME

Cognos Home Directory

--SDAPHOME=SDAPHOME

SDAP Home Directory

--SPSSHOME=SPSSHOME

SPSS Modeler Server Home Directory

--CLEMBHOME=CLEMBHOME

SPSS Modeler Batch Home Directory

--JAVAHOME=JAVAHOME

JAVA Home Directory

--DB2_HOME=DB2_HOME

DB2 Home Directory

--CPLEXHOME=CPLEXHOME

CPLEX Home Directory

--CMA_ADM=CMA_ADM

CMA Admin ID

--install

Install CMA Configuration

[--verbose]

Verbose mode

[--trace=tracelevel]

Level of trace, 1-4

Note: To learn more about cmaconfig.py, run CMAHOME/bin/cmaconfig.py -h.

Restart Cognos to apply your settings.

Run cmahome.py. cmahome.py helps create your own configuration and installs Capacity Management Analytics features. It will create a cmainstance-config.js in the CMAHOME/configuration folder with your inputs. It will also install the Capacity Management Analytics features you have specified. This program must be run using your Capacity Management Analytics administrator ID.

cmahome.py --CMADBHOST=<CMADBHOST> --HCM_INDEXBP=<HCM_INDEXBP> --HCM_BP=<HCM_BP> --CMADBPORT=<CMADBPORT> --CMADBSID=<CMADBSID> --CMADBLOCNAME=<CMADBLOCNAME> --HCM_CCSID=<HCM_CCSID> --HCM_STOGROUP=<HCM_STOGROUP> --WLM_GENERAL=<WLM_GENERAL> --HCM_DATABASE=<HCM_DATABASE> --HCM_SCHEMA=<HCM_SCHEMA> --DRL_DATABASE=<DRL_DATABASE> --DRL_SCHEMA=<DRL_SCHEMA> --DRL_STOGROUP=<DRL_STOGROUP> --HOST=<HOST> --DB2_HOME=<DB2_HOME> --CPLEXHOME=<CPLEXHOME> --SPSSHOME=<SPSSHOME> --CLEMBHOME=<CLEMBHOME> --SDAPHOME=<SDAPHOME> --COGNOSHOME=<COGNOSHOME> --COGNOSNAMESPACE=<COGNOSNAMESPACE> --JAVAHOME=<JAVAHOME> --CMA_ADM=<CMA_ADM> --CMA_PWD=<CMA_PWD> --CMADBAUSR=<CMADBAUSR> --CMADBAPWD=<CMADBAPWD> --DB2LUW_ADM=<DB2LUW_ADM> --DB2LUW_PWD=<DB2LUW_PWD> --HCM_USER=<HCM_USER> --HCM_PWD=<HCM_PWD> --CMADBALIAS=<CMADBALIAS> --feature=<FEATURE> --sample --install –run

--CMADBHOST=CMADBHOST

CMA Database Host Name

--HCM_INDEXBP=HCM_INDEXBP

CMA Database Index Buffer Pool

--HCM_BP=HCM_BP

CMA Database Buffer Pool

--CMADBPORT=CMADBPORT

CMA Database Port

--CMADBSID=CMADBSID

CMA Database Sub System ID

--CMADBLOCNAME=CMADBLOCNAME

CMA Database Location Name

--HCM_CCSID=HCM_CCSID

CMA Database CCSID Encoding

--HCM_STOGROUP=HCM_STOGROUP

CMA Database Storage Group Name

--WLM_GENERAL=WLM_GENERAL

DB2 for z/OS General WLM Environment Name

--HCM_DATABASE=HCM_DATABASE

CMA Database Name

--HCM_SCHEMA=HCM_SCHEMA

CMA Database Schema Name

--DRL_DATABASE=DRL_DATABASE

TDSz Database Name

--DRL_SCHEMA=DRL_SCHEMA

TDSz Database Schema Name

--DRL_STOGROUP=DRL_STOGROUP

TDSz Database Storage Group

--HOST=HOST

Host Name where Cognos / SPSS / CPLEX is installed

--DB2_HOME=DB2_HOME

DB2 Database Path

--CPLEXHOME=CPLEXHOME

CPLEX Home Directory

--SPSSHOME=SPSSHOME

SPSS Modeler Server Home Directory

--CLEMBHOME=CLEMBHOME

SPSS Modeler Batch Home Directory

--SDAPHOME=SDAPHOME

SDAP Home Directory

--COGNOSHOME=COGNOSHOME

Cognos Home Directory

--COGNOSNAMESPACE=COGNOSNAMESPACE

Cognos Security Namespace

--JAVAHOME=JAVAHOME

Java Path

--CMA_ADM=CMA_ADM

CMA Admin User

--CMA_PWD=CMA_PWD

CMA Admin Password

--CMADBAUSR=CMADBAUSR

CMA Database Admin User

--CMADBAPWD=CMADBAPWD

CMA Database Admin Password

--DB2LUW_ADM=DB2LUW_ADM

DB2LUW Admin User

--DB2LUW_PWD=DB2LUW_PWD

DB2LUW Admin Password

--HCM_USER=HCM_USER

CMA User Name

--HCM_PWD=HCM_PWD

CMA User Password

Note: HCM_USER and HCM_PWD use the zlinux machine and z/OS database. Their usernames and passwords should match. Passwords are case sensitive.

--CMADBALIAS=CMADBALIAS

Name of CMA Database Alias

--feature=FEATURE

Install CMA OR CMA specific features into your home. BASE (CMA base), AA (Application Analytics),PI (Anomaly Detection), CPF (Capacity Planning and Forecasting), SCA (Software Cost Analysis), SMO (Systems Management and Optimizations) Example: AA, CPF, SMO. Default is ALL

--install

Install CMA

--run

Run/Execute the generated commands for a feature

--sample

Will install the sample database feature

[--trace=tracelevel]

Trace Level, 1-4

[--verbose]

Verbose mode

Note: To learn more about cmahome.py, run CMAHOME/bin/cmahome.py -h.

Run cmainstance. cmainstance.py creates your own instance and installs Capacity Management Analytics features. You can also use cmainstance to create a database, set up Cognos, run SPSS Modeler streams and the CPLEX optimization program. All Capacity Management Analytics users, including the administrator, can use cmainstance to create and manage their instance. cmainstance will use the cmainstance-config.js in the CMAHOME/configuration folder and change the parameters according to what you specify on the command line and will save the file in your CMAINSTANCE/configuration directory. You can always update your configuration file by executing cmainstance.py. For instance, cmainstance.py --HCM_SCHEMA=<HCM_SCHEMA> .

cmainstance.py --CMAINSTANCE=<CMAINSTANCE> --instance --run --sample --feature=<FEATURE>

--feature=FEATURE

Install CMA OR CMA specific features into your instance. BASE (CMA base), AA (Application Analytics),PI (Anomaly Detection), CPF (Capacity Planning and Forecasting), SCA (Software Cost Analysis), SMO (Systems Management and Optimizations) Example: AA, CPF, SMO. Default is ALL.

--instance

Create CMA instance

--run

Run and execute the generated commands for a feature

--sample

Will create the sample database

[--trace=tracelevel]

Trace Level, 1-4

[--verbose]

Verbose mode

The command will create your instance, install Capacity Management Analytics features into your instance, set up Cognos reports, create a sample database and a Capacity Management Analytics database. It will also execute streams with the sample data. You will the results in the Capacity Management Analyticsdatabase and Cognos reports.

If you want to use your own data instead of the sample data, you can execute cmainstance.py --instance first to create your instance and install Capacity Management Analytics features in your instance. Then review and make changes corresponding to your data and run the individual feature.py program to do feature work.

Note: To learn more about cmahome.py, run CMAHOME/bin/cmainstance.py -h.

Completing Capacity Management Analytics input files
This section will provide you with details on how to fill in your IBM Capacity Management Analytics input files.

Completing Capacity Management Analytics input filesThis section will provide you with details on how to fill in your IBM Capacity Management Analytics input files.cmabase.parms -PBLOCKSIZEYou can decrease the number of times data is inserted into the database by increasing BLOCKSIZE, and the stream can run faster. However, larger BLOCKSIZE increases the duration of each insertion. BLOCKSIZE is measured in days. When you enter the BLOCKSIZE value, enter the number of days of data that you want to treat as a block. For example, 30. -PSDATEThe start date that you want use for MIPS, CHANNEL, and DEVICE related data. For example, "2015-01-01" -PEDATEThe start date that you want use for MIPS, CHANNEL, and DEVICE related data. For example, "2016-12-31" -PSTART_DATE_DISThe start date you want use for analyzing data on a distributed system. For example, "2015-01-01" -PEND_DATE_DISThe end date you want use for analyzing data on a distributed system. For example, "2015-12-31"cmasca.parms -PUSERSELECTION“Define” or “weight”. “Define” indicates that only the define capacity is considered in the optimization algorithm. “Weight” indicates that both weighted capacity and define capacity are considered in the optimization algorithm. -PDATEMINThe SCA product forecast stream uses the date from DATEMIN to the latest date to forecast all of the product and LPAR combinations. For example, "2015-01-01" -PODDObsolete detection days. If a product no longer exists or does not run for recent ODD days in one LPAR, we assume that this combination is obsolete and the stream does not forecast this combination. All of the obsoleted combinations appear in the product forecast log file. For example, 20. -PREFRESH“True” or “false”. If you want to replace current data, then set it to true, otherwise set it to false. -PIWP“True” or “false”. IWP is Integrated Workload Pricing. For more information on IWP and the appropriate settings, see IWP Defining Programs (http://www.ibm.com/systems/z/resources/swprice/reference/exhibits/iwp.html) -PGSSPFILEThe full path of the server location for the gssp_scaling_factors.csv file. For example, CMAINSTANCEDATA/gssp_scaling_factors.csv -PMLCFILEThe full path of the server location for the mlc_prod.csv file. For example, CMAINSTANCEDATA/mlc_prod.csv. -PIPLAFILEThe full path of the server location for the ipla_prod.csv file. For example, CMAINSTANCEDATA/ipla_prod.csv. -PENTERPRISEFILEThe full path of the server location for the enterprise.csv file. For example, CMAINSTANCEDATA/enterprise.csv. -PCURRENCYFILEThe full path of the server location for the currency.csv file. For example, CMAINSTANCEDATA/currency.csv. -PNO89_FILEThe full path of the server location for the no89 file. For example, CMAINSTANCEDATA/no89. -PCAREFILEThe full path of the server location for the opt_care_prod.csv optimization file. For example, CMAINSTANCEDATA/opt_care_prod.csv. -PRESERVEDFILEThe full path of the server location for the opt_reserved_prod.csv optimization file. For example, CMAINSTANCEDATA/opt_reserved_prod.csv. -PBOUNDFILEThe full path of the server location for the opt_bound_prod.csv optimization file. For example,CMAINSTANCEDATA/opt_bound_prod.csv.cmasmo.parms -PTOPThe amount of TOP records you want to get. For example, 10. -PSTART_DATE_DISThe start date you want use for analyzing data on a distributed system. For example, "2015-01-01" -PEND_DATE_DISThe end date you want use for analyzing data on a distributed system. For example, "2015-12-31" -PREFRESH“True” or “false”. If you want to replace current data, then set it to true, otherwise set it to false. -PBLOCKSIZEYou can decrease the number of times data is inserted into the database by increasing BLOCKSIZE, and the stream can run faster. However, larger BLOCKSIZE increases the duration of each insertion. BLOCKSIZE is measured in days. When you enter the BLOCKSIZE value, enter the number of days of data that you want to treat as a block. For example, 30. -PSDATEThe start date that you want use for MIPS, CHANNEL, and DEVICE related data. For example, "2015-01-01" -PEDATEThe start date that you want use for MIPS, CHANNEL, and DEVICE related data. For example, "2016-12-31"cmapi.parms -PANOMALY_RATIODefines the ratio that identifies abnormal transactions. A smaller ratio means that a smaller deviation identifies an anomaly, resulting in more anomalies being detected. A larger ratio means that a larger deviation is required to identify an anomaly, resulting in fewer anomalies being detected. The default value is 1. -PMEMORYThe amount of memory on your modeler server machine. -PCPU_RESPONSE_RATIOIdentifies what should be considered the abnormal ratio between response seconds and CPU seconds of a single transaction. If your normal CICS transactions have a small differential between response seconds and CPU seconds, the ratio you set should be small. When the differential between response seconds and CPU seconds is larger than the CPU_RESPONSE_RATIO you specify, the transaction is considered abnormal. The default value is 50. -PSCORE_RATIODefines the ratio that the tree-based anomaly model uses to judge whether the transaction is abnormal or not. The default value is 0.5. The recommended range of this parameter value is [0.5,0.6]. -PTRANSACTIONFILEThe full path of server location where the transaction.csv file is located. For example, CMAINSTANCEDATA/transaction.csv. -PDATEFILEThe full path of server location where the data_info.csv file is located. For example ,CMAINSTANCEDATA/date_info.csv.cmacpf.parms -PDATE_START The start date you want to use for MIPS forecast. For example, “2015-01-01” -PDATE_ENDThe start date you want to use for MIPS forecast. For example, “2015-12-31” -PHOUR_STARTThe start hour of the observed data that you are using. For example, 0. -PHOUR_ENDThe end hour of the observed data that you are using. For example, 23". -PPEAKRANKThe Nth highest peak rank values. Valid values are in the range [1,5]. You can also input multiple rank values. For example, 1, 2, 3, and 4 represent highest peak, 2nd highest peak, 3rd highest peak, and 4th highest peak. For example, "1,2" cmaaa.parms -PDATE_START_APPLThe start date that you want use for capture ratio data. For example, 2015-01-01 -PDATE_END_APPL The end date that you want use for capture ratio data. For example, 2016-01-01 -PTIME_START_APPLThe start time that you want use for capture ratio data. The default value is 00:00:00. -PTIME_END_APPLThe end time that you want use for capture ratio data. The default value is 23:59:59. -PREFRESH“True” or “false”. If you want to replace current data, then set it to true, otherwise set it to false. -PDATE_START The start date of the observed data that you are using. For example, "2015-01-01". -PDATE_ENDThe end date of the observed data that you are using. For example, "2015-06-30" -PHOUR_STARTThe start hour of the observed data that you are using. For example, 0. -PHOUR_ENDThe end hour of the observed data that you are using. For example, 23" -PPEAKRANK The Nth highest peak rank values. Valid values are in the range [1,5]. You can also input multiple rank values. For example, “1,2” -PAPPFILEThe full path of server location where the appl_lob_z.csv file is located. For example, CMAINSTANCEDATA/appl_lob_z.csv. -PAPPFILEDISThe full path of server location where the appl_lob.csv file is located. For example CMAINSTANCEDATA/appl_lob.csv. mlc_prod.csvThis file defines the licensing, pricing, and system information for each MLC product for which you want to view monetary values in the Software Cost Analysis reports and workspace. The monetary values are expressed in the currency that is defined for an enterprise in the currency.csv file.CPU_SERIAL_NO is the last 4 characters of the machine serial number. The CPU serial numbers that are listed here must match with the serial numbers that are listed in the enterprise.csv file.PID is an eight-character field that must follow the IBM format (XXXX-XXX) for a product ID. LICENSE is a six-character field. It must be one of the acronyms that are listed here. To view details on each license, refer to IBM System z Software Pricing (www.ibm.com/systems/z/resources/swprice/mlc/index.html). AWLC – Advanced Workload License ChargeAEWLC – Advanced Entry Workload License ChargeEWLC – Entry Workload License ChargeVWLC – Variable Workload License ChargeMWLC – Midrange Workload License ChargeZNALC – System z New Application Workload License ChargeTIER is a five-character field that specifies the tier/level for an associated LICENSE. To view details on the tiers/levels for each license, refer to IBM System z Software Pricing (www.ibm.com/systems/z/resources/swprice/mlc/index.html). PRICE_PER_MSU is a decimal value that goes up to two decimal places.ipla_prod.csvThis file defines the VUE, pricing, and system information for each IPLA product for which you want to view monetary values in the Software Cost Analysis reports and workspace. The entitlement for each IPLA is by CPU Serial Number, not the entire logical enterprise. CPU_SERIAL_NO is the last 4 characters of the machine serial number. The CPU serial numbers that are listed here must match with the serial numbers that are listed in the enterprise.csv file.PID is an eight-character field that must follow the IBM format (XXXX-XXX) for a product ID. SS_PID is an eight character field that must follow the IBM format (XXXX-XXX) for the Subscription and Support product ID. This value can be left blank if Subscription and Support were not purchased for a product.VUE is the six-character field for the Value Unit Exhibit (VUE) for a product. It must be one of the VUEs listed here. Refer toOverview of Value Unit Exhibits (www.ibm.com/systems/z/resources/swprice/zipla/vue.html) for more details.VUE001VUE007VUE020ASSIGNED_ENTITLED_MSU is a numeric field. The entitled MSUs are specifically for the corresponding CPU_SERIAL_NO, not the entire enterprise entitlement. Adding up each entitlement totals the enterprise entitlement. PRICE_PER_VU is a decimal value that goes up to two decimal places.ASSIGNED_SS_ENTITLED_MSU is a numeric field. The entitled MSUs are specifically for the corresponding CPU_SERIAL_NO, not the entire enterprise entitlement. This value can be left blank if Subscription and Support were not purchased for a product.SS_PRICE_PER_MTH specifies the monthly price of Subscription and Support. It is a decimal value that goes up to two decimal places. enterprise.csvThis file sets the CPU serial number for each enterprise.ENTERPRISE is a 20-character enterprise name. The enterprise names in the enterprise.csv file must match with the names defined in the currency.csv file.CPU_SERIAL_NO is the last 4 characters of the machine serial number. If a CPU serial number is missing in this table, then the CPU Serial No is missing in Software Cost Analysis Summary workspaceManaging System z capacity can be costly and complex. This workspace allows you to better manage your software costs on System z by providing graphs and tables that show both the details and the summary of the software costs..currency.csvThis file defines the currency of each logically named enterprise. An enterprise is defined as a grouping of mainframe systems that all have the same currency.ENTERPRISE is a 20-character enterprise name.CURRENCY is the three character international symbol for the currency that is used to display monetary values for the associated ENTERPRISE in reports. The default decimal separator for values in these files is the period, which is the ISO format. If a non-ISO format is used, then the SPSS Modeler stream properties must be modified to use the same decimal. gssp_scaling_factors.csv file: PROD_ID: the IBM product ID registered on the System z machine.GSSP_SCALING_FACTOR: the GSSP scaling factor for the product.no89 Prepare the registration file of NO89 products. The file should follow the conventions of NO89 DD statements that are described in the topic Modifying the NO89 DD statement in the SCRT Users Guide (www.ibm.com/common/ssi/cgi-bin/ssialias?infotype=SA&subtype=WH&htmlfid=ZSL03022USEN). The definition section of the file should begin with //NO89 and end with //SYSLIN DD For example://no89 * LOTUS ACTIVE INSIGHT FOR Z/OS V6 5655-U15=2827-BAA6:JB0,2817-4E15:JE0,2097-99FF:JC0 * COBOL FOR OS/390 & VM V2 5648-A25=CF3,CF2,J90,JB0 * VA PL/I FOR OS/390 V2 5655-B22=*NONE //SYSLIN DD holiday.csv Date—The date of the holiday or the day on which the MIPS volume is notable.Isholiday—Whether this date is a holiday. 1 indicates that the date is a holiday, 0 indicates that it is not.D_name—The name of the holiday. Note that this column is for reference and is not required.Ishigh—Whether the MIPS volume on a date is high over the data range. 1 indicates MIPS_USED is high for this date, 0 means MIPS_USED is not high for this date.Islow—Whether the MIPS volume on a date is low below the data range. 1 indicates MIPS_USED is low for this date, 0 means MIPS_USED is not low for this date. Date format in holiday.csv should be ISO format (YYYY-MM-DD). If using a non-ISO format, the stream properties of forecast streams must be updated to match the date format in holiday.csv. For more information about modifying the holiday.csv file, see Time period definitionDefining the time period encompasses several distinct tasks, including modifying the holiday.csv file, modifying the Time Interval node, and setting the number of time periods.. If you use Microsoft Excel to edit csv files and your computer uses a different locale than what is used in the SPSS Modeler streams, you must ensure that the data format in the csv remains consistent with date format that is used in the streams. Microsoft Excel can change the locale that is used in the file to match your computer's locale.opt_care_prod.csv This file is used to identify the cared products. If you do not need to identify any products as cared, do not enter anything in this file.For any product that is considered a cared product, enter the product ID in the PROD_ID column.For each cared product, enter 1 in the CARED column. If a PROD_ID is entered, but CARED is not set or is set to 0, this product is considered as not cared.opt_bound_prod.csv This file is used to identify bounded products. If you do not need to identify any products as bounded, do not enter anything in this file.If two or more products need to be bounded, enter the product IDs individually in the PROD_ID column.Enter the identical numeric identifier for the products to be bounded in the BOUND_ID column. The numeric identifier must be unique for each group of bounded products. For example, if product IDs 5635-A03 and 5655-R36 should be bounded, enter each product ID in separate cells in the PROD_ID column, and assign the numeric identifier 1 in the BOUND_ID column for each product.Example of the bounded_prod tab.Repeat steps 1 and 2 for each group of products that should be bounded.opt_reserved_prod.csv This file is used to identify reserved products. If you do not need to identify any products as reserved, do not enter anything in this file.For each product to be reserved, enter the product ID in the PROD_ID column.Enter the name of the LPAR reserved for the product in the LPAR_NAME column.Enter the CPC number of the LPAR in the CPC_SERIAL_NO column.Set the reserved flag in the RESERVED column.Enter the reserved amount by MSU in the RESERVED_MSU column. Example of reserved_prod tab. If RESERVED is 1 and RESERVED_MSU is n, the product is arranged in the specified LPAR with an amount equal or above n. If RESERVED is 1 and RESERVED_MSU is 0, the product is arranged in the specified LPAR with an amount above 0. If RESERVED is 1 and RESERVED_MSU is n, but n is beyond the MSU the product can produce, then the product is kept in its original LPAR after optimization. If the product is reserved in an LPAR that can not host it for any reason, then the reservation is ignored and the stream seeks any suitable LPAR for the product.transaction.csv Specify the transactions for which you want to perform anomaly detection. This file contains a single column, TRANSACTION_ID. You must enter the full value for the TRANSACTION_ID. You cannot use wildcard characters.date_info.csv This file contains three columns. Do not modify the value in the first column. In the second column, specify the date at which anomaly detection scoring should begin for the specified TRANSACTION_ID. Leave the third column value empty. The third column can optionally specify the end date for anomaly detection scoring, but Capacity Management Analytics generates scoring data for all dates after the specified start date. For information on the following files, refer to Configuring input files for LPAR weight optimizationTo run the IBM Capacity Management Analytics LPAR weight optimization scripts, you must provide input data in separate CSV files.. lwo_parameters.csvlwo_parameters_model.csvlwo_parameters_date_list.csvlwo_parameters_per_lpar.csv For information on the following files, refer to Mapping table definitions for application analytics reportingThe application analytics reports categorize the application information by using mapping tables. The application mapping is defined in two spreadsheet files that are provided with the Capacity Management Analytics Solution Kit installation. one file (appl_lob_z.csv) is used for z/OS systems. The other (appl_lob.csv) is used for distributed systems..appl_lob_z.csvappl_lob.csvManually installing IBM Capacity Management Analytics 2.1.1If you want to run IBM Capacity Management Analyticson a platform other than 64-bit Red Hat Enterprise Linux Server Edition version 6 and 7, you must install the product manually.

Completing Capacity Management Analytics input files

This section will provide you with details on how to fill in your IBM® Capacity Management Analytics input files.

cmabase.parms

-PBLOCKSIZE

You can decrease the number of times data is inserted into the database by increasing BLOCKSIZE, and the stream can run faster. However, larger BLOCKSIZE increases the duration of each insertion. BLOCKSIZE is measured in days. When you enter the BLOCKSIZE value, enter the number of days of data that you want to treat as a block. For example, 30.

-PSDATE

The start date that you want use for MIPS, CHANNEL, and DEVICE related data. For example, "2015-01-01"

-PEDATE

The start date that you want use for MIPS, CHANNEL, and DEVICE related data. For example, "2016-12-31"

-PSTART_DATE_DIS

The start date you want use for analyzing data on a distributed system. For example, "2015-01-01"

-PEND_DATE_DIS

The end date you want use for analyzing data on a distributed system. For example, "2015-12-31"

cmasca.parms

-PUSERSELECTION

“Define” or “weight”. “Define” indicates that only the define capacity is considered in the optimization algorithm. “Weight” indicates that both weighted capacity and define capacity are considered in the optimization algorithm.

-PDATEMIN

The SCA product forecast stream uses the date from DATEMIN to the latest date to forecast all of the product and LPAR combinations. For example, "2015-01-01"

-PODD

Obsolete detection days. If a product no longer exists or does not run for recent ODD days in one LPAR, we assume that this combination is obsolete and the stream does not forecast this combination. All of the obsoleted combinations appear in the product forecast log file. For example, 20.

-PREFRESH

“True” or “false”. If you want to replace current data, then set it to true, otherwise set it to false.

-PIWP

“True” or “false”. IWP is Integrated Workload Pricing. For more information on IWP and the appropriate settings, see IWP Defining Programs (http://www.ibm.com/systems/z/resources/swprice/reference/exhibits/iwp.html)

-PGSSPFILE

The full path of the server location for the gssp_scaling_factors.csv file. For example, CMAINSTANCEDATA/gssp_scaling_factors.csv

-PMLCFILE

The full path of the server location for the mlc_prod.csv file. For example, CMAINSTANCEDATA/mlc_prod.csv.

-PIPLAFILE

The full path of the server location for the ipla_prod.csv file. For example, CMAINSTANCEDATA/ipla_prod.csv.

-PENTERPRISEFILE

The full path of the server location for the enterprise.csv file. For example, CMAINSTANCEDATA/enterprise.csv.

-PCURRENCYFILE

The full path of the server location for the currency.csv file. For example, CMAINSTANCEDATA/currency.csv.

-PNO89_FILE

The full path of the server location for the no89 file. For example, CMAINSTANCEDATA/no89.

-PCAREFILE

The full path of the server location for the opt_care_prod.csv optimization file. For example, CMAINSTANCEDATA/opt_care_prod.csv.

-PRESERVEDFILE

The full path of the server location for the opt_reserved_prod.csv optimization file. For example, CMAINSTANCEDATA/opt_reserved_prod.csv.

-PBOUNDFILE

The full path of the server location for the opt_bound_prod.csv optimization file. For example,CMAINSTANCEDATA/opt_bound_prod.csv.

cmasmo.parms

-PTOP

The amount of TOP records you want to get. For example, 10.

-PSTART_DATE_DIS

The start date you want use for analyzing data on a distributed system. For example, "2015-01-01"

-PEND_DATE_DIS

The end date you want use for analyzing data on a distributed system. For example, "2015-12-31"

-PREFRESH

“True” or “false”. If you want to replace current data, then set it to true, otherwise set it to false.

-PBLOCKSIZE

You can decrease the number of times data is inserted into the database by increasing BLOCKSIZE, and the stream can run faster. However, larger BLOCKSIZE increases the duration of each insertion. BLOCKSIZE is measured in days. When you enter the BLOCKSIZE value, enter the number of days of data that you want to treat as a block. For example, 30.

-PSDATE

The start date that you want use for MIPS, CHANNEL, and DEVICE related data. For example, "2015-01-01"

-PEDATE

The start date that you want use for MIPS, CHANNEL, and DEVICE related data. For example, "2016-12-31"

cmapi.parms

-PANOMALY_RATIO

Defines the ratio that identifies abnormal transactions. A smaller ratio means that a smaller deviation identifies an anomaly, resulting in more anomalies being detected. A larger ratio means that a larger deviation is required to identify an anomaly, resulting in fewer anomalies being detected. The default value is 1.

-PMEMORY

The amount of memory on your modeler server machine.

-PCPU_RESPONSE_RATIO

Identifies what should be considered the abnormal ratio between response seconds and CPU seconds of a single transaction. If your normal CICS transactions have a small differential between response seconds and CPU seconds, the ratio you set should be small. When the differential between response seconds and CPU seconds is larger than the CPU_RESPONSE_RATIO you specify, the transaction is considered abnormal. The default value is 50.

-PSCORE_RATIO

Defines the ratio that the tree-based anomaly model uses to judge whether the transaction is abnormal or not. The default value is 0.5. The recommended range of this parameter value is [0.5,0.6].

-PTRANSACTIONFILE

The full path of server location where the transaction.csv file is located. For example, CMAINSTANCEDATA/transaction.csv.

-PDATEFILE

The full path of server location where the data_info.csv file is located. For example ,CMAINSTANCEDATA/date_info.csv.

cmacpf.parms

-PDATE_START

The start date you want to use for MIPS forecast. For example, “2015-01-01”

-PDATE_END

The start date you want to use for MIPS forecast. For example, “2015-12-31”

-PHOUR_START

The start hour of the observed data that you are using. For example, 0.

-PHOUR_END

The end hour of the observed data that you are using. For example, 23".

-PPEAKRANK

The Nth highest peak rank values. Valid values are in the range [1,5]. You can also input multiple rank values. For example, 1, 2, 3, and 4 represent highest peak, 2nd highest peak, 3rd highest peak, and 4th highest peak. For example, "1,2"

cmaaa.parms

-PDATE_START_APPL

The start date that you want use for capture ratio data. For example, 2015-01-01

-PDATE_END_APPL

The end date that you want use for capture ratio data. For example, 2016-01-01

-PTIME_START_APPL

The start time that you want use for capture ratio data. The default value is 00:00:00.

-PTIME_END_APPL

The end time that you want use for capture ratio data. The default value is 23:59:59.

-PREFRESH

“True” or “false”. If you want to replace current data, then set it to true, otherwise set it to false.

-PDATE_START

The start date of the observed data that you are using. For example, "2015-01-01".

-PDATE_END

The end date of the observed data that you are using. For example, "2015-06-30"

-PHOUR_START

The start hour of the observed data that you are using. For example, 0.

-PHOUR_END

The end hour of the observed data that you are using. For example, 23"

-PPEAKRANK

The Nth highest peak rank values. Valid values are in the range [1,5]. You can also input multiple rank values. For example, “1,2”

-PAPPFILE

The full path of server location where the appl_lob_z.csv file is located. For example, CMAINSTANCEDATA/appl_lob_z.csv.

-PAPPFILEDIS

The full path of server location where the appl_lob.csv file is located. For example CMAINSTANCEDATA/appl_lob.csv.

mlc_prod.csv

This file defines the licensing, pricing, and system information for each MLC product for which you want to view monetary values in the Software Cost Analysis reports and workspace. The monetary values are expressed in the currency that is defined for an enterprise in the currency.csv file.

CPU_SERIAL_NO is the last 4 characters of the machine serial number. The CPU serial numbers that are listed here must match with the serial numbers that are listed in the enterprise.csv file.

PID is an eight-character field that must follow the IBM format (XXXX-XXX) for a product ID.

LICENSE is a six-character field. It must be one of the acronyms that are listed here. To view details on each license, refer to IBM System z® Software Pricing (www.ibm.com/systems/z/resources/swprice/mlc/index.html).

AWLC – Advanced Workload License Charge

AEWLC – Advanced Entry Workload License Charge

EWLC – Entry Workload License Charge

VWLC – Variable Workload License Charge

MWLC – Midrange Workload License Charge

ZNALC – System z New Application Workload License Charge

TIER is a five-character field that specifies the tier/level for an associated LICENSE. To view details on the tiers/levels for each license, refer to IBM System z Software Pricing (www.ibm.com/systems/z/resources/swprice/mlc/index.html).

PRICE_PER_MSU is a decimal value that goes up to two decimal places.

ipla_prod.csv

This file defines the VUE, pricing, and system information for each IPLA product for which you want to view monetary values in the Software Cost Analysis reports and workspace. The entitlement for each IPLA is by CPU Serial Number, not the entire logical enterprise.

CPU_SERIAL_NO is the last 4 characters of the machine serial number. The CPU serial numbers that are listed here must match with the serial numbers that are listed in the enterprise.csv file.

PID is an eight-character field that must follow the IBM format (XXXX-XXX) for a product ID.

SS_PID is an eight character field that must follow the IBM format (XXXX-XXX) for the Subscription and Support product ID. This value can be left blank if Subscription and Support were not purchased for a product.

VUE is the six-character field for the Value Unit Exhibit (VUE) for a product. It must be one of the VUEs listed here. Refer toOverview of Value Unit Exhibits (www.ibm.com/systems/z/resources/swprice/zipla/vue.html) for more details.

VUE001

VUE007

VUE020

ASSIGNED_ENTITLED_MSU is a numeric field. The entitled MSUs are specifically for the corresponding CPU_SERIAL_NO, not the entire enterprise entitlement. Adding up each entitlement totals the enterprise entitlement.

PRICE_PER_VU is a decimal value that goes up to two decimal places.

ASSIGNED_SS_ENTITLED_MSU is a numeric field. The entitled MSUs are specifically for the corresponding CPU_SERIAL_NO, not the entire enterprise entitlement. This value can be left blank if Subscription and Support were not purchased for a product.

SS_PRICE_PER_MTH specifies the monthly price of Subscription and Support. It is a decimal value that goes up to two decimal places.

enterprise.csv

This file sets the CPU serial number for each enterprise.

ENTERPRISE is a 20-character enterprise name. The enterprise names in the enterprise.csv file must match with the names defined in the currency.csv file.

CPU_SERIAL_NO is the last 4 characters of the machine serial number. If a CPU serial number is missing in this table, then the CPU Serial No is missing in Software Cost Analysis Summary workspace.

currency.csv

This file defines the currency of each logically named enterprise. An enterprise is defined as a grouping of mainframe systems that all have the same currency.

ENTERPRISE is a 20-character enterprise name.

CURRENCY is the three character international symbol for the currency that is used to display monetary values for the associated ENTERPRISE in reports.

Note: The default decimal separator for values in these files is the period, which is the ISO format. If a non-ISO format is used, then the SPSS Modeler stream properties must be modified to use the same decimal.

gssp_scaling_factors.csv file:

PROD_ID: the IBM product ID registered on the System z machine.

GSSP_SCALING_FACTOR: the GSSP scaling factor for the product.

no89

Prepare the registration file of NO89 products. The file should follow the conventions of NO89 DD statements that are described in the topic "Modifying the NO89 DD statement" in the SCRT Users Guide (www.ibm.com/common/ssi/cgi-bin/ssialias?infotype=SA&subtype=WH&htmlfid=ZSL03022USEN). The definition section of the file should begin with //NO89 and end with //SYSLIN DD

For example:

//no89
	* LOTUS ACTIVE INSIGHT FOR Z/OS V6
	5655-U15=2827-BAA6:JB0,2817-4E15:JE0,2097-99FF:JC0
	* COBOL FOR OS/390 & VM V2
	5648-A25=CF3,CF2,J90,JB0
	* VA PL/I FOR OS/390 V2
	5655-B22=*NONE
	//SYSLIN  DD

holiday.csv

Date—The date of the holiday or the day on which the MIPS volume is notable.

Isholiday—Whether this date is a holiday. 1 indicates that the date is a holiday, 0 indicates that it is not.

D_name—The name of the holiday. Note that this column is for reference and is not required.

Ishigh—Whether the MIPS volume on a date is high over the data range. 1 indicates MIPS_USED is high for this date, 0 means MIPS_USED is not high for this date.

Islow—Whether the MIPS volume on a date is low below the data range. 1 indicates MIPS_USED is low for this date, 0 means MIPS_USED is not low for this date.

Note: Date format in holiday.csv should be ISO format (YYYY-MM-DD). If using a non-ISO format, the stream properties of forecast streams must be updated to match the date format in holiday.csv. For more information about modifying the holiday.csv file, see Time period definition.

Tip: If you use Microsoft Excel to edit csv files and your computer uses a different locale than what is used in the SPSS® Modeler streams, you must ensure that the data format in the csv remains consistent with date format that is used in the streams. Microsoft Excel can change the locale that is used in the file to match your computer's locale.

opt_care_prod.csv

This file is used to identify the cared products. If you do not need to identify any products as cared, do not enter anything in this file.

For any product that is considered a cared product, enter the product ID in the PROD_ID column.

For each cared product, enter 1 in the CARED column. If a PROD_ID is entered, but CARED is not set or is set to 0, this product is considered as not cared.

opt_bound_prod.csv

This file is used to identify bounded products. If you do not need to identify any products as bounded, do not enter anything in this file.

If two or more products need to be bounded, enter the product IDs individually in the PROD_ID column.

Enter the identical numeric identifier for the products to be bounded in the BOUND_ID column. The numeric identifier must be unique for each group of bounded products. For example, if product IDs 5635-A03 and 5655-R36 should be bounded, enter each product ID in separate cells in the PROD_ID column, and assign the numeric identifier 1 in the BOUND_ID column for each product.

Repeat steps 1 and 2 for each group of products that should be bounded.

opt_reserved_prod.csv

This file is used to identify reserved products. If you do not need to identify any products as reserved, do not enter anything in this file.

For each product to be reserved, enter the product ID in the PROD_ID column.

Enter the name of the LPAR reserved for the product in the LPAR_NAME column.

Enter the CPC number of the LPAR in the CPC_SERIAL_NO column.

Set the reserved flag in the RESERVED column.

Enter the reserved amount by MSU in the RESERVED_MSU column.

If RESERVED is 1 and RESERVED_MSU is n, the product is arranged in the specified LPAR with an amount equal or above n. If RESERVED is 1 and RESERVED_MSU is 0, the product is arranged in the specified LPAR with an amount above 0. If RESERVED is 1 and RESERVED_MSU is n, but n is beyond the MSU the product can produce, then the product is kept in its original LPAR after optimization. If the product is reserved in an LPAR that can not host it for any reason, then the reservation is ignored and the stream seeks any suitable LPAR for the product.

transaction.csv

Specify the transactions for which you want to perform anomaly detection. This file contains a single column, TRANSACTION_ID.

Note: You must enter the full value for the TRANSACTION_ID. You cannot use wildcard characters.

date_info.csv

This file contains three columns. Do not modify the value in the first column. In the second column, specify the date at which anomaly detection scoring should begin for the specified TRANSACTION_ID. Leave the third column value empty. The third column can optionally specify the end date for anomaly detection scoring, but Capacity Management Analytics generates scoring data for all dates after the specified start date.

For information on the following files, refer to Configuring input files for LPAR weight optimization.

lwo_parameters.csv

lwo_parameters_model.csv

lwo_parameters_date_list.csv

lwo_parameters_per_lpar.csv

For information on the following files, refer to Mapping table definitions for application analytics reporting.

appl_lob_z.csv

appl_lob.csv

Parent topic: Manually installing IBM Capacity Management Analytics 2.1.1

Manually running features for IBM Capacity Management Analytics 2.1.1featuresmanually runningPrior to running any IBM Capacity Management Analytics feature programs, ensure you have the following environment variables set in your profile:CMAINSTANCECMAHOMEPYTHONPATH=$CMAHOME/bin:$CMAHOME/lib:$PYTHONPATHCapacity Management Analytics feature programs help you to manage your features. For instance, they enable you to create instances, cleaning up instances, and uninstalling features. They also help perform all feature functions such as creating and removing databases, running SPSS streams, and running CPLEX optimization programs.After you install the Capacity Management Analytics Solution Kit, run cmahome.py and create your own cmainstance by running cmainstance.py.You can run the feature program to invoke other feature functions, for instance, cmabase.py --createdb –run to create a database. For more information, see each Capacity Management Analytics feature program.Before you begin invoking feature functionality to execute SPSS streams or the CPLEX optimization program, ensure you have the required input files in your CMAINSTANCE/data folder and provide the correct information.If you make any customized change to any file in the folder, update the file to read only.Summary of stream and input files by featureFeature name-modeStreamsRequired input filessmo -- etlchannel_top_etl.strdevice_top_etl.strdevice_lpar_agg_etl.strcmasmo.parmssmo -- refresh_rollchannel_top_etl_refresh_roll.strdevice_top_etl_refresh_roll.strdevice_lpar_agg_etl_refresh_roll.strcmasmo.parms smo – refresh_fullchannel_top_etl_refresh_full.strdevice_top_etl_refresh_full.strdevice_lpar_agg_etl_refresh_full.strcmasmo.parmssmo -- optimizationLwo.jarlwo_parameters.csvlwo_parameters_model.csvlwo_parameters_date_list.csvlwo_parameters_per_lpar.csvsca -- etlsca_subcap.strsca_tiers.strsca_no89.strcmasca.parmsgssp_scaling_factors.csvno89sca -- pricingsca_pricing.strcmasca.parmsmlc_prod.csvipla_prod.csventerprise.csvcurrency.csvsca -- forecastmsu_gssp_forecast_timeseries.strmsu_prod_forecast_timeseries.strmsu_lpar_forecast_timeseries.strcmasca.parmsholiday.csvsca -- optmizationprod_bill_msu_calculate.strprod_bill_msu_optimization.strcmasca.parmsopt_bound_prod.csvopt_care_prod.csvopt_reserved_prod.csvaa -- etlappl_lob_z.strappl_lob.strappl_capture_ratio.strappl_lob_z.csvappl_lob.csvcmaaa.parmsaa -- forecastappl_avg_daily_forecast_timeseries.strappl_avg_hourly_forecast_timeseries.strappl_peak_daily_forecast_timeseries.strcmaaa.parmsholiday.csvcpf -- forecastmvspm-lpar-hourly-forecast-timeseries.strmvspm-lpar-daily-forecast-timeseries.strmvspm-lpar-monthly-forecast-timeseries.strmvspm-lpar-daily-peak-forecast-timeseries.strmvspm-cpc-daily-peak-forecast-timeseries.strcmacpf.parmsholiday.csvpi -- anomaly anomaly_clean_up.stranomaly_detect_building_r.stranomaly_detect_scoring.stranomaly_detect_building_c.stranomaly_detect_scoring.stranomaly_detect_treebased.stranomaly_classification.strcmapi.parmsdate_info.csvtransaction.csvOptional. To run a sample database, CMASAMPLEDB, run cmasamledb.py.--instanceManage CMA sample database instance. If run is specified, invoke createdb and import. Import should be the same format as createdb. --createdbGenerate the sql and commands to create the CMA sample database --importGenerate the sql and commands to import data into the CMA sample database --runRun and execute the generated SQL and/or commands [--verbose]Verbose [--trace=TRACE]Trace Level To learn more about cmasampledb.py, run cmasampledb.py -h.To create the feature directory structure for this CMA instance, CMABASE, run cmabase.py.CMABASE is the prerequisite of CMASMO, CMAAA, CMASCA, CMAPI, and CMACPF detailed in the following steps. --instanceCreate feature directory structure for this CMA instance. If run is specified, invoke creatdb, defdata, setup, and etl --createdbGenerate the sql and commands to create the CMA data warehouse --defdataGenerate the commands to create the default data for this feature --setupGenerate the commands to set up Cognos: deploy images, import reports and update FM --etlGenerate the commands etl data warehouse data for this feature --refresh_rollRun BASE etl (refresh-roll) streams --refresh_fullRun BASE etl (refresh-full) streams --runRun and execute the generated SQL commands [--verbose]Verbose [--trace=TRACE]Trace Level To learn more about cmabase.py, run cmabase.py -h.Optional. To create a SMO instance, cmasmo, and to create directories and prepare files and data, run cmasmo.py.--instanceCreate SMO instance - create directories and prepare files and data. If --run is specified, invokeetl and lwo optimization. Lwo optimization should be the same format as etl. --etlRun SMO etl streams --optimization=OPTIMIZATIONOptimization model to run. lwo--Lpar Weight Optimization --refresh_rollRun SMO etl (refresh-roll) streams --refresh_fullRun SMO etl (refresh-full) streams --runRun and execute the generated commands [--plwo=PLWO]Parameter file for optimization. Default: CMAINSTANCE/data/lwo_parameters.csv [--verbose]Verbose [--trace=TRACE]Trace Level To learn more about cmasmo.py, run cmasmo.py -h.Optional. To create a SCA instance, cmasca, and to create directories and prepare files and data, run cmasca.py.--instanceCreate SCA instance: create directories and prepare files and data. If --run is specified, invoke etl, defdata, pricing, forecast and optimization --etlRun SCA etl streams --defdataGenerate SCA default data --pricingRun SCA pricing stream --forecastRun SCA forecast streams --optimizationRun SCA optimization streams --runRun and execute the generated commands [--verbose]Verbose [--trace=TRACE]Trace Level To learn more about cmasca.py, run cmasca.py -h.Optional. To create an AA instance, CMAAA, and to create directories and prepare files and data, run cmaaa.py.--instanceCreate AA instance: create directories and prepare files and data. If --run is specified, invoke etl and forecast --etlRun AA etl streams --forecastRun AA forecast streams --runRun and execute the generated commands [--verbose]Verbose [--trace=TRACE]Trace Level To learn more about cmaaa.py, run cmaaa.py -h.Optional. To create a CPF instance, cmacpf, and to create directories and prepare files and data, runcmacpf.py. --instanceCreate CPF instance: create directories and prepare files and data. If --run is specified, invoke forecast --forecastRun forecast streams --runRun and execute the generated commands [--verbose]Verbose [--trace=TRACE]Trace Level To learn more about cmacpf.py, run cmacpf.py -h.Optional. To create a PI instance, cmapi, and to create directories and prepare files and data, run cmapi.py.--instanceCreate PI instance: create directories and prepare files and data. If --run is specified, invoke anomaly --anomalyRun anomaly detection streams --runRun and execute the generated commands [--verbose]Verbose [--trace=TRACE]Trace Level To learn more about cmapi.py, run cmapi.py -h.

Manually running features for IBM Capacity Management Analytics 2.1.1

Before you begin

Prior to running any IBM® Capacity Management Analytics feature programs, ensure you have the following environment variables set in your profile:

CMAINSTANCE

CMAHOME

PYTHONPATH=$CMAHOME/bin:$CMAHOME/lib:$PYTHONPATH

About this task

Capacity Management Analytics feature programs help you to manage your features. For instance, they enable you to create instances, cleaning up instances, and uninstalling features. They also help perform all feature functions such as creating and removing databases, running SPSS streams, and running CPLEX optimization programs.

Procedure

After you install the Capacity Management Analytics Solution Kit, run cmahome.py and create your own cmainstance by running cmainstance.py.

Note: You can run the feature program to invoke other feature functions, for instance, cmabase.py --createdb –run to create a database. For more information, see each Capacity Management Analytics feature program.

Before you begin invoking feature functionality to execute SPSS streams or the CPLEX optimization program, ensure you have the required input files in your CMAINSTANCE/data folder and provide the correct information.

Note: If you make any customized change to any file in the folder, update the file to read only.

Table 14. Summary of stream and input files by feature

Feature name-mode
Streams
Required input files

smo -- etl
channel_top_etl.str

device_top_etl.str

device_lpar_agg_etl.str

cmasmo.parms

smo -- refresh_roll
channel_top_etl_refresh_roll.str

device_top_etl_refresh_roll.str

device_lpar_agg_etl_refresh_roll.str

cmasmo.parms

smo – refresh_full
channel_top_etl_refresh_full.str

device_top_etl_refresh_full.str

device_lpar_agg_etl_refresh_full.str

cmasmo.parms

smo -- optimization
Lwo.jar
lwo_parameters.csv

lwo_parameters_model.csv

lwo_parameters_date_list.csv

lwo_parameters_per_lpar.csv

sca -- etl
sca_subcap.str

sca_tiers.str

sca_no89.str

cmasca.parms

gssp_scaling_factors.csv

no89

sca -- pricing
sca_pricing.str
cmasca.parms

mlc_prod.csv

ipla_prod.csv

enterprise.csv

currency.csv

sca -- forecast
msu_gssp_forecast_timeseries.str

msu_prod_forecast_timeseries.str

msu_lpar_forecast_timeseries.str

cmasca.parms

holiday.csv

sca -- optmization
prod_bill_msu_calculate.str

prod_bill_msu_optimization.str

cmasca.parms

opt_bound_prod.csv

opt_care_prod.csv

opt_reserved_prod.csv

aa -- etl
appl_lob_z.str

appl_lob.str

appl_capture_ratio.str

appl_lob_z.csv

appl_lob.csv

cmaaa.parms

aa -- forecast
appl_avg_daily_forecast_timeseries.str

appl_avg_hourly_forecast_timeseries.str

appl_peak_daily_forecast_timeseries.str

cmaaa.parms

holiday.csv

cpf -- forecast
mvspm-lpar-hourly-forecast-timeseries.str

mvspm-lpar-daily-forecast-timeseries.str

mvspm-lpar-monthly-forecast-timeseries.str

mvspm-lpar-daily-peak-forecast-timeseries.str

mvspm-cpc-daily-peak-forecast-timeseries.str

cmacpf.parms

holiday.csv

pi -- anomaly
anomaly_clean_up.str

anomaly_detect_building_r.str

anomaly_detect_scoring.str

anomaly_detect_building_c.str

anomaly_detect_scoring.str

anomaly_detect_treebased.str

anomaly_classification.str

cmapi.parms

date_info.csv

transaction.csv

Optional. To run a sample database, CMASAMPLEDB, run cmasamledb.py.

--instance

Manage CMA sample database instance. If run is specified, invoke createdb and import. Import should be the same format as createdb.

--createdb

Generate the sql and commands to create the CMA sample database

--import

Generate the sql and commands to import data into the CMA sample database

--run

Run and execute the generated SQL and/or commands

[--verbose]

Verbose

[--trace=TRACE]

Trace Level

Note: To learn more about cmasampledb.py, run cmasampledb.py -h.

To create the feature directory structure for this CMA instance, CMABASE, run cmabase.py.

Note: CMABASE is the prerequisite of CMASMO, CMAAA, CMASCA, CMAPI, and CMACPF detailed in the following steps.

--instance

Create feature directory structure for this CMA instance. If run is specified, invoke creatdb, defdata, setup, and etl

--createdb

Generate the sql and commands to create the CMA data warehouse

--defdata

Generate the commands to create the default data for this feature

--setup

Generate the commands to set up Cognos: deploy images, import reports and update FM

--etl

Generate the commands etl data warehouse data for this feature

--refresh_roll

Run BASE etl (refresh-roll) streams

--refresh_full

Run BASE etl (refresh-full) streams

--run

Run and execute the generated SQL commands

[--verbose]

Verbose

[--trace=TRACE]

Trace Level

Note: To learn more about cmabase.py, run cmabase.py -h.

Optional. To create a SMO instance, cmasmo, and to create directories and prepare files and data, run cmasmo.py.

--instance

Create SMO instance - create directories and prepare files and data. If --run is specified, invokeetl and lwo optimization. Lwo optimization should be the same format as etl.

--etl

Run SMO etl streams

--optimization=OPTIMIZATION

Optimization model to run. lwo--Lpar Weight Optimization

--refresh_roll

Run SMO etl (refresh-roll) streams

--refresh_full

Run SMO etl (refresh-full) streams

--run

Run and execute the generated commands

[--plwo=PLWO]

Parameter file for optimization. Default: CMAINSTANCE/data/lwo_parameters.csv

[--verbose]

Verbose

[--trace=TRACE]

Trace Level

Note: To learn more about cmasmo.py, run cmasmo.py -h.

Optional. To create a SCA instance, cmasca, and to create directories and prepare files and data, run cmasca.py.

--instance

Create SCA instance: create directories and prepare files and data. If --run is specified, invoke etl, defdata, pricing, forecast and optimization

--etl

Run SCA etl streams

--defdata

Generate SCA default data

--pricing

Run SCA pricing stream

--forecast

Run SCA forecast streams

--optimization

Run SCA optimization streams

--run

Run and execute the generated commands

[--verbose]

Verbose

[--trace=TRACE]

Trace Level

Note: To learn more about cmasca.py, run cmasca.py -h.

Optional. To create an AA instance, CMAAA, and to create directories and prepare files and data, run cmaaa.py.

--instance

Create AA instance: create directories and prepare files and data. If --run is specified, invoke etl and forecast

--etl

Run AA etl streams

--forecast

Run AA forecast streams

--run

Run and execute the generated commands

[--verbose]

Verbose

[--trace=TRACE]

Trace Level

Note: To learn more about cmaaa.py, run cmaaa.py -h.

Optional. To create a CPF instance, cmacpf, and to create directories and prepare files and data, runcmacpf.py.

--instance

Create CPF instance: create directories and prepare files and data. If --run is specified, invoke forecast

--forecast

Run forecast streams

--run

Run and execute the generated commands

[--verbose]

Verbose

[--trace=TRACE]

Trace Level

Note: To learn more about cmacpf.py, run cmacpf.py -h.

Optional. To create a PI instance, cmapi, and to create directories and prepare files and data, run cmapi.py.

--instance

Create PI instance: create directories and prepare files and data. If --run is specified, invoke anomaly

--anomaly

Run anomaly detection streams

--run

Run and execute the generated commands

[--verbose]

Verbose

[--trace=TRACE]

Trace Level

Note: To learn more about cmapi.py, run cmapi.py -h.

Report, workspace, and folder customizationIBM Capacity Management Analytics lets you create custom reports, workspaces, and folders that satisfy your unique analytic requirements. You can modify the reports, workspaces, and folders by using IBM Cognos Business Intelligence tools.reportscustomizingWhen you create custom reports or workspaces, save to a location outside of the IBM Capacity Management Analytics Solution Kit folder. Saving to a location outside of the IBM Capacity Management Analytics Solution Kit folder ensures that your reports and workspaces are not overwritten when updates occur to Capacity Management Analytics. When you create a new folder, you can move any report, object, or template into the folder. If you cut and paste reports, but you want to keep the report shortcut in the ReportsAll folder in IBM Cognos Connection, then you should create a new shortcut in the Public FoldersIBM Capacity Management Analytics Solution KitReportsAll folder.Customizing a Capacity Management Analytics reportThere are four options to build a custom report with IBM Capacity Management Analytics, listed in order of preference:Customizing a Capacity Management Analytics workspaceIBM Capacity Management Analytics lets you create custom workspaces that satisfy your unique analytic requirement using standard IBM Cognos Business Intelligence authoring tools.

Report, workspace, and folder customization

IBM® Capacity Management Analytics lets you create custom reports, workspaces, and folders that satisfy your unique analytic requirements. You can modify the reports, workspaces, and folders by using IBM Cognos® Business Intelligence tools.

When you create custom reports or workspaces, save to a location outside of the IBM Capacity Management Analytics Solution Kit folder. Saving to a location outside of the IBM Capacity Management Analytics Solution Kit folder ensures that your reports and workspaces are not overwritten when updates occur to Capacity Management Analytics.

When you create a new folder, you can move any report, object, or template into the folder. If you cut and paste reports, but you want to keep the report shortcut in the Reports > All folder in IBM Cognos Connection, then you should create a new shortcut in the Public Folders > IBM Capacity Management Analytics Solution Kit > Reports > All folder.

Customizing a Capacity Management Analytics report
There are four options to build a custom report with IBM Capacity Management Analytics, listed in order of preference:

Customizing a Capacity Management Analytics workspace
IBM Capacity Management Analytics lets you create custom workspaces that satisfy your unique analytic requirement using standard IBM Cognos Business Intelligence authoring tools.

Customizing a Capacity Management Analytics reportThere are four options to build a custom report with IBM Capacity Management Analytics, listed in order of preference:reportscustomizingBuilding a custom report from an existing reportBuilding a custom report from a category templateBuilding a custom report from the base templateBuilding a custom report without a templateTo use any of the first three options, you must copy a report or template from the IBM Capacity Management Analytics Solution Kit folder to your own folder. For detailed instructions on copying reports, see IBM Knowledge Center Cognos Business Intelligence welcome page (http://www-01.ibm.com/support/knowledgecenter/SSEP7J/welcome).If you are using data from tables that are at different time intervals, you must normalize or roll up the lowest granularity to the highest before you can join the data. For example, Table 1 has an interval of 30 minutes and Table 2 has an interval of 60 minutes. Before you join the data, you must ensure that Table 1 aggregates two 30 minute intervals to equal one 60 minute interval so that it matches the rows in Table 2.There are several options for creating a custom workspace, as described in Customizing a Capacity Management Analytics workspaceIBM Capacity Management Analytics lets you create custom workspaces that satisfy your unique analytic requirement using standard IBM Cognos Business Intelligence authoring tools..Report, workspace, and folder customizationIBM Capacity Management Analytics lets you create custom reports, workspaces, and folders that satisfy your unique analytic requirements. You can modify the reports, workspaces, and folders by using IBM Cognos Business Intelligence tools.Building a custom report from an existing reportYou can edit an existing IBM Capacity Management Analytics report to create a custom report. For example, you can add a chart to an existing report.Building a custom report from a specific templateYou can use a specific template to build a new report in one of several categories. For example, the CPU Template is a specific template that holds commonly used prompts for a CPU metric based report, and you can use this template to build your own CPU report.Building a custom report from the base templateYou can use the Base template as a starting point to create a custom IBM Capacity Management Analytics report. Building a custom report without a templateYou can use IBM Cognos Report Studio to create a new custom IBM Capacity Management Analytics report that is not based on any other report or template.Customizing reports with Base and Prompt objectsIBM Cognos Capacity Management Analytics provides a number of Base and Prompt objects that you can use when creating a new report or modifying an existing report.

Customizing a Capacity Management Analytics report

There are four options to build a custom report with IBM® Capacity Management Analytics, listed in order of preference:

Building a custom report from an existing report

Building a custom report from a category template

Building a custom report from the base template

Building a custom report without a template

To use any of the first three options, you must copy a report or template from the IBM Capacity Management Analytics Solution Kit folder to your own folder. For detailed instructions on copying reports, see IBM Knowledge Center Cognos Business Intelligence welcome page (http://www-01.ibm.com/support/knowledgecenter/SSEP7J/welcome).

Important: If you are using data from tables that are at different time intervals, you must normalize or roll up the lowest granularity to the highest before you can join the data. For example, Table 1 has an interval of 30 minutes and Table 2 has an interval of 60 minutes. Before you join the data, you must ensure that Table 1 aggregates two 30 minute intervals to equal one 60 minute interval so that it matches the rows in Table 2.

There are several options for creating a custom workspace, as described in Customizing a Capacity Management Analytics workspace.

Building a custom report from an existing report
You can edit an existing IBM Capacity Management Analytics report to create a custom report. For example, you can add a chart to an existing report.

Building a custom report from a specific template
You can use a specific template to build a new report in one of several categories. For example, the CPU Template is a specific template that holds commonly used prompts for a CPU metric based report, and you can use this template to build your own CPU report.

Building a custom report from the base template
You can use the Base template as a starting point to create a custom IBM Capacity Management Analytics report.

Building a custom report without a template
You can use IBM Cognos Report Studio to create a new custom IBM Capacity Management Analytics report that is not based on any other report or template.

Customizing reports with Base and Prompt objects
IBM Cognos Capacity Management Analytics provides a number of Base and Prompt objects that you can use when creating a new report or modifying an existing report.

Parent topic: Report, workspace, and folder customization

Building a custom report from an existing reportYou can edit an existing IBM Capacity Management Analytics report to create a custom report. For example, you can add a chart to an existing report.reportscustomizing from existingIn IBM Cognos Connection, open the \Public Folders\IBM Capacity Management Analytics Solution Kit\Reports directory.Open the directory that contains the report that you want to customize.Do not copy from the \Public Folders\IBM Capacity Management Analytics Solution Kit\Reports\All directory. This directory contains report shortcuts, with each shortcut pointing to a Capacity Management Analytics report. Any modification of a shortcut automatically updates the source report. For more information about shortcuts, see the topic Create a shortcut in the IBM Cognos Connection User Guide.Copy the report to a location outside of the \Public Folders\IBM Capacity Management Analytics Solution Kit directory. Saving to a location outside of the \Public Folders\IBM Capacity Management Analytics Solution Kit directory ensures that your custom reports are not overwritten when updates occur to Capacity Management Analytics. Open the copied report in IBM Cognos Report Studio.In Cognos Report Studio, edit the report as necessary. For detailed instructions on editing reports, refer to the IBM Cognos Report Studio User Guide. You can also insert the Base or Prompt objects that are provided in the IBM Capacity Management Analytics Solution Kit to your report. For more information on Base and Prompt objects, see Customizing reports with Base and Prompt objectsIBM Cognos Capacity Management Analytics provides a number of Base and Prompt objects that you can use when creating a new report or modifying an existing report..In Cognos Report Studio, click File, Save As and save the report with a new name.Customizing a Capacity Management Analytics reportThere are four options to build a custom report with IBM Capacity Management Analytics, listed in order of preference:

Building a custom report from an existing report

You can edit an existing IBM® Capacity Management Analytics report to create a custom report. For example, you can add a chart to an existing report.

Procedure

In IBM Cognos® Connection, open the \Public Folders\IBM Capacity Management Analytics Solution Kit\Reports directory.

Open the directory that contains the report that you want to customize. Do not copy from the \Public Folders\IBM Capacity Management Analytics Solution Kit\Reports\All directory. This directory contains report shortcuts, with each shortcut pointing to a Capacity Management Analytics report. Any modification of a shortcut automatically updates the source report. For more information about shortcuts, see the topic "Create a shortcut" in the IBM Cognos Connection User Guide.

Copy the report to a location outside of the \Public Folders\IBM Capacity Management Analytics Solution Kit directory. Saving to a location outside of the \Public Folders\IBM Capacity Management Analytics Solution Kit directory ensures that your custom reports are not overwritten when updates occur to Capacity Management Analytics.

Open the copied report in IBM Cognos Report Studio.

In Cognos Report Studio, edit the report as necessary. For detailed instructions on editing reports, refer to the IBM Cognos Report Studio User Guide. You can also insert the Base or Prompt objects that are provided in the IBM Capacity Management Analytics Solution Kit to your report. For more information on Base and Prompt objects, see Customizing reports with Base and Prompt objects.

In Cognos Report Studio, click File, Save As and save the report with a new name.

Parent topic: Customizing a Capacity Management Analytics report

Building a custom report from a specific templateYou can use a specific template to build a new report in one of several categories. For example, the CPU Template is a specific template that holds commonly used prompts for a CPU metric based report, and you can use this template to build your own CPU report.reportscustomizing from specific templateIn IBM Cognos Connection, open the \Public Folders\IBM Capacity Management Analytics Solution Kit\Templates\Specific Templates directory.Copy a specific template to a location outside of the \Public Folders\IBM Capacity Management Analytics Solution Kit directory.Saving to a location outside of the \Public Folders\IBM Capacity Management Analytics Solution Kit directory ensures that your custom reports are not overwritten when updates occur to IBM Capacity Management Analytics. Open the copied template in IBM Cognos Report Studio.In Cognos Report Studio, edit the template as necessary. For detailed instructions on editing reports, refer to the IBM Cognos Report Studio User Guide. You can also insert the Base and Prompt objects that are provided in the Capacity Management Analytics Solution Kit to your report. For more information on Base and Prompt objects, see Customizing reports with Base and Prompt objectsIBM Cognos Capacity Management Analytics provides a number of Base and Prompt objects that you can use when creating a new report or modifying an existing report..In Cognos Report Studio, click File, Save As and save the report with a new name.Customizing a Capacity Management Analytics reportThere are four options to build a custom report with IBM Capacity Management Analytics, listed in order of preference:

Building a custom report from a specific template

You can use a specific template to build a new report in one of several categories. For example, the CPU Template is a specific template that holds commonly used prompts for a CPU metric based report, and you can use this template to build your own CPU report.

Procedure

In IBM Cognos® Connection, open the \Public Folders\IBM Capacity Management Analytics Solution Kit\Templates\Specific Templates directory.

Copy a specific template to a location outside of the \Public Folders\IBM Capacity Management Analytics Solution Kit directory. Saving to a location outside of the \Public Folders\IBM Capacity Management Analytics Solution Kit directory ensures that your custom reports are not overwritten when updates occur to IBM® Capacity Management Analytics.

Open the copied template in IBM Cognos Report Studio.

In Cognos Report Studio, edit the template as necessary. For detailed instructions on editing reports, refer to the IBM Cognos Report Studio User Guide. You can also insert the Base and Prompt objects that are provided in the Capacity Management Analytics Solution Kit to your report. For more information on Base and Prompt objects, see Customizing reports with Base and Prompt objects.

In Cognos Report Studio, click File, Save As and save the report with a new name.

Parent topic: Customizing a Capacity Management Analytics report

Building a custom report from the base templateYou can use the Base template as a starting point to create a custom IBM Capacity Management Analytics report. reportscustomizing from base templateIn IBM Cognos Connection, open the \Public Folders\IBM Capacity Management Analytics Solution Kit\Templates directory.Copy the Base template to a location outside of the \Public Folders\IBM Capacity Management Analytics Solution Kit directory.Saving to a location outside of the \Public Folders\IBM Capacity Management Analytics Solution Kit directory ensures that your custom reports are not overwritten when updates occur to Capacity Management Analytics. Open the copied Base template in IBM Cognos Report Studio.In Report Studio, edit the template as necessary. For detailed instructions on editing reports, refer to the IBM Cognos Report Studio User Guide. You can also insert the Base and Prompt objects that are provided in the Capacity Management Analytics Solution Kit to your report. For more information on Base and Prompt objects, see Customizing reports with Base and Prompt objectsIBM Cognos Capacity Management Analytics provides a number of Base and Prompt objects that you can use when creating a new report or modifying an existing report..In Report Studio, click File, Save As and save the report with a new name.Customizing a Capacity Management Analytics reportThere are four options to build a custom report with IBM Capacity Management Analytics, listed in order of preference:

Building a custom report from the base template

You can use the Base template as a starting point to create a custom IBM® Capacity Management Analytics report.

Procedure

In IBM Cognos® Connection, open the \Public Folders\IBM Capacity Management Analytics Solution Kit\Templates directory.

Copy the Base template to a location outside of the \Public Folders\IBM Capacity Management Analytics Solution Kit directory. Saving to a location outside of the \Public Folders\IBM Capacity Management Analytics Solution Kit directory ensures that your custom reports are not overwritten when updates occur to Capacity Management Analytics.

Open the copied Base template in IBM Cognos Report Studio.

In Report Studio, edit the template as necessary. For detailed instructions on editing reports, refer to the IBM Cognos Report Studio User Guide. You can also insert the Base and Prompt objects that are provided in the Capacity Management Analytics Solution Kit to your report. For more information on Base and Prompt objects, see Customizing reports with Base and Prompt objects.

In Report Studio, click File, Save As and save the report with a new name.

Parent topic: Customizing a Capacity Management Analytics report

Building a custom report without a templateYou can use IBM Cognos Report Studio to create a new custom IBM Capacity Management Analytics report that is not based on any other report or template.reportscreating newIn IBM Cognos Connection, open the directory where you want to save the new report. Click Launch, Report Studio.If the Select Package dialog box appears, navigate to the \Public Folders\IBM Capacity Management Analytics Solution Kit\Models directory in the List of all packages‬, then select a Capacity Management Analytics package for your report.Use the Cognos Report Studio features to create a report that is useful for your analysis. For detailed instructions on using Cognos Report Studio, refer to the IBM Cognos Report Studio User Guide. You can also insert the Base/Prompt objects that are provided in the IBM Capacity Management Analytics Solution Kit to your report. For more information on Base/Prompt objects, see Customizing reports with Base and Prompt objectsIBM Cognos Capacity Management Analytics provides a number of Base and Prompt objects that you can use when creating a new report or modifying an existing report..Click File, Save.Pre-built query data items DATE_BAND & TIME_BAND for the interval band You run hcm_udf_create_01.sql to create user-defined functions such as YEAR_BAND and TIME_BAND. These user-defined functions can be used in Capacity Management Analytics reporting, for query data items [DATE_BAND] and [TIME_BAND]. In a CMA pre-built report, query data item [DATE_BAND], invoking user-defined functions YEAR_BAND, QUARTER_BAND, MONTH_BAND and DATE_BAND, creates interval bands for yearly, quarterly, monthly, and daily level. The query data item [TIME_BAND], invoking user-defined function TIME_BAND, creates interval bands for hourly and minute level. These two data items, associated with Layout Component Reference common_prompt_set_2 or common_prompt_set_3, provide the user flexibility to report on different user-selected interval bands. Each Capacity Management Analytics pre-built report or template includes data items [DATE_BAND] and [TIME_BAND] in its main query. When you build your own report and want to enable reporting on different interval bands, you need to have these two data items in the main query as well. You can copy them from any Capacity Management Analytics pre-built report or template to your report.Each Capacity Management Analytics pre-built report or template also includes filters for [DATE] and [TIME], which can be used in conjunction with the [DATE_BAND] and [TIME_BAND] data items. You can copy them from any Capacity Management Analytics pre-built report or template to your report.Customizing a Capacity Management Analytics reportThere are four options to build a custom report with IBM Capacity Management Analytics, listed in order of preference:

Building a custom report without a template

You can use IBM® Cognos® Report Studio to create a new custom IBM Capacity Management Analytics report that is not based on any other report or template.

Procedure

In IBM Cognos Connection, open the directory where you want to save the new report.

Click Launch, Report Studio.

If the Select Package dialog box appears, navigate to the \Public Folders\IBM Capacity Management Analytics Solution Kit\Models directory in the List of all packages‬, then select a Capacity Management Analytics package for your report.

Use the Cognos Report Studio features to create a report that is useful for your analysis. For detailed instructions on using Cognos Report Studio, refer to the IBM Cognos Report Studio User Guide. You can also insert the Base/Prompt objects that are provided in the IBM Capacity Management Analytics Solution Kit to your report. For more information on Base/Prompt objects, see Customizing reports with Base and Prompt objects.

Click File, Save.

What to do next

Pre-built query data items DATE_BAND & TIME_BAND for the interval band

You run hcm_udf_create_01.sql to create user-defined functions such as YEAR_BAND and TIME_BAND. These user-defined functions can be used in Capacity Management Analytics reporting, for query data items [DATE_BAND] and [TIME_BAND].

In a CMA pre-built report, query data item [DATE_BAND], invoking user-defined functions YEAR_BAND, QUARTER_BAND, MONTH_BAND and DATE_BAND, creates interval bands for yearly, quarterly, monthly, and daily level. The query data item [TIME_BAND], invoking user-defined function TIME_BAND, creates interval bands for hourly and minute level.

These two data items, associated with Layout Component Reference common_prompt_set_2 or common_prompt_set_3, provide the user flexibility to report on different user-selected interval bands. Each Capacity Management Analytics pre-built report or template includes data items [DATE_BAND] and [TIME_BAND] in its main query. When you build your own report and want to enable reporting on different interval bands, you need to have these two data items in the main query as well. You can copy them from any Capacity Management Analytics pre-built report or template to your report.

Each Capacity Management Analytics pre-built report or template also includes filters for [DATE] and [TIME], which can be used in conjunction with the [DATE_BAND] and [TIME_BAND] data items. You can copy them from any Capacity Management Analytics pre-built report or template to your report.

Parent topic: Customizing a Capacity Management Analytics report

Customizing reports with Base and Prompt objectsIBM Cognos Capacity Management Analytics provides a number of Base and Prompt objects that you can use when creating a new report or modifying an existing report.Customizing a Capacity Management Analytics reportThere are four options to build a custom report with IBM Capacity Management Analytics, listed in order of preference:Inserting a Base object into a reportBase objects are common objects that can be used in any IBM Capacity Management Analytics report or template.Modifying a child object of a Base objectIf you insert a Base object that contains children into your report, you can modify the child objects as required. For example, if you insert the app_header1 object into your report, you can modify the text item in the title bar to display your report name.Inserting a Prompt object into a reportA Prompt object asks a user to provide parameter values before a report is run. You can insert a Prompt object into any IBM Capacity Management Analytics report

Customizing reports with Base and Prompt objects

IBM® Cognos® Capacity Management Analytics provides a number of Base and Prompt objects that you can use when creating a new report or modifying an existing report.

Inserting a Base object into a report
Base objects are common objects that can be used in any IBM Capacity Management Analytics report or template.

Modifying a child object of a Base object
If you insert a Base object that contains children into your report, you can modify the child objects as required. For example, if you insert the app_header1 object into your report, you can modify the text item in the title bar to display your report name.

Inserting a Prompt object into a report
A Prompt object asks a user to provide parameter values before a report is run. You can insert a Prompt object into any IBM Capacity Management Analytics report

Parent topic: Customizing a Capacity Management Analytics report

Inserting a Base object into a reportBase objects are common objects that can be used in any IBM Capacity Management Analytics report or template.reportsinserting a Base objectBase objectinserting into reportIn IBM Cognos Connection, open the \Public Folders\IBM Capacity Management Analytics Solution Kit\Templates\Objects directory.Run the Base report.Identify the object that you want to insert into your report.You can review the object properties, which appear in yellow, to help identify the object that you want. In IBM Cognos Report Studio, open the Capacity Management Analytics report into which you want to insert the Base object.From the Cognos Report Studio Toolbox tab, drag the Layout Component Reference object to the location in your report at which you want the Base object to appear.In the Component Location box, click Another report.Click the More button. Navigate to the \Public Folders\IBM Capacity Management Analytics Solution Kit\Templates\Objects directory and open the Base report.In the Available components to reference box, select the name of the object you want to insert into your report, then click OK.A copy of the Base object appears in your Capacity Management Analytics report at the location where you placed the Layout Component Reference object.Customizing reports with Base and Prompt objectsIBM Cognos Capacity Management Analytics provides a number of Base and Prompt objects that you can use when creating a new report or modifying an existing report.

Inserting a Base object into a report

Base objects are common objects that can be used in any IBM® Capacity Management Analytics report or template.

Procedure

In IBM Cognos® Connection, open the \Public Folders\IBM Capacity Management Analytics Solution Kit\Templates\Objects directory.

Run the Base report.

Identify the object that you want to insert into your report. You can review the object properties, which appear in yellow, to help identify the object that you want.

In IBM Cognos Report Studio, open the Capacity Management Analytics report into which you want to insert the Base object.

From the Cognos Report Studio Toolbox tab, drag the Layout Component Reference object to the location in your report at which you want the Base object to appear.

In the Component Location box, click Another report.

Click the More button.

Navigate to the \Public Folders\IBM Capacity Management Analytics Solution Kit\Templates\Objects directory and open the Base report.

In the Available components to reference box, select the name of the object you want to insert into your report, then click OK.

Results

A copy of the Base object appears in your Capacity Management Analytics report at the location where you placed the Layout Component Reference object.

Parent topic: Customizing reports with Base and Prompt objects

Modifying a child object of a Base objectIf you insert a Base object that contains children into your report, you can modify the child objects as required. For example, if you insert the app_header1 object into your report, you can modify the text item in the title bar to display your report name.Base objectmodifying child objectIn your report, select the copy of the parent object you created with the Layout Component Reference object.In the Properties pane, double-click the Overrides property.In the Overrides dialog box, select the child object you want to modify, then click OK.The child object is replaced by the text Drop item to override component child.Drag any object from the Toolbox list to replace the child object. Customizing reports with Base and Prompt objectsIBM Cognos Capacity Management Analytics provides a number of Base and Prompt objects that you can use when creating a new report or modifying an existing report.

Modifying a child object of a Base object

If you insert a Base object that contains children into your report, you can modify the child objects as required. For example, if you insert the app_header1 object into your report, you can modify the text item in the title bar to display your report name.

Procedure

In your report, select the copy of the parent object you created with the Layout Component Reference object.

In the Properties pane, double-click the Overrides property.

In the Overrides dialog box, select the child object you want to modify, then click OK. The child object is replaced by the text Drop item to override component child.

Drag any object from the Toolbox list to replace the child object.

Parent topic: Customizing reports with Base and Prompt objects

Inserting a Prompt object into a reportA Prompt object asks a user to provide parameter values before a report is run. You can insert a Prompt object into any IBM Capacity Management Analytics reportreportsinserting Prompt objectPrompt objectinserting into reportIn IBM Cognos Connection, open the \Public Folders\IBM Capacity Management Analytics Solution Kit\Templates\Objects directory.Open the Prompt report in IBM Cognos Report Studio.Identify the Prompt object that you want to insert into your report.You can review the object properties, which appear in yellow, to help identify the object that you want.The Prompt report is a multi-page report. Use Page Explorer to help you locate the Prompt object that you want to insert into your report. In Cognos Report Studio, open the Capacity Management Analytics report into which you want to insert the Prompt object.From the Cognos Report Studio Toolbox tab, drag the Layout Component Reference object to the location in your report at which you want the Prompt object to appear.In the Component Location box, click Another report.Click the More button. Navigate to the \Public Folders\IBM Capacity Management Analytics Solution Kit\Templates\Objects directory and open the Prompt report.In the Available components to reference box, select the name of the object you want to insert into your report, then click OK.In Cognos Report Studio, open the Query Explorer and copy the query that the identified Prompt object uses. The yellow document for each Prompt object provides the related information for the object, such as Object Name, Query, and Parameter.In the report with the Layout Component Reference object, open the Query Explorer and paste the query for the Prompt object.A copy of the Prompt object appears in your Capacity Management Analytics report at the location where you placed the Layout Component Reference object.Customizing reports with Base and Prompt objectsIBM Cognos Capacity Management Analytics provides a number of Base and Prompt objects that you can use when creating a new report or modifying an existing report.

Inserting a Prompt object into a report

A Prompt object asks a user to provide parameter values before a report is run. You can insert a Prompt object into any IBM® Capacity Management Analytics report

Procedure

In IBM Cognos® Connection, open the \Public Folders\IBM Capacity Management Analytics Solution Kit\Templates\Objects directory.

Open the Prompt report in IBM Cognos Report Studio.

Identify the Prompt object that you want to insert into your report. You can review the object properties, which appear in yellow, to help identify the object that you want.

Note: The Prompt report is a multi-page report. Use Page Explorer to help you locate the Prompt object that you want to insert into your report.

In Cognos Report Studio, open the Capacity Management Analytics report into which you want to insert the Prompt object.

From the Cognos Report Studio Toolbox tab, drag the Layout Component Reference object to the location in your report at which you want the Prompt object to appear.

In the Component Location box, click Another report.

Click the More button.

Navigate to the \Public Folders\IBM Capacity Management Analytics Solution Kit\Templates\Objects directory and open the Prompt report.

In the Available components to reference box, select the name of the object you want to insert into your report, then click OK.

In Cognos Report Studio, open the Query Explorer and copy the query that the identified Prompt object uses. The yellow document for each Prompt object provides the related information for the object, such as Object Name, Query, and Parameter.

In the report with the Layout Component Reference object, open the Query Explorer and paste the query for the Prompt object.

Results

A copy of the Prompt object appears in your Capacity Management Analytics report at the location where you placed the Layout Component Reference object.

Parent topic: Customizing reports with Base and Prompt objects

Customizing a Capacity Management Analytics workspaceIBM Capacity Management Analytics lets you create custom workspaces that satisfy your unique analytic requirement using standard IBM Cognos Business Intelligence authoring tools.workspacescustomizingWhen you create custom workspaces, save your workspaces to a location outside of the IBM Capacity Management Analytics Solution kit folder. Saving to a location outside of the IBM Capacity Management Analytics Solution Kit folder ensures that your workspaces are not overwritten when updates occur to Capacity Management Analytics.There are two options to build a custom workspace with IBM Capacity Management Analytics:Building a custom workspace from an existing workspaceYou can edit an existing IBM Capacity Management Analytics workspace to create a custom workspace. For example, you can add a chart to an existing workspace.Creating a new custom workspaceYou can use IBM Cognos Workspace to create a new custom IBM Capaticy Management Analytics workspace without modifying an existing workspace.Report, workspace, and folder customizationIBM Capacity Management Analytics lets you create custom reports, workspaces, and folders that satisfy your unique analytic requirements. You can modify the reports, workspaces, and folders by using IBM Cognos Business Intelligence tools.Building a custom workspace from an existing workspaceYou can edit an existing IBM Capacity Management Analytics workspace to create a custom workspace. For example, you can add a chart to an existing workspace.Creating a new custom workspaceYou can use IBM Cognos Workspace to create a new custom IBM Capaticy Management Analytics workspace without modifying an existing workspace.Customizing a workspace with widgetsIBM Capacity Management Analytics provides a number of pre-built widgets, which you can use when creating a new workspace or modifying an existing workspace.Creating your own Prompts & Titlebars reportA Prompts & Titlebars report contains prompts and titlebars which are used in your own workspace. You can insert Prompts & Titlebars object into any IBM Capacity Management Analytics workspace. Creating your own widgets report used in CanvasA widgets report used in Canvas contains charts and tables you want to show in the workspace. You can insert any chart or table widget into any IBM Capacity Management Analytics workspace.Customizing top N display for the zServer Monitoring DashboardMany tables and charts on the zServer Monitoring Dashboard are configured to display the top five or top ten items from a query. You can modify the widget associated with a given table or chart to return any top number (top N) of items.Customizing top N display for the Enterprise DashboardMany tables and charts on the Enterprise Dashboard are configured to display the top 10 items from a query. You can modify the widget that is associated with a given table or chart to return any top number (top N) of items.Customizing high usage and low usage thresholds for the Enterprise DashboardThe Percentage of Servers with High/Low Usage charts on the Enterprise Dashboard are configured with the default high usage threshold as 90% busy and the default low usage threshold as 10% busy. You can modify the widget that is associated with a given chart to use any high or low usage threshold.

Customizing a Capacity Management Analytics workspace

IBM® Capacity Management Analytics lets you create custom workspaces that satisfy your unique analytic requirement using standard IBM Cognos® Business Intelligence authoring tools.

When you create custom workspaces, save your workspaces to a location outside of the IBM Capacity Management Analytics Solution kit folder. Saving to a location outside of the IBM Capacity Management Analytics Solution Kit folder ensures that your workspaces are not overwritten when updates occur to Capacity Management Analytics.

There are two options to build a custom workspace with IBM Capacity Management Analytics:

Building a custom workspace from an existing workspace

Creating a new custom workspace

Building a custom workspace from an existing workspace
You can edit an existing IBM Capacity Management Analytics workspace to create a custom workspace. For example, you can add a chart to an existing workspace.

Creating a new custom workspace
You can use IBM Cognos Workspace to create a new custom IBM Capaticy Management Analytics workspace without modifying an existing workspace.

Customizing a workspace with widgets
IBM Capacity Management Analytics provides a number of pre-built widgets, which you can use when creating a new workspace or modifying an existing workspace.

Creating your own Prompts & Titlebars report
A Prompts & Titlebars report contains prompts and titlebars which are used in your own workspace. You can insert Prompts & Titlebars object into any IBM Capacity Management Analytics workspace.

Creating your own widgets report used in Canvas
A widgets report used in Canvas contains charts and tables you want to show in the workspace. You can insert any chart or table widget into any IBM Capacity Management Analytics workspace.

Customizing top N display for the zServer Monitoring Dashboard
Many tables and charts on the zServer Monitoring Dashboard are configured to display the top five or top ten items from a query. You can modify the widget associated with a given table or chart to return any top number (top N) of items.

Customizing top N display for the Enterprise Dashboard
Many tables and charts on the Enterprise Dashboard are configured to display the top 10 items from a query. You can modify the widget that is associated with a given table or chart to return any top number (top N) of items.

Customizing high usage and low usage thresholds for the Enterprise Dashboard
The Percentage of Servers with High/Low Usage charts on the Enterprise Dashboard are configured with the default high usage threshold as 90% busy and the default low usage threshold as 10% busy. You can modify the widget that is associated with a given chart to use any high or low usage threshold.

Parent topic: Report, workspace, and folder customization

Building a custom workspace from an existing workspaceYou can edit an existing IBM Capacity Management Analytics workspace to create a custom workspace. For example, you can add a chart to an existing workspace.workspacesbuilding new from existingIn IBM Cognos Connection, open the \Public Folders\IBM Capacity Management Analytics Solution Kit\Workspaces directory.Copy the workspace you want to customize to a location outside of the \Public Folders\IBM Capacity Management Analytics Solution Kit directory. Saving to a location outside of the \Public Folders\IBM Capacity Management Analytics Solution Kit directory ensures that your custom workspaces are not overwritten when updates occur to Capacity Management Analytics.Open the copied workspace in IBM Cognos Workspace.In IBM Cognos Workspace, edit the workspace as necessary. For detailed instructions on editing workspaces, refer to the IBM Cognos Workspace User Guide, available on the IBM Cognos Business Intelligence welcome page of IBM Knowledge Center. You can also insert the workspace objects that are provided with the IBM Capacity Management Analytics Solution Kit into your workspace. For more information on workspace objects, see Customizing a workspace with widgetsIBM Capacity Management Analytics provides a number of pre-built widgets, which you can use when creating a new workspace or modifying an existing workspace..In Cognos Workspace, click Save As on the application bar and save the workspace with a new name.Customizing a Capacity Management Analytics workspaceIBM Capacity Management Analytics lets you create custom workspaces that satisfy your unique analytic requirement using standard IBM Cognos Business Intelligence authoring tools.

Building a custom workspace from an existing workspace

You can edit an existing IBM® Capacity Management Analytics workspace to create a custom workspace. For example, you can add a chart to an existing workspace.

Procedure

In IBM Cognos® Connection, open the \Public Folders\IBM Capacity Management Analytics Solution Kit\Workspaces directory.

Copy the workspace you want to customize to a location outside of the \Public Folders\IBM Capacity Management Analytics Solution Kit directory. Saving to a location outside of the \Public Folders\IBM Capacity Management Analytics Solution Kit directory ensures that your custom workspaces are not overwritten when updates occur to Capacity Management Analytics.

Open the copied workspace in IBM Cognos Workspace.

In IBM Cognos Workspace, edit the workspace as necessary.

For detailed instructions on editing workspaces, refer to the IBM Cognos Workspace User Guide, available on the IBM Cognos Business Intelligence welcome page of IBM Knowledge Center.

You can also insert the workspace objects that are provided with the IBM Capacity Management Analytics Solution Kit into your workspace. For more information on workspace objects, see Customizing a workspace with widgets.

In Cognos Workspace, click Save As on the application bar and save the workspace with a new name.

Parent topic: Customizing a Capacity Management Analytics workspace

Creating a new custom workspace You can use IBM Cognos Workspace to create a new custom IBM Capaticy Management Analytics workspace without modifying an existing workspace.workspacescreating newIn IBM Cognos Connection, open the directory where you want to save the new workspace.Click Launch > Cognos Workspace.Click the Create New icon.Use the Cognos Workspace features to create a workspace that is useful for your analysis. For detailed instructions on using workspaces, refer to the IBM Cognos Workspace User Guide, available on the IBM Cognos Business Intelligence welcome page of IBM Knowledge Center.You can also insert the workspace objects that are provided with the IBM Capacity Management Analytics Solution Kit into your workspace. For more information on workspace objects, see Customizing a workspace with widgetsIBM Capacity Management Analytics provides a number of pre-built widgets, which you can use when creating a new workspace or modifying an existing workspace..Click Save on the application bar.Customizing a Capacity Management Analytics workspaceIBM Capacity Management Analytics lets you create custom workspaces that satisfy your unique analytic requirement using standard IBM Cognos Business Intelligence authoring tools.

Creating a new custom workspace

You can use IBM® Cognos® Workspace to create a new custom IBM Capaticy Management Analytics workspace without modifying an existing workspace.

Procedure

In IBM Cognos Connection, open the directory where you want to save the new workspace.

Click Launch > Cognos Workspace.

Click the Create New icon.

Use the Cognos Workspace features to create a workspace that is useful for your analysis.

For detailed instructions on using workspaces, refer to the IBM Cognos Workspace User Guide, available on the IBM Cognos Business Intelligence welcome page of IBM Knowledge Center.

You can also insert the workspace objects that are provided with the IBM Capacity Management Analytics Solution Kit into your workspace. For more information on workspace objects, see Customizing a workspace with widgets.

Click Save on the application bar.

Parent topic: Customizing a Capacity Management Analytics workspace

Customizing a workspace with widgetsIBM Capacity Management Analytics provides a number of pre-built widgets, which you can use when creating a new workspace or modifying an existing workspace.workspacescustomizing with pre-built widgetsReview IBM Capacity Management Analytics Workspace pre-built widgetsThe following widgets are available for inclusion in a new or existing workspace. for a list of available pre-built widgets.In the Workspace Content pane, navigate to \Public Folders\IBM Capacity Management Analytics Solution Kit\Workspaces\Objects\Prompts & Titlebars directory. Open an existing Prompts & Titlebars report, and then drag the Prompt page to the canvas, or to the global area for a workspace with multiple tabs. For more information on Prompts & Titlebars objects, see Creating your own Prompts & Titlebars reportA Prompts & Titlebars report contains prompts and titlebars which are used in your own workspace. You can insert Prompts & Titlebars object into any IBM Capacity Management Analytics workspace. .You can use the keyboard shortcut Ctrl+Shift+Enter to add a selected widget, or right-click the widget in the Content pane and select Insert in Current Tab.In the Workspace Content pane, navigate to \Public Folders\IBM Capacity Management Analytics Solution Kit\Workspaces\Objects\Common directory.Open the Titlebar Template report. Drag the Titlebar page from the Content pane and edit the titlebar as required for your purposes. For details on editing the titlebar text, see Editing titlebars in Cognos Workspace AdvancedThe Titlebar Template is a base template you can use in your workspace. You can edit this template to satisfy your particular requirements in Cognos Workspace Advanced..Some titlebars also incorporate drill-through capabilities. For more information on editing such tilebars, see Creating your own Prompts & Titlebars reportA Prompts & Titlebars report contains prompts and titlebars which are used in your own workspace. You can insert Prompts & Titlebars object into any IBM Capacity Management Analytics workspace. .Perform a search for the widget in the Workspace Content pane. For more information about workspace search, see Performing a search in the IBM Cognos Workspace User Guide.Click the Show Information Card icon to enable displaying high-level information about workspace content such as type, owner and description.Drag a report part (chart or table) from the Result pane to the canvas. For more information on widgets used in Canvas, see Creating your own widgets report used in CanvasA widgets report used in Canvas contains charts and tables you want to show in the workspace. You can insert any chart or table widget into any IBM Capacity Management Analytics workspace..To align widgets, drag one widget towards another widget until a dotted line appears, and then drop the widget.To remove a widget from the workspace, click the Widget Actions icon, then click Remove from Workspace.Click the Save button on the Application Bar to save the workspace.Customizing a Capacity Management Analytics workspaceIBM Capacity Management Analytics lets you create custom workspaces that satisfy your unique analytic requirement using standard IBM Cognos Business Intelligence authoring tools.Editing titlebars in Cognos Workspace AdvancedThe Titlebar Template is a base template you can use in your workspace. You can edit this template to satisfy your particular requirements in Cognos Workspace Advanced.IBM Capacity Management Analytics Workspace pre-built widgetsThe following widgets are available for inclusion in a new or existing workspace.

Customizing a workspace with widgets

IBM® Capacity Management Analytics provides a number of pre-built widgets, which you can use when creating a new workspace or modifying an existing workspace.

About this task

Review IBM Capacity Management Analytics Workspace pre-built widgets for a list of available pre-built widgets.

Procedure

In the Workspace Content pane, navigate to \Public Folders\IBM Capacity Management Analytics Solution Kit\Workspaces\Objects\Prompts & Titlebars directory.

Open an existing Prompts & Titlebars report, and then drag the Prompt page to the canvas, or to the global area for a workspace with multiple tabs. For more information on Prompts & Titlebars objects, see Creating your own Prompts & Titlebars report.

Note: You can use the keyboard shortcut Ctrl+Shift+Enter to add a selected widget, or right-click the widget in the Content pane and select Insert in Current Tab.

In the Workspace Content pane, navigate to \Public Folders\IBM Capacity Management Analytics Solution Kit\Workspaces\Objects\Common directory.

Open the Titlebar Template report.

Drag the Titlebar page from the Content pane and edit the titlebar as required for your purposes. For details on editing the titlebar text, see Editing titlebars in Cognos Workspace Advanced. Some titlebars also incorporate drill-through capabilities. For more information on editing such tilebars, see Creating your own Prompts & Titlebars report.

Perform a search for the widget in the Workspace Content pane.

For more information about workspace search, see "Performing a search" in the IBM Cognos Workspace User Guide.

Click the Show Information Card icon to enable displaying high-level information about workspace content such as type, owner and description.

Drag a report part (chart or table) from the Result pane to the canvas. For more information on widgets used in Canvas, see Creating your own widgets report used in Canvas.

To align widgets, drag one widget towards another widget until a dotted line appears, and then drop the widget.

To remove a widget from the workspace, click the Widget Actions icon, then click Remove from Workspace.

Click the Save button on the Application Bar to save the workspace.

Editing titlebars in Cognos Workspace Advanced
The Titlebar Template is a base template you can use in your workspace. You can edit this template to satisfy your particular requirements in Cognos Workspace Advanced.

IBM Capacity Management Analytics Workspace pre-built widgets
The following widgets are available for inclusion in a new or existing workspace.

Parent topic: Customizing a Capacity Management Analytics workspace

Editing titlebars in Cognos Workspace AdvancedThe Titlebar Template is a base template you can use in your workspace. You can edit this template to satisfy your particular requirements in Cognos Workspace Advanced.Open your workspace.Click the Actions icon for the titlebar widget, then click Do More.The titlebar widget opens in Cognos Workspace Advanced.Edit the titlebar according to your needs.For detailed instructions on using workspace advanced, refer to the IBM Cognos Workspace Advanced User Guide, available on the IBM Cognos Business Intelligence welcome page of IBM Knowledge Center.Click Done on the tool bar to save your changes and return to your workspaceCustomizing a workspace with widgetsIBM Capacity Management Analytics provides a number of pre-built widgets, which you can use when creating a new workspace or modifying an existing workspace.

Editing titlebars in Cognos Workspace Advanced

The Titlebar Template is a base template you can use in your workspace. You can edit this template to satisfy your particular requirements in Cognos® Workspace Advanced.

Procedure

Open your workspace.

Click the Actions icon for the titlebar widget, then click Do More. The titlebar widget opens in Cognos Workspace Advanced.

Edit the titlebar according to your needs.

For detailed instructions on using workspace advanced, refer to the IBM Cognos Workspace Advanced User Guide, available on the IBM Cognos Business Intelligence welcome page of IBM Knowledge Center.

Click Done on the tool bar to save your changes and return to your workspace

Parent topic: Customizing a workspace with widgets

IBM Capacity Management Analytics Workspace pre-built widgetsThe following widgets are available for inclusion in a new or existing workspace.workspacespre-built widgetsCapacity Management Analytics Workspace pre-built widgetsObject TypeObject NameLocationChartAs if all on zIIP ChartPublic Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > CPU > zIIP/zAAP What-ifs – LPAR LevelChartAs if all on zIIP Engines ChartPublic Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > CPU > zIIP/zAAP What-ifs – LPAR LevelChartAs if all on zIIP/zAAP ChartPublic Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > CPU > zIIP/zAAP What-ifs – LPAR LevelChartAs if all on zIIP/zAAP Engines ChartPublic Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > CPU > zIIP/zAAP What-ifs – LPAR LevelChartDASD I/O Response Time Components & I/O Rate ChartPublic Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > I/O > DASD I/O PerformanceChartLatent Demand ChartPublic Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > CPU > Latent DemandChartLCU I/O Queuing Delay Time - Top N at Machine Level ChartPublic Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > I/O > DASD I/O PerformanceChartMemory Usage ChartPublic Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > Memory > Memory UsageChartMIPS Used at LPAR Level ChartPublic Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > CPU > MIPS Used – LPAR LevelChartObserved Engines ChartPublic Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > CPU > zIIP/zAAP What-ifs – LPAR LevelChartObserved MIPS ChartPublic Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > CPU > zIIP/zAAP What-ifs – LPAR LevelChartPercentage of Servers with High Usage ChartPublic Folders > IBM Capacity Management Analytics Solution Kit > Reports > CPU > Percentage of Servers with High/Low UsageChartPercentage of Servers with Low Usage ChartPublic Folders > IBM Capacity Management Analytics Solution Kit > Reports > CPU > Percentage of Servers with High/Low UsageChartServers reporting by Operating System ChartPublic Folders > IBM Capacity Management Analytics Solution Kit > Reports > CPU > Servers Reporting by Operating System - Enterprise LevelChartTop N DASD I/O Channel Path Utilizations ChartPublic Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > I/O > Channel UtilizationChartTop N Max TCP/IP Server Ports Connections ChartPublic Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > I/O > TCP/IP Server Ports Connection StatisticsPageFOR MACHINE LEVEL SUMMARYPublic Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > Software Cost Analysis > Software Cost Analysis SummaryPageFORE PRODUCT SUMMARYPublic Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > Software Cost Analysis > Software Cost Analysis SummaryPageOBS MACHINE LEVEL SUMMARYPublic Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > Software Cost Analysis > Software Cost Analysis SummaryPageOBS PRODUCT SUMMARYPublic Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > Software Cost Analysis > Software Cost Analysis SummaryPageOPT MACHINE LEVEL SUMMARYPublic Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > Software Cost Analysis > Software Cost Analysis SummaryPageOPT PRODUCT SUMMARYPublic Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > Software Cost Analysis > Software Cost Analysis SummaryPageOPT SUGGESTED ACTIONPublic Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > Software Cost Analysis > Software Cost Analysis SummaryTableAIX - 10 Highest CPU Usage Peaks TablePublic Folders > IBM Capacity Management Analytics Solution Kit > Reports > CPU > AIX - 10 Highest/Lowest CPU Usage PeaksTableAIX - 10 Lowest CPU Usage Peaks TablePublic Folders > IBM Capacity Management Analytics Solution Kit > Reports > CPU > AIX - 10 Highest/Lowest CPU Usage PeaksTableDASD I/O Channel Utilization - Top N at Machine Level TablePublic Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > I/O > Channel UtilizationTableDASD I/O Queuing Delay Intensity - Top N at Machine Level TablePublic Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > I/O > DASD I/O PerformanceTableLinux for System X –10 Lowest CPU Usage Peaks TablePublic Folders > IBM Capacity Management Analytics Solution Kit > Reports > CPU > Linux for System X - 10 Highest/Lowest CPU Usage PeaksTableLinux for System X –10 Highest CPU Usage Peaks TablePublic Folders > IBM Capacity Management Analytics Solution Kit > Reports > CPU > Linux for System X - 10 Highest/Lowest CPU Usage PeaksTableLinux for System Z –10 Lowest CPU Usage Peaks TablePublic Folders > IBM Capacity Management Analytics Solution Kit > Reports > CPU > Linux for System Z - 10 Highest/Lowest CPU Usage PeaksTableLinux for System Z –10 Highest CPU Usage Peaks TablePublic Folders > IBM Capacity Management Analytics Solution Kit > Reports > CPU > Linux for System Z - 10 Highest/Lowest CPU Usage PeaksTableLPAR Configuration Metrics TablePublic Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > CPU > MIPS Used – Machine LevelTableMIPS Used at Machine Level TablePublic Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > CPU > MIPS Used – Machine LevelTableSCA Summary - NO89 Products Matrix TablePublic Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > Software Cost Analysis > NO89 Products MatrixTableWindows - 10 Highest CPU Usage Peaks TablePublic Folders > IBM Capacity Management Analytics Solution Kit > Reports > CPU > Windows - 10 Highest/Lowest CPU Usage PeaksTableWindows - 10 Lowest CPU Usage Peaks TablePublic Folders > IBM Capacity Management Analytics Solution Kit > Reports > CPU > Windows - 10 Highest/Lowest CPU Usage PeaksTablezIIP zAAP What-ifs - LPAR Level TablePublic Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > CPU > zIIP/zAAP What-ifs – LPAR LevelTablez/OS - 10 Lowest CPU Usage Peaks TablePublic Folders > IBM Capacity Management Analytics Solution Kit > Reports > CPU > z/OS - 10 Highest/Lowest CPU Usage PeaksTablez/OS - 10 Highest CPU Usage Peaks TablePublic Folders > IBM Capacity Management Analytics Solution Kit > Reports > CPU > z/OS - 10 Highest/Lowest CPU Usage PeaksCommon ObjectsTitlebar TemplatePublic Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Common > Titlebar TemplateCommon ObjectsDate & Time Prompt GroupPublic Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Common > Date & Time Prompt GroupCommon ObjectsLicensePublic Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Common > LicenseSpecific Prompt Group and TitlebarSoftware Cost Analysis Prompt & Titlebar PromptsTitlebarPublic Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Prompts & Titlebars > Software Cost Analysis Prompt & TitlebarSpecific Prompt Group and TitlebarzIIP/zAAP What-ifs - LPAR Level Prompts & TitlebarsPromptsTitlebarPublic Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Prompts & TitlebarsSpecific Prompt Group and TitlebarDistributed Prompt & TitlebarPromptsTitlebarPublic Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Prompts & Titlebars > Distributed Prompt & TitlebarSpecific Prompt Group and TitlebarzServer Monitoring Dashboard Prompts & TitlebarsPromptsMachine Level TitlebarCPU & Storage TitlebarI/O & Network TitlebarPublic Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Prompts & TitlebarsCustomizing a workspace with widgetsIBM Capacity Management Analytics provides a number of pre-built widgets, which you can use when creating a new workspace or modifying an existing workspace.

IBM Capacity Management Analytics Workspace pre-built widgets

The following widgets are available for inclusion in a new or existing workspace.

Table 15. Capacity Management Analytics Workspace pre-built widgets

Object Type
Object Name
Location

Chart
As if all on zIIP Chart
Public Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > CPU > zIIP/zAAP What-ifs – LPAR Level

Chart
As if all on zIIP Engines Chart
Public Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > CPU > zIIP/zAAP What-ifs – LPAR Level

Chart
As if all on zIIP/zAAP Chart
Public Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > CPU > zIIP/zAAP What-ifs – LPAR Level

Chart
As if all on zIIP/zAAP Engines Chart
Public Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > CPU > zIIP/zAAP What-ifs – LPAR Level

Chart
DASD I/O Response Time Components & I/O Rate Chart
Public Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > I/O > DASD I/O Performance

Chart
Latent Demand Chart
Public Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > CPU > Latent Demand

Chart
LCU I/O Queuing Delay Time - Top N at Machine Level Chart
Public Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > I/O > DASD I/O Performance

Chart
Memory Usage Chart
Public Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > Memory > Memory Usage

Chart
MIPS Used at LPAR Level Chart
Public Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > CPU > MIPS Used – LPAR Level

Chart
Observed Engines Chart
Public Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > CPU > zIIP/zAAP What-ifs – LPAR Level

Chart
Observed MIPS Chart
Public Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > CPU > zIIP/zAAP What-ifs – LPAR Level

Chart
Percentage of Servers with High Usage Chart
Public Folders > IBM Capacity Management Analytics Solution Kit > Reports > CPU > Percentage of Servers with High/Low Usage

Chart
Percentage of Servers with Low Usage Chart
Public Folders > IBM Capacity Management Analytics Solution Kit > Reports > CPU > Percentage of Servers with High/Low Usage

Chart
Servers reporting by Operating System Chart
Public Folders > IBM Capacity Management Analytics Solution Kit > Reports > CPU > Servers Reporting by Operating System - Enterprise Level

Chart
Top N DASD I/O Channel Path Utilizations Chart
Public Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > I/O > Channel Utilization

Chart
Top N Max TCP/IP Server Ports Connections Chart
Public Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > I/O > TCP/IP Server Ports Connection Statistics

Page
FOR MACHINE LEVEL SUMMARY
Public Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > Software Cost Analysis > Software Cost Analysis Summary

Page
FORE PRODUCT SUMMARY
Public Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > Software Cost Analysis > Software Cost Analysis Summary

Page
OBS MACHINE LEVEL SUMMARY
Public Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > Software Cost Analysis > Software Cost Analysis Summary

Page
OBS PRODUCT SUMMARY
Public Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > Software Cost Analysis > Software Cost Analysis Summary

Page
OPT MACHINE LEVEL SUMMARY
Public Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > Software Cost Analysis > Software Cost Analysis Summary

Page
OPT PRODUCT SUMMARY
Public Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > Software Cost Analysis > Software Cost Analysis Summary

Page
OPT SUGGESTED ACTION
Public Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > Software Cost Analysis > Software Cost Analysis Summary

Table
AIX - 10 Highest CPU Usage Peaks Table
Public Folders > IBM Capacity Management Analytics Solution Kit > Reports > CPU > AIX - 10 Highest/Lowest CPU Usage Peaks

Table
AIX - 10 Lowest CPU Usage Peaks Table
Public Folders > IBM Capacity Management Analytics Solution Kit > Reports > CPU > AIX - 10 Highest/Lowest CPU Usage Peaks

Table
DASD I/O Channel Utilization - Top N at Machine Level Table
Public Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > I/O > Channel Utilization

Table
DASD I/O Queuing Delay Intensity - Top N at Machine Level Table
Public Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > I/O > DASD I/O Performance

Table
Linux for System X –10 Lowest CPU Usage Peaks Table
Public Folders > IBM Capacity Management Analytics Solution Kit > Reports > CPU > Linux for System X - 10 Highest/Lowest CPU Usage Peaks

Table
Linux for System X –10 Highest CPU Usage Peaks Table
Public Folders > IBM Capacity Management Analytics Solution Kit > Reports > CPU > Linux for System X - 10 Highest/Lowest CPU Usage Peaks

Table
Linux for System Z –10 Lowest CPU Usage Peaks Table
Public Folders > IBM Capacity Management Analytics Solution Kit > Reports > CPU > Linux for System Z - 10 Highest/Lowest CPU Usage Peaks

Table
Linux for System Z –10 Highest CPU Usage Peaks Table
Public Folders > IBM Capacity Management Analytics Solution Kit > Reports > CPU > Linux for System Z - 10 Highest/Lowest CPU Usage Peaks

Table
LPAR Configuration Metrics Table
Public Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > CPU > MIPS Used – Machine Level

Table
MIPS Used at Machine Level Table
Public Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > CPU > MIPS Used – Machine Level

Table
SCA Summary - NO89 Products Matrix Table
Public Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > Software Cost Analysis > NO89 Products Matrix

Table
Windows - 10 Highest CPU Usage Peaks Table
Public Folders > IBM Capacity Management Analytics Solution Kit > Reports > CPU > Windows - 10 Highest/Lowest CPU Usage Peaks

Table
Windows - 10 Lowest CPU Usage Peaks Table
Public Folders > IBM Capacity Management Analytics Solution Kit > Reports > CPU > Windows - 10 Highest/Lowest CPU Usage Peaks

Table
zIIP zAAP What-ifs - LPAR Level Table
Public Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > CPU > zIIP/zAAP What-ifs – LPAR Level

Table
z/OS - 10 Lowest CPU Usage Peaks Table
Public Folders > IBM Capacity Management Analytics Solution Kit > Reports > CPU > z/OS - 10 Highest/Lowest CPU Usage Peaks

Table
z/OS - 10 Highest CPU Usage Peaks Table
Public Folders > IBM Capacity Management Analytics Solution Kit > Reports > CPU > z/OS - 10 Highest/Lowest CPU Usage Peaks

Common Objects
Titlebar Template
Public Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Common > Titlebar Template

Common Objects
Date & Time Prompt Group
Public Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Common > Date & Time Prompt Group

Common Objects
License
Public Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Common > License

Specific Prompt Group and Titlebar
Software Cost Analysis Prompt & Titlebar

Prompts

Titlebar

Public Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Prompts & Titlebars > Software Cost Analysis Prompt & Titlebar

Specific Prompt Group and Titlebar
zIIP/zAAP What-ifs - LPAR Level Prompts & Titlebars

Prompts

Titlebar

Public Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Prompts & Titlebars

Specific Prompt Group and Titlebar
Distributed Prompt & Titlebar

Prompts

Titlebar

Public Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Prompts & Titlebars > Distributed Prompt & Titlebar

Specific Prompt Group and Titlebar
zServer Monitoring Dashboard Prompts & Titlebars

Prompts

Machine Level Titlebar

CPU & Storage Titlebar

I/O & Network Titlebar

Public Folders > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Prompts & Titlebars

Parent topic: Customizing a workspace with widgets

Creating your own Prompts & Titlebars reportA Prompts & Titlebars report contains prompts and titlebars which are used in your own workspace. You can insert Prompts & Titlebars object into any IBM Capacity Management Analytics workspace. workspacesPrompts & Titlebars reportIn IBM Cognos Connection, open the \Public Folders\IBM Capacity Management Analytics Solution Kit\Workspaces\Objects\Prompts & Titlebars directory.Create your own Prompts & Titlebars report and name the report as appropriate according to your workspace usage. You can create your own Prompts & Titlebars report from either one of the Prompts & Titlebars reports that Capacity Management Analytics provides. You can also create Prompts & Titlebars report using the common objects Capacity Management Analytics provides. The common objects that you can use to customize your report are located in \Public Folders\IBM Capacity Management Analytics Solution Kit\Workspaces\Objects\Common. See Report, workspace, and folder customizationIBM Capacity Management Analytics lets you create custom reports, workspaces, and folders that satisfy your unique analytic requirements. You can modify the reports, workspaces, and folders by using IBM Cognos Business Intelligence tools. on creating reports.Edit your own Prompts & Titlebar report as necessary.In Cognos Report Studio, click File > Save As and save the report.Customizing a Capacity Management Analytics workspaceIBM Capacity Management Analytics lets you create custom workspaces that satisfy your unique analytic requirement using standard IBM Cognos Business Intelligence authoring tools.

Creating your own Prompts & Titlebars report

A Prompts & Titlebars report contains prompts and titlebars which are used in your own workspace. You can insert Prompts & Titlebars object into any IBM Capacity Management Analytics workspace.

Procedure

In IBM Cognos Connection, open the \Public Folders\IBM Capacity Management Analytics Solution Kit\Workspaces\Objects\Prompts & Titlebars directory.

Create your own Prompts & Titlebars report and name the report as appropriate according to your workspace usage.

You can create your own Prompts & Titlebars report from either one of the Prompts & Titlebars reports that Capacity Management Analytics provides. You can also create Prompts & Titlebars report using the common objects Capacity Management Analytics provides.

Note: The common objects that you can use to customize your report are located in \Public Folders\IBM Capacity Management Analytics Solution Kit\Workspaces\Objects\Common.

See Report, workspace, and folder customization on creating reports.

Edit your own Prompts & Titlebar report as necessary.

In Cognos Report Studio, click File > Save As and save the report.

Parent topic: Customizing a Capacity Management Analytics workspace

Creating your own widgets report used in CanvasA widgets report used in Canvas contains charts and tables you want to show in the workspace. You can insert any chart or table widget into any IBM Capacity Management Analytics workspace.workspacescreating widgets reportIn IBM Cognos Connection, open the \Public Folders\IBM Capacity Management Analytics Solution Kit\Workspaces\Objects\Charts & Tables directory.Create your own widgets report, and name the report as appropriate for your report usage. You can create your own widgets report from one of the widgets reports Capacity Management Analytics provides or from scratch. See Report, workspace, and folder customizationIBM Capacity Management Analytics lets you create custom reports, workspaces, and folders that satisfy your unique analytic requirements. You can modify the reports, workspaces, and folders by using IBM Cognos Business Intelligence tools. on creating reports.Reports should be saved in different folders according to the usage of each report.Edit your own widgets report as necessary.In Cognos Report Studio, click File > Save As and save the report.Customizing a Capacity Management Analytics workspaceIBM Capacity Management Analytics lets you create custom workspaces that satisfy your unique analytic requirement using standard IBM Cognos Business Intelligence authoring tools.

Creating your own widgets report used in Canvas

A widgets report used in Canvas contains charts and tables you want to show in the workspace. You can insert any chart or table widget into any IBM Capacity Management Analytics workspace.

Procedure

In IBM Cognos Connection, open the \Public Folders\IBM Capacity Management Analytics Solution Kit\Workspaces\Objects\Charts & Tables directory.

Create your own widgets report, and name the report as appropriate for your report usage. You can create your own widgets report from one of the widgets reports Capacity Management Analytics provides or from scratch. See Report, workspace, and folder customization on creating reports.

Note: Reports should be saved in different folders according to the usage of each report.

Edit your own widgets report as necessary.

In Cognos Report Studio, click File > Save As and save the report.

Parent topic: Customizing a Capacity Management Analytics workspace

Customizing top N display for the zServer Monitoring DashboardMany tables and charts on the zServer Monitoring Dashboard are configured to display the top five or top ten items from a query. You can modify the widget associated with a given table or chart to return any top number (top N) of items.top NcustomizingzServer Monitoring DashboardcustomizingGenerally, to customize the top N value for a chart or table, you open the associated widget source report in IBM Cognos Report Studio, and in the widget source query, set the filter for [RANK] to the desired number. For example, changing [RANK] <= 10 to [RANK] <= 12 returns the top 12 items instead of the top 10 items.The following table lists the widgets on the zServer Monitoring Dashboard for which top N can be modified.Widgets on the zServer Monitoring Dashboard for which top N can be modifiedWidget NameWidget Source ReportWidget Source QueryTop 5 Max TCP/IP Server Ports Connections ChartPublic > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > I/O > TCP/IP Server Ports Connection Statisticsqry_main_chartDASD I/O Channel Utilization - Top N at Machine Level TablePublic > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > I/O > Channel Utilizationqry_channel_machineLCU I/O Queuing Delay Time - Top N 10 at Machine Level ChartPublic > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > I/O > DASD I/O Performanceqry_main_chart_machine_levelDASD I/O Queuing Delay Intensity - Top N at Machine Level TablePublic > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > I/O > DASD I/O Performanceqry_dasd_qi_by_machineTop 5 DASD I/O Channel Path Utilization ChartPublic > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > I/O > Channel Utilizationqry_channelOpen the zServer Monitoring Dashboard workspace.Right-click the widget for which you want to change the top N, then click Widget Actions > Properties. Click the Report tab on the Properties dialog box.Note the location of the source report for the widget as displayed on the dialog box. You will have to go to this location to edit the source report.The report location will look similar to this:Public Folders >IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > I/O > Channel UtilizationIn IBM Cognos Connection, navigate to the location of the source report for the widget you want to modify.Click the Open with Report Studio icon Open in Report Studio iconfor the report you want to edit.Click the Select Object icon Select object icon to select the table or chart widget you want to modify.Right-click the table or chart widget, then click Go to Query.In the Detail Filters pane of Report Studio, double-click the [Rank] filter.In the Detail Filter Expression dialog box, modify the Expression Definition to use the top N filter you want to see in your chart or table. For example, set the Expression Definition to [RANK] <= 7 if you want to see the top seven items in a chart or table.Click OK to save the Expression Definition.For the LCU I/O Queuing Delay Time - Top 10 at Machine Level chart, Top 5 DASD I/O Channel Path Utilizations chart, and Top 5 Max TCP/IP Server Ports Connections chart, edit the chart title to reflect the new top N value you set in Step 10. All other chart and table titles use variables to automatically display the top N value you set.Click Save on the Report Studio toolbar to save the report.Reopen the zServer Monitoring Dashboard workspace.Right-click the chart or table for which you updated the top N value, then click Widget Actions > Reset.Click Yes to confirm that you want to reset the widget.Click the Refresh View icon in the Global Area of the zServer Monitoring Dashboard workspace to view the updated chart or table.Click Save on the zServer Monitoring Dashboard toolbar to save the workspace.Customizing a Capacity Management Analytics workspaceIBM Capacity Management Analytics lets you create custom workspaces that satisfy your unique analytic requirement using standard IBM Cognos Business Intelligence authoring tools.

Customizing top N display for the zServer Monitoring Dashboard

Many tables and charts on the zServer Monitoring Dashboard are configured to display the top five or top ten items from a query. You can modify the widget associated with a given table or chart to return any top number (top N) of items.

About this task

Generally, to customize the top N value for a chart or table, you open the associated widget source report in IBM® Cognos® Report Studio, and in the widget source query, set the filter for [RANK] to the desired number. For example, changing [RANK] <= 10 to [RANK] <= 12 returns the top 12 items instead of the top 10 items.

The following table lists the widgets on the zServer Monitoring Dashboard for which top N can be modified.

Table 16. . Widgets on the zServer Monitoring Dashboard for which top N can be modified

Widget Name | Widget Source Report | Widget Source Query
Top 5 Max TCP/IP Server Ports Connections Chart | Public > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > I/O > TCP/IP Server Ports Connection Statistics | qry_main_chart
DASD I/O Channel Utilization - Top N at Machine Level Table | Public > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > I/O > Channel Utilization | qry_channel_machine
LCU I/O Queuing Delay Time - Top N 10 at Machine Level Chart | Public > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > I/O > DASD I/O Performance | qry_main_chart_machine_level
DASD I/O Queuing Delay Intensity - Top N at Machine Level Table | Public > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > I/O > DASD I/O Performance | qry_dasd_qi_by_machine
Top 5 DASD I/O Channel Path Utilization Chart | Public > IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > I/O > Channel Utilization | qry_channel

Procedure

Open the zServer Monitoring Dashboard workspace.

Right-click the widget for which you want to change the top N, then click Widget Actions > Properties.

Click the Report tab on the Properties dialog box.

Note the location of the source report for the widget as displayed on the dialog box. You will have to go to this location to edit the source report. The report location will look similar to this:Public Folders >IBM Capacity Management Analytics Solution Kit > Workspaces > Objects > Charts & Tables > I/O > Channel Utilization

In IBM Cognos Connection, navigate to the location of the source report for the widget you want to modify.

Click the Open with Report Studio icon Open in Report Studio iconfor the report you want to edit.

Click the Select Object icon Select object icon to select the table or chart widget you want to modify.

Right-click the table or chart widget, then click Go to Query.

In the Detail Filters pane of Report Studio, double-click the [Rank] filter.

In the Detail Filter Expression dialog box, modify the Expression Definition to use the top N filter you want to see in your chart or table. For example, set the Expression Definition to [RANK] <= 7 if you want to see the top seven items in a chart or table.

Click OK to save the Expression Definition.

For the LCU I/O Queuing Delay Time - Top 10 at Machine Level chart, Top 5 DASD I/O Channel Path Utilizations chart, and Top 5 Max TCP/IP Server Ports Connections chart, edit the chart title to reflect the new top N value you set in Step 10. All other chart and table titles use variables to automatically display the top N value you set.

Click Save on the Report Studio toolbar to save the report.

Reopen the zServer Monitoring Dashboard workspace.

Right-click the chart or table for which you updated the top N value, then click Widget Actions > Reset.

Click Yes to confirm that you want to reset the widget.

Click the Refresh View icon in the Global Area of the zServer Monitoring Dashboard workspace to view the updated chart or table.

Click Save on the zServer Monitoring Dashboard toolbar to save the workspace.

Parent topic: Customizing a Capacity Management Analytics workspace

Customizing top N display for the Enterprise DashboardMany tables and charts on the Enterprise Dashboard are configured to display the top 10 items from a query. You can modify the widget that is associated with a given table or chart to return any top number (top N) of items. Generally, to customize the top N value for a chart or table, you open the associated widget source report in IBM Cognos Report Studio, and in the widget source query, set the filter for [RANK] to the wanted number. For example, changing [RANK] <= 10 to [RANK] <= 12 returns the top 12 items instead of the top 10 items. The following table lists the widgets on the Enterprise Dashboard for which top N can be modified. Widgets on the Enterprise Dashboard for which top N can be modifiedWidget NameWidget Source ReportWidget Source QueryAIX - 10 Highest CPU Usage Peaks TablePublic > IBM Capacity Management Analytics Solution Kit > Reports > CPU > AIX - 10 Highest/Lowest CPU Usage Peaksqry_main_highest _usage_top_list_aixAIX - 10 Lowest CPU Usage Peaks TablePublic > IBM Capacity Management Analytics Solution Kit > Reports > CPU > AIX - 10 Highest/Lowest CPU Usage Peaksqry_main_highest _usage_bottom_list_aixLinux for System X - 10 Highest CPU Usage Peaks TablePublic > IBM Capacity Management Analytics Solution Kit > Reports > CPU > Linux for System X - 10 Highest/Lowest CPU Usage Peaksqry_main_highest _usage_top_list_xLinuxLinux for System X –10 Lowest CPU Usage Peaks TablePublic > IBM Capacity Management Analytics Solution Kit > Reports > CPU > Linux for System X - 10 Highest/Lowest CPU Usage Peaksqry_main_highest _usage_bottom_list_xLinuxLinux for System Z –10 Highest CPU Usage Peaks TablePublic > IBM Capacity Management Analytics Solution Kit > Reports > CPU > Linux for System Z - 10 Highest/Lowest CPU Usage Peaksqry_main_highest _usage_top_list_zLinuxLinux for System Z –10 Lowest CPU Usage Peaks TablePublic > IBM Capacity Management Analytics Solution Kit > Reports > CPU > Linux for System Z - 10 Highest/Lowest CPU Usage Peaksqry_main_highest _usage_bottom_list_zLinuxWindows - 10 Highest CPU Usage Peaks TablePublic > IBM Capacity Management Analytics Solution Kit > Reports > CPU > Windows - 10 Highest/Lowest CPU Usage Peaksqry_main_highest _usage_top_list_winWindows - 10 Lowest CPU Usage Peaks TablePublic > IBM Capacity Management Analytics Solution Kit > Reports > CPU > Windows - 10 Highest/Lowest CPU Usage Peaksqry_main_highest _usage_bottom_list_winz/OS - 10 Highest CPU Usage Peaks TablePublic > IBM Capacity Management Analytics Solution Kit > Reports > CPU > z/OS - 10 Highest/Lowest CPU Usage Peaksqry_main_highest _usage_top_list_zosz/OS - 10 Lowest CPU Usage Peaks TablePublic > IBM Capacity Management Analytics Solution Kit > Reports > CPU > z/OS - 10 Highest/Lowest CPU Usage Peaksqry_main_highest _usage_bottom_list_zostop NcustomizingEnterprise DashboardcustomizingOpen the Enterprise Dashboard workspace.Right-click the widget for which you want to change the top N, then click Widget Actions > Properties. Click the Report tab on the Properties dialog box.Note the location of the source report for the widget as displayed on the dialog box. You must go to this location to edit the source report.The report location looks similar to this: Public Folders > IBM Capacity Management Analytics Solution Kit > Reports > CPU > z/OS - 10 Highest/Lowest CPU Usage Peaks.In IBM Cognos Connection, navigate to the location of the source report for the widget you want to modify.Click the Open with Report Studio icon Open in Report Studio icon for the report you want to edit.Click the Select Object icon Select object icon to select the table or chart widget you want to modify.Right-click the table or chart widget, then click Go to Query.In the Detail Filters pane of Report Studio, double-click the [Rank] filter.In the Detail Filter Expression dialog box, modify the Expression Definition to use the top N filter you want to see in your chart or table. For example, set the Expression Definition to [RANK] <= 7 if you want to see the top seven items in a chart or table.Click OK to save the Expression Definition.For the AIX - 10 Highest/Lowest CPU Usage Peaks, Linux for System X - 10 Highest/Lowest CPU Usage Peaks, Linux for System Z - 10 Highest/Lowest CPU Usage Peaks, Windows - 10 Highest/Lowest CPU Usage Peaks, and z/OS - 10 Highest/Lowest CPU Usage Peaks tables, edit the table title to reflect the new top N value you set in Step 10.All other table titles use variables to automatically display the top N value you set.Click Save on the Report Studio toolbar to save the report.Reopen the Enterprise Dashboard workspace.Right-click the table for which you updated the top N value, then click Widget Actions > Reset.Click Yes to confirm that you want to reset the widget.Click the Refresh View icon in the Global Area of the zServer Monitoring Dashboard workspace to view the updated chart or table.Click Save on the Enterprise Dashboard toolbar to save the workspace.Customizing a Capacity Management Analytics workspaceIBM Capacity Management Analytics lets you create custom workspaces that satisfy your unique analytic requirement using standard IBM Cognos Business Intelligence authoring tools.

Customizing top N display for the Enterprise Dashboard

Many tables and charts on the Enterprise Dashboard are configured to display the top 10 items from a query. You can modify the widget that is associated with a given table or chart to return any top number (top N) of items.

Generally, to customize the top N value for a chart or table, you open the associated widget source report in IBM® Cognos® Report Studio, and in the widget source query, set the filter for [RANK] to the wanted number. For example, changing [RANK] <= 10 to [RANK] <= 12 returns the top 12 items instead of the top 10 items.

The following table lists the widgets on the Enterprise Dashboard for which top N can be modified.

Widgets on the Enterprise Dashboard for which top N can be modified

Widget Name
Widget Source Report
Widget Source Query

AIX® - 10 Highest CPU Usage Peaks Table
Public > IBM Capacity Management Analytics Solution Kit > Reports > CPU > AIX - 10 Highest/Lowest CPU Usage Peaks

qry_main_highest
_usage_top_list_aix

AIX - 10 Lowest CPU Usage Peaks Table
Public > IBM Capacity Management Analytics Solution Kit > Reports > CPU > AIX - 10 Highest/Lowest CPU Usage Peaks

qry_main_highest
_usage_bottom_list_aix

Linux for System X - 10 Highest CPU Usage Peaks Table
Public > IBM Capacity Management Analytics Solution Kit > Reports > CPU > Linux for System X - 10 Highest/Lowest CPU Usage Peaks

qry_main_highest
_usage_top_list_xLinux

Linux for System X –10 Lowest CPU Usage Peaks Table
Public > IBM Capacity Management Analytics Solution Kit > Reports > CPU > Linux for System X - 10 Highest/Lowest CPU Usage Peaks

qry_main_highest
_usage_bottom_list_xLinux

Linux for System Z –10 Highest CPU Usage Peaks Table
Public > IBM Capacity Management Analytics Solution Kit > Reports > CPU > Linux for System Z - 10 Highest/Lowest CPU Usage Peaks

qry_main_highest
_usage_top_list_zLinux

Linux for System Z –10 Lowest CPU Usage Peaks Table
Public > IBM Capacity Management Analytics Solution Kit > Reports > CPU > Linux for System Z - 10 Highest/Lowest CPU Usage Peaks

qry_main_highest
_usage_bottom_list_zLinux

Windows - 10 Highest CPU Usage Peaks Table
Public > IBM Capacity Management Analytics Solution Kit > Reports > CPU > Windows - 10 Highest/Lowest CPU Usage Peaks

qry_main_highest
_usage_top_list_win

Windows - 10 Lowest CPU Usage Peaks Table
Public > IBM Capacity Management Analytics Solution Kit > Reports > CPU > Windows - 10 Highest/Lowest CPU Usage Peaks

qry_main_highest
_usage_bottom_list_win

z/OS - 10 Highest CPU Usage Peaks Table
Public > IBM Capacity Management Analytics Solution Kit > Reports > CPU > z/OS - 10 Highest/Lowest CPU Usage Peaks

qry_main_highest
_usage_top_list_zos

z/OS - 10 Lowest CPU Usage Peaks Table
Public > IBM Capacity Management Analytics Solution Kit > Reports > CPU > z/OS - 10 Highest/Lowest CPU Usage Peaks

qry_main_highest
_usage_bottom_list_zos

Procedure

Open the Enterprise Dashboard workspace.

Right-click the widget for which you want to change the top N, then click Widget Actions > Properties.

Click the Report tab on the Properties dialog box.

Note the location of the source report for the widget as displayed on the dialog box. You must go to this location to edit the source report.

The report location looks similar to this: Public Folders > IBM Capacity Management Analytics Solution Kit > Reports > CPU > z/OS - 10 Highest/Lowest CPU Usage Peaks.

In IBM Cognos Connection, navigate to the location of the source report for the widget you want to modify.

Click the Open with Report Studio icon Open in Report Studio icon for the report you want to edit.

Click the Select Object icon Select object icon to select the table or chart widget you want to modify.

Right-click the table or chart widget, then click Go to Query.

In the Detail Filters pane of Report Studio, double-click the [Rank] filter.

In the Detail Filter Expression dialog box, modify the Expression Definition to use the top N filter you want to see in your chart or table. For example, set the Expression Definition to [RANK] <= 7 if you want to see the top seven items in a chart or table.

Click OK to save the Expression Definition.

For the AIX - 10 Highest/Lowest CPU Usage Peaks, Linux for System X - 10 Highest/Lowest CPU Usage Peaks, Linux for System Z - 10 Highest/Lowest CPU Usage Peaks, Windows - 10 Highest/Lowest CPU Usage Peaks, and z/OS - 10 Highest/Lowest CPU Usage Peaks tables, edit the table title to reflect the new top N value you set in Step 10.

All other table titles use variables to automatically display the top N value you set.

Click Save on the Report Studio toolbar to save the report.

Reopen the Enterprise Dashboard workspace.

Right-click the table for which you updated the top N value, then click Widget Actions > Reset.

Click Yes to confirm that you want to reset the widget.

Click the Refresh View icon in the Global Area of the zServer Monitoring Dashboard workspace to view the updated chart or table.

Click Save on the Enterprise Dashboard toolbar to save the workspace.

Parent topic: Customizing a Capacity Management Analytics workspace

Customizing high usage and low usage thresholds for the Enterprise DashboardThe Percentage of Servers with High/Low Usage charts on the Enterprise Dashboard are configured with the default high usage threshold as 90% busy and the default low usage threshold as 10% busy. You can modify the widget that is associated with a given chart to use any high or low usage threshold.high usage thresholdcustomizinglow usage thresholdcustomizingEnterprise DashboardcustomizingGenerally, to customize the usage threshold value for the Percentage of Servers with High Usage chart or the Percentage of Servers with Low Usage chart, you open the associated widget source report in IBM Cognos Report Studio, and in the widget source query, set the filter for [CPU_BUSY_PCNT] to the wanted threshold value. For example, changing [CPU_BUSY_PCNT] > 90 to [CPU_BUSY_PCNT] > 80 returns the percentage of the total number of servers having a peak utilization greater than 80%.The following table lists the widgets on the Enterprise Dashboard for which usage threshold can be modified.Widgets on the Enterprise Dashboard for which usage threshold can be modifiedWidget NameWidget Source ReportWidget Source QueriesPercentage of Servers with High Usage ChartPublic > IBM Capacity Management Analytics Solution Kit > Reports > CPU > Percentage of Servers with High/Low Usageqry_main_ninety_percent_zLinux qry_main_ninety_percent_xLinux qry_main_ninety_percent_win qry_main_ninety_percent_aix qry_main_ninety_percent_zosPercentage of Servers with Low Usage ChartPublic > IBM Capacity Management Analytics Solution Kit > Reports > CPU > Percentage of Servers with High/Low Usageqry_main_ten_percent_zLinux qry_main_ten_percent_xLinux qry_main_ten_percent_win qry_main_ten_percent_aix qry_main_ten_percent_zosOpen the Enterprise Dashboard workspace.Right-click the widget for which you want to change the usage threshold, then click Widget Actions > Properties. Click the Report tab on the Properties dialog box.Note the location of the source report for the widget as displayed on the dialog box. You must go to this location to edit the source report.The report location looks similar to this: Public > IBM Capacity Management Analytics Solution Kit > Reports > CPU > Percentage of Servers with High/Low UsageIn IBM Cognos Connection, navigate to the location of the source report for the widget you want to modify.Click the Open with Report Studio icon Open in Report Studio icon for the report you want to edit.Click the Query Explorer icon Query Explorer icon, then click the Select Queries icon Select Queries icon to select the query you want to modify.In the Detail Filters pane of Report Studio, double-click the usage threshold filter.In the Detail Filter Expression dialog box, modify the Expression Definition to use the usage threshold filter you want to see in your chart or table. For example, set the Expression Definition to [CPU_BUSY_PCNT] > 80 if you want to see the percentage of the total number of servers having a peak utilization greater than 80% in a chart or table.Click OK to save the Expression Definition.Click Save on the Report Studio toolbar to save the report.Customizing a Capacity Management Analytics workspaceIBM Capacity Management Analytics lets you create custom workspaces that satisfy your unique analytic requirement using standard IBM Cognos Business Intelligence authoring tools.

Customizing high usage and low usage thresholds for the Enterprise Dashboard

The Percentage of Servers with High/Low Usage charts on the Enterprise Dashboard are configured with the default high usage threshold as 90% busy and the default low usage threshold as 10% busy. You can modify the widget that is associated with a given chart to use any high or low usage threshold.

About this task

Generally, to customize the usage threshold value for the Percentage of Servers with High Usage chart or the Percentage of Servers with Low Usage chart, you open the associated widget source report in IBM® Cognos® Report Studio, and in the widget source query, set the filter for [CPU_BUSY_PCNT] to the wanted threshold value. For example, changing [CPU_BUSY_PCNT] > 90 to [CPU_BUSY_PCNT] > 80 returns the percentage of the total number of servers having a peak utilization greater than 80%.

The following table lists the widgets on the Enterprise Dashboard for which usage threshold can be modified.

Table 17. . Widgets on the Enterprise Dashboard for which usage threshold can be modified

Widget Name
Widget Source Report
Widget Source Queries

Percentage of Servers with High Usage Chart
Public > IBM Capacity Management Analytics Solution Kit > Reports > CPU > Percentage of Servers with High/Low Usage

qry_main_ninety_percent_zLinux

qry_main_ninety_percent_xLinux

qry_main_ninety_percent_win

qry_main_ninety_percent_aix

qry_main_ninety_percent_zos

Percentage of Servers with Low Usage Chart
Public > IBM Capacity Management Analytics Solution Kit > Reports > CPU > Percentage of Servers with High/Low Usage

qry_main_ten_percent_zLinux

qry_main_ten_percent_xLinux

qry_main_ten_percent_win

qry_main_ten_percent_aix

qry_main_ten_percent_zos

Procedure

Open the Enterprise Dashboard workspace.

Right-click the widget for which you want to change the usage threshold, then click Widget Actions > Properties.

Click the Report tab on the Properties dialog box.

Note the location of the source report for the widget as displayed on the dialog box. You must go to this location to edit the source report. The report location looks similar to this: Public > IBM Capacity Management Analytics Solution Kit > Reports > CPU > Percentage of Servers with High/Low Usage

In IBM Cognos Connection, navigate to the location of the source report for the widget you want to modify.

Click the Open with Report Studio icon Open in Report Studio icon for the report you want to edit.

Click the Query Explorer icon Query Explorer icon, then click the Select Queries icon Select Queries icon to select the query you want to modify.

In the Detail Filters pane of Report Studio, double-click the usage threshold filter.

In the Detail Filter Expression dialog box, modify the Expression Definition to use the usage threshold filter you want to see in your chart or table. For example, set the Expression Definition to [CPU_BUSY_PCNT] > 80 if you want to see the percentage of the total number of servers having a peak utilization greater than 80% in a chart or table.

Click OK to save the Expression Definition.

Click Save on the Report Studio toolbar to save the report.

Parent topic: Customizing a Capacity Management Analytics workspace

Software cost analysisSoftware cost analysis allows you to better manage z/OS software costs and identify where and when workloads need to be adjusted and when more capacity is required.Software cost analysisSoftware cost analysis in IBM Capacity Management Analytics includes one workspace and four reports.The Software Cost Analysis Summary workspaceManaging System z capacity can be costly and complex. This workspace allows you to better manage your software costs on System z by providing graphs and tables that show both the details and the summary of the software costs. shows the machine level summary and registered IBM product MSU and monetary value summary. The SCA: LPAR MSU UtilizationThis report helps you understand and forecast your costs based on your MSU usage. You can compare your historical MSU usage with the forecasted usage to see upcoming trends and take proactive action to adjust your workload and capacity limits to help control your bill. shows the MSU utilization metrics from an LPAR perspective.The SCA: Product MSU and PriceThis report shows a registered IBM product's billable MSUs and monetary cost from a single CPC perspective for each scenario: Observed, Forecasted, and Optimized. shows an IBM product’s billable MSUs and monetary cost on a CPC from product and CPC perspective. The SCA: NO89 Product MSU and PriceThis report helps you understand and forecast your costs based on your MSU usage. It also shows you the monetary cost of your MSU for each NO89 product. You can compare the historical MSU usage with the forecasted usage, see upcoming trends, and take proactive measures to adjust your workload and capacity limits to help control your costs. is similar to the Product MSU and Price report, but shows the IBM NO89, unregistered products. The SCA: NO89 Products MatrixThis report provides a single place to review all of the NO89 products that use sub-capacity pricing. It also shows where these products are designated to be run (both the CPCs and the LPARs). It shows all of the locations where a product is running along with all of the other NO89 products to provide a planning resource for LPAR and CPC consolidations. From this report, you can also drill to the NO89 Product MSU and Price report for any product. shows the running matrix of LPARs and IBM NO89 products.The Sub-Capacity Reporting Tool (SCRT) is the official pricing tool for IBM. The Software Cost Analysis capability of Capacity Management Analytics is for your reference and analysis. If there is any discrepancy between SCRT and Capacity Management Analytics, SCRT values are used for billing purposes. The monetary value shown in Capacity Management Analytics reports is based on the highest MSU value calculated by Capacity Management Analytics and the price value customer supply in the HCM.CUSTOMER_PRICE table, which may differ from actual IBM charges. The Sub-Capacity Reporting Tool (SCRT) is the official pricing tool for IBM. Capacity Management Analytics is to be used only for reference and planning. Any discrepancies in Capacity Management Analytics will not be considered valid against SCRT. The monetary values shown in Capacity Management Analytics reports are based on the highest calculated MSU value, and the monetary values given by the customer may be different with the final bill that IBM provides.Capacity Management Analytics does not enforce any of the Terms & Conditions set forth by the IBM products. Anything not listed is therefore not supported by Capacity Management Analytics. Among the unsupported items in Capacity Management Analytics are:Capacity On Demand Technology Update Pricing Mobile PricingEntitlement IDs / FeaturesSingle Version ChargingSYSPLEX / Parallel SYSPLEX PricingSupported scenariosCapacity Management Analytics supports three scenarios for Software Cost Analysis: observed, forecasted, and optimized. Supported pricing structuresFor registered IBM products, Software Cost Analysis supports the following pricing structures.MSU calculationsIn Capacity Management Analytics reports and workspaces, MSU is calculated according to the date and time range you selectProduct display namesTo help you select products easily in report prompts, Capacity Management Analytics shows products using the product display name. z/OS product identificationz/OS products are identified by the product ID in SMF89 which has the z/OS feature.Historical data support for pricing information and NO89 Product Matrix informationHistorical information is not considered for pricing-related calculations or in the NO89 Product Matrix report.

Software cost analysis

Software cost analysis allows you to better manage z/OS software costs and identify where and when workloads need to be adjusted and when more capacity is required.

Software cost analysis in IBM® Capacity Management Analytics includes one workspace and four reports.

The Software Cost Analysis Summary workspace shows the machine level summary and registered IBM product MSU and monetary value summary.

The SCA: LPAR MSU Utilization shows the MSU utilization metrics from an LPAR perspective.

The SCA: Product MSU and Price shows an IBM product’s billable MSUs and monetary cost on a CPC from product and CPC perspective.

The SCA: NO89 Product MSU and Price is similar to the Product MSU and Price report, but shows the IBM NO89, unregistered products.

The SCA: NO89 Products Matrix shows the running matrix of LPARs and IBM NO89 products.

Important: The Sub-Capacity Reporting Tool (SCRT) is the official pricing tool for IBM. The Software Cost Analysis capability of Capacity Management Analytics is for your reference and analysis. If there is any discrepancy between SCRT and Capacity Management Analytics, SCRT values are used for billing purposes. The monetary value shown in Capacity Management Analytics reports is based on the highest MSU value calculated by Capacity Management Analytics and the price value customer supply in the HCM.CUSTOMER_PRICE table, which may differ from actual IBM charges.

The Sub-Capacity Reporting Tool (SCRT) is the official pricing tool for IBM. Capacity Management Analytics is to be used only for reference and planning. Any discrepancies in Capacity Management Analytics will not be considered valid against SCRT. The monetary values shown in Capacity Management Analytics reports are based on the highest calculated MSU value, and the monetary values given by the customer may be different with the final bill that IBM provides.

Capacity Management Analytics does not enforce any of the Terms & Conditions set forth by the IBM products. Anything not listed is therefore not supported by Capacity Management Analytics. Among the unsupported items in Capacity Management Analytics are:

Capacity On Demand

Technology Update Pricing

Mobile Pricing

Entitlement IDs / Features

Single Version Charging

SYSPLEX / Parallel SYSPLEX Pricing

Supported scenarios
Capacity Management Analytics supports three scenarios for Software Cost Analysis: observed, forecasted, and optimized.

Supported pricing structures
For registered IBM products, Software Cost Analysis supports the following pricing structures.

MSU calculations
In Capacity Management Analytics reports and workspaces, MSU is calculated according to the date and time range you select

Product display names
To help you select products easily in report prompts, Capacity Management Analytics shows products using the product display name.

z/OS product identification
z/OS® products are identified by the product ID in SMF89 which has the "z/OS" feature.

Historical data support for pricing information and NO89 Product Matrix information
Historical information is not considered for pricing-related calculations or in the NO89 Product Matrix report.

Supported scenariosCapacity Management Analytics supports three scenarios for Software Cost Analysis: observed, forecasted, and optimized. Software Cost Analysissupported scenariosObserved scenarios The observed scenario shows what has been observed in the past. The data is from Tivoli Decision Support for z/OS with SMF70 and SMF89 records. You must ensure that data is aligned between SMF70 and SMF89. If they are not both collected at the same time interval, report values might be inaccurate.Forecasted scenarios The forecasted scenario shows what is predicted to happen in the future. The data is from tables that store the result from forecast streams in the HCM schema. Following two sentences seem contradictory, awaiting clarification. Although the forecast stream is for predicting the future, the stream also provides referenced forecasted value for previous periods, so you can sometimes see both observed and forecasted data in reports. For comparison purposes, some reports and workspaces show observed values as well as forecasted values. In those places, observed values are retrieved directly from observed data, and not from forecasted result tables, so you can see the latest actual observed value. For more information about forecast streams, such as the default date ranges that the stream is supporting, see Software Cost Analysis forecasting streams.Optimized scenarios The optimized scenario shows suggested actions based on forecasted MSU values. The data is from tables that store the result from optimization streams in the HCM schema. As the suggested actions are generated based on forecasted data, you should run optimization streams immediately after forecast stream. This ensures that you can see aligned forecasted and optimized data in reports. If there are new forecasted results, but the optimization stream has not been run, then the values that are shown in forecasted and optimized scenarios might not be aligned. When this occurs, reports provide a warning message to remind you to run the optimization stream: CMA-RW002 Scenario Synchronization Warning Message Please note current optimization result is based on the forecast result running at <timestamp>. There are new updates, please run optimization again. For more information about optimization streams, such as the default date ranges that the stream is supporting, see Billable MSU optimization streamThis stream is used to determine how to rearrange qualified products, or parts of products, among LPARs to lower the total cost of the billable MSUs. A qualified product is an IBM product that can produce billable MSUs in the forecasted data. If the forecasted MSU usage for a product is zero or if it is not an IBM product, then it is not included in the optimization..Software cost analysisSoftware cost analysis allows you to better manage z/OS software costs and identify where and when workloads need to be adjusted and when more capacity is required.

Supported scenarios

Capacity Management Analytics supports three scenarios for Software Cost Analysis: observed, forecasted, and optimized.

Observed scenarios

The observed scenario shows what has been observed in the past. The data is from Tivoli® Decision Support for z/OS® with SMF70 and SMF89 records.

Note: You must ensure that data is aligned between SMF70 and SMF89. If they are not both collected at the same time interval, report values might be inaccurate.

Forecasted scenarios

The forecasted scenario shows what is predicted to happen in the future. The data is from tables that store the result from forecast streams in the HCM schema.

Although the forecast stream is for predicting the future, the stream also provides referenced forecasted value for previous periods, so you can sometimes see both observed and forecasted data in reports.

For comparison purposes, some reports and workspaces show observed values as well as forecasted values. In those places, observed values are retrieved directly from observed data, and not from forecasted result tables, so you can see the latest actual observed value.

For more information about forecast streams, such as the default date ranges that the stream is supporting, see Software Cost Analysis forecasting streams.

Optimized scenarios

The optimized scenario shows suggested actions based on forecasted MSU values. The data is from tables that store the result from optimization streams in the HCM schema.

As the suggested actions are generated based on forecasted data, you should run optimization streams immediately after forecast stream. This ensures that you can see aligned forecasted and optimized data in reports. If there are new forecasted results, but the optimization stream has not been run, then the values that are shown in forecasted and optimized scenarios might not be aligned. When this occurs, reports provide a warning message to remind you to run the optimization stream: CMA-RW002 Scenario Synchronization Warning Message Please note current optimization result is based on the forecast result running at <timestamp>. There are new updates, please run optimization again.

For more information about optimization streams, such as the default date ranges that the stream is supporting, see Billable MSU optimization stream.

Parent topic: Software cost analysis

Supported pricing structuresFor registered IBM products, Software Cost Analysis supports the following pricing structures.Software Cost Analysissupported pricing structuresSub-capacity pricing of Monthly License Charge (MLC) pricing Software Cost Analysis supports these pricing structures for Monthly License Charges.Workload License Charges (WLC) - Variable WLC Structure is considered in monetary value calculations. For details, see http://www.ibm.com/systems/z/resources/swprice/mlc/wlc.html.Advanced Workload License Charges (AWLC), including Integrated Workload Pricing (IWP), when appropriate - AWLC Structure is considered in monetary value calculations. For details, see http://www.ibm.com/systems/z/resources/swprice/mlc/awlc.html.Entry Workload License Charges (EWLC) - EWLC Structure is considered in monetary value calculations. For details, see http://www.ibm.com/systems/z/resources/swprice/mlc/ewlc.html.Advanced Entry Workload License Charges (AEWLC), including Integrated Workload Pricing (IWP), when appropriate - AEWLC Structure is considered in monetary value calculations. For details, see http://www.ibm.com/systems/z/resources/swprice/mlc/aewlc.html.Midrange Workload License Charges (MWLC) - MWLC Structure is considered in monetary value calculations. For details, see http://www.ibm.com/systems/z/resources/swprice/mlc/mwlc.html.System z New Application License Charges (zNALC) - zNALC Structure is considered in monetary value calculations. For details, see http://www.ibm.com/systems/z/resources/swprice/mlc/znalc.html. System z International Product License Agreement (IPLA) Software Software Cost Analysis supports these pricing structures for System z International Product License Agreement (IPLA) Software. Execution-based licensing termsGetting Started Sub-capacity Pricing (GSSP)Reference-based licensing termsz/OS based licensing terms Value Unit Pricing is applied. The following Value Unit Exhibits (VUE) which convert MSU to VU are supported:VUE001VUE007VUE020For details on Value Unit Exhibits, see http://www.ibm.com/systems/z/resources/swprice/zipla/vue.html. IBM NO89 products For IBM NO89 products, Software Cost Analysis supports the same pricing structures as registered products. z/OS V1 (5694-A01) and z/OS V2 (5655-ZOS) z/OS products can use either traditional pricing structures or System z New Application License Charges (zNALC). Capacity Management Analytics calculates MSU and monetary value according to the pricing structure in effect: zNALC or traditional pricing. WebSphere Transformation Extender V8 (5655-R95) Software Cost Analysis in Capacity Management Analytics considers product ID as the unique identifier of a product and uses the same pricing structure, except for WebSphere Transformation Extender V8 (5655-R95). WebSphere Transformation Extender V8 (5655-R95) differs from all other Getting Started Sub-Capacity Pricing products because only parts of this product (just two of the four priced features) are eligible for Getting Started Sub-Capacity Pricing . Capacity Management Analytics considers WebSphere Transformation Extender V8 as one product, and use the GSSP values in the HCM.SUBCAP_PROGRAMS table to identify the pricing structure that is applied in Software Cost Analysis. Software cost analysisSoftware cost analysis allows you to better manage z/OS software costs and identify where and when workloads need to be adjusted and when more capacity is required.

Supported pricing structures

For registered IBM® products, Software Cost Analysis supports the following pricing structures.

Sub-capacity pricing of Monthly License Charge (MLC) pricing

Software Cost Analysis supports these pricing structures for Monthly License Charges.

Workload License Charges (WLC) - Variable WLC Structure is considered in monetary value calculations. For details, see http://www.ibm.com/systems/z/resources/swprice/mlc/wlc.html.

Advanced Workload License Charges (AWLC), including Integrated Workload Pricing (IWP), when appropriate - AWLC Structure is considered in monetary value calculations. For details, see http://www.ibm.com/systems/z/resources/swprice/mlc/awlc.html.

Entry Workload License Charges (EWLC) - EWLC Structure is considered in monetary value calculations. For details, see http://www.ibm.com/systems/z/resources/swprice/mlc/ewlc.html.

Advanced Entry Workload License Charges (AEWLC), including Integrated Workload Pricing (IWP), when appropriate - AEWLC Structure is considered in monetary value calculations. For details, see http://www.ibm.com/systems/z/resources/swprice/mlc/aewlc.html.

Midrange Workload License Charges (MWLC) - MWLC Structure is considered in monetary value calculations. For details, see http://www.ibm.com/systems/z/resources/swprice/mlc/mwlc.html.

System z® New Application License Charges (zNALC) - zNALC Structure is considered in monetary value calculations. For details, see http://www.ibm.com/systems/z/resources/swprice/mlc/znalc.html.

System z International Product License Agreement (IPLA) Software

Software Cost Analysis supports these pricing structures for System z International Product License Agreement (IPLA) Software.

Execution-based licensing terms

Getting Started Sub-capacity Pricing (GSSP)

Reference-based licensing terms

z/OS® based licensing terms

Value Unit Pricing is applied. The following Value Unit Exhibits (VUE) which convert MSU to VU are supported:

VUE001

VUE007

VUE020

For details on Value Unit Exhibits, see http://www.ibm.com/systems/z/resources/swprice/zipla/vue.html.

IBM NO89 products

For IBM NO89 products, Software Cost Analysis supports the same pricing structures as registered products.

z/OS V1 (5694-A01) and z/OS V2 (5655-ZOS)

z/OS products can use either traditional pricing structures or System z New Application License Charges (zNALC). Capacity Management Analytics calculates MSU and monetary value according to the pricing structure in effect: zNALC or traditional pricing.

WebSphere Transformation Extender V8 (5655-R95)

Software Cost Analysis in Capacity Management Analytics considers product ID as the unique identifier of a product and uses the same pricing structure, except for WebSphere® Transformation Extender V8 (5655-R95).

WebSphere Transformation Extender V8 (5655-R95) differs from all other Getting Started Sub-Capacity Pricing products because only parts of this product (just two of the four priced features) are eligible for Getting Started Sub-Capacity Pricing . Capacity Management Analytics considers WebSphere Transformation Extender V8 as one product, and use the GSSP values in the HCM.SUBCAP_PROGRAMS table to identify the pricing structure that is applied in Software Cost Analysis.

Parent topic: Software cost analysis

MSU calculationsIn Capacity Management Analytics reports and workspaces, MSU is calculated according to the date and time range you selectSoftware Cost AnalysisMSU calculationsWhen observing MSU calculations you should be aware that: The date and time range used in calculations is based on the time zone you select in the report. If the LPARs in a CPC are in different time zones, or the CPCs in an enterprise are in different time zones, all of them are converted to the time zone you specify in the report. Unlike SCRT, which calculates billable MSU within one month only, Software Cost Analysis calculates all values according to the date and time range you select in prompts. In the four-hour rolling average calculation of the first three hours of the date and time range that you select in report or workspace, the values of the previous hours are included in the calculation (if there is any data).The following terms are important to understand and are referenced in the descriptions of the MSU calculations performed by Capacity Management Analytics.LPAR 4HRA MSUThe four-hour rolling average of LPAR Hourly MSU Utilization of a LPAR at each hour. In the implementation of Observed scenario, this value is not calculated by Capacity Management Analytics, but by DRL.MVSPM_LPAR_MSU_T.MSU_4HRA, which is from SMF70LAC directly.LPAR BILLABLE MSUThe minimum value of LPAR 4HRA MSU and defined capacity.Defined capacity is from DRL. MVSPM_LPAR_MSU_T.CAPACITY_LIMIT_MSU. If defined capacity is not set or the value is 0, then use the value from DRL.MVSPM_LPAR_MSU_T.PROC_CAPACITY_MSU to compare with LPAR 4HRA MSU.If the minimum value is not an integer, rounding is performed:If value is < 1, then use 1. For example, 0.2 is rounded up to 1.If value >= 1, then round the value down to the nearest integer. For example, 2.6 is rounded down to 2.If the value is very near to an integer (less than 10^-4), then use the integer value. For example, 3.9999 is rounded up to 4.GROUP CAPACITY LIMITThe capacity limit of the group; an LPAR can be set to a group. The value is from DRL.MVSPM_LPAR_MSU_T.CAPACITY_GRP_LMT.PRODUCT HOURLY MSU UTILIZATIONThe MSU that the product utilized at each hour. Each hour’s value is the sum of each SMF collection interval’s value within an hour.PRODUCT 4HRA MSUThe four hour rolling average of PRODUCT HOURLY MSU UTILIZATION of the product on the LPAR at each hour.MLC & IPLA Execution-based calculationMLC & IPLA Execution-based is calculated as follows:Calculate TOTAL BILLABLE MSU PER CPC, which is the sum of LPAR BILLABLE MSU of each LPAR the product is running on at each hour. GROUP CAPACITY LIMIT is considered. If one or more LPARs on which the product runs belong to a group, then the sum of the LPAR BILLABLE MSU in the group must be equal to or less than the GROUP CAPACITY LIMIT.Find the peak TOTAL BILLABLE MSU PER CPC during the specified date and time range.MLC-IWP Adjusted calculationMLC-IWP Adjusted is calculated as follows:For each LPAR on which the product runs, calculate the TOTAL MSU UTILIZATION OF IWP DEFINING PRODUCTS. This value is the sum of PRODUCT 4HRA MSU of IWP defining products which are identified in the HCM.SUBCAP_PROGRAMS table and are running on the same LPAR.For each LPAR on which the product runs, calculate the LPAR ADJUSTED BILLABLE MSU. This value equals the rounded value of (LPAR BILLABLE MSU) – (TOTAL MSU UTILIZATION OF IWP DEFINING PRODUCTS). In this formula, LPAR BILLABLE MSU and TOTAL MSU UTILIZATION OF IWP DEFINING PRODUCTS are not rounded prior to calculation.Calculate TOTAL ADJUSTED BILLABLE MSU PER CPC, which is the sum of LPAR ADJUSTED BILLABLE MSU of each LPAR on which the product is running at each hour.GROUP CAPACITY LIMIT is considered. If one or more LPARs on which the product runs belong to a group, then the sum of the LPAR ADJUSTED BILLABLE MSU in the group must be equal to or less than the GROUP CAPACITY LIMIT.Find the peak TOTAL BILLABLE MSU PER CPC during the specified date and time range.IPLA Execution-based GSSP calculationIPLA Execution-based GSSP is calculated as follows:For each LPAR on which the product runs, get the PRODUCT 4HRA MSU of this product, and PRODUCT INT 4HRA MSU.PRODUCT INT 4HRA MSU is the four hour rolling average utilization of the DB2 time, which is used by this product via the DB2 JDBC type 2 connector. The one hour utilization value is calculated by the same formula of PRODUCT HOURLY MSU UTILIZATION, but the parameters are from the DRL.MVSPM_PROD_INT_T table instead of the DRL.MVSPM_PROD_T table.For each LPAR on which the product runs, calculate LPAR GSSP MSU1, which is equal to (PRODUCT 4HRA MSU) – (PRODUCT INT 4HRA MSU).For each LPAR on which the product runs, calculate LPAR GSSP BILLABLE MSU, which is the smaller of LPAR BILLABLE MSU and (LPAR GSSP MSU1) * (GSSP SCALING FACTOR). GSSP SCALING FACTOR is from the HCM.SUBCAP_PROGRAMS table.Calculate TOTAL GSSP BILLABLE MSU PER CPC, which is the sum of LPAR GSSP BILLABLE MSU of each LPAR on which the product is running at each hour.GROUP CAPACITY LIMIT is considered. If one or more LPARs on which the product runs belong to a group, then the sum of the LPAR GSSP BILLABLE MSU in the group must be equal to or less than the GROUP CAPACITY LIMIT.Find the peak TOTAL GSSP BILLABLE MSU PER CPC during the specified date and time range.IPLA z/OS-based calculationIPLA z/OS-based is calculated as follows:Calculate TOTAL ZOS BILLABLE MSU PER CPC, which is the sum of LPAR BILLABLE MSU of all LPARs that runs z/OS at each hour. Because only z/OS system can collect SMF70 and SMF89 records, all the LPARs for which data is collected are included.GROUP CAPACITY LIMIT is considered. If one or more LPARs belong to a group, then sum of the LPAR BILLABLE MSU in the group must be equal to or less than the GROUP CAPACITY LIMIT.Find the peak TOTAL ZOS BILLABLE MSU PER CPC as ZOS MSU PER CPC during the specified date and time range.IPLA Reference-based calculationIPLA Reference-based is calculated as follows:Find the parent products of this product. This product's parent names are from HCM.SUBCAP_PROGRAMS.PARENT_NAME. The parent products can be identified by mapping PROD_ID to PARENT_NAME in the HCM.PARENT_PROGRAMS table.Calculate the HIGHEST MSU PER CPC of all the parents.Calculate TOTAL PARENT MSU, which is the sum of HIGHEST MSU PER CPC of all this product’s parents.Calculate the HIGHEST MSU PER CPC of this child product by getting the minimum value of TOTAL PARENT MSU PER CPC and ZOS MSU PER CPC. Software cost analysisSoftware cost analysis allows you to better manage z/OS software costs and identify where and when workloads need to be adjusted and when more capacity is required.

MSU calculations

In Capacity Management Analytics reports and workspaces, MSU is calculated according to the date and time range you select

When observing MSU calculations you should be aware that:

The date and time range used in calculations is based on the time zone you select in the report. If the LPARs in a CPC are in different time zones, or the CPCs in an enterprise are in different time zones, all of them are converted to the time zone you specify in the report. Unlike SCRT, which calculates billable MSU within one month only, Software Cost Analysis calculates all values according to the date and time range you select in prompts. In the four-hour rolling average calculation of the first three hours of the date and time range that you select in report or workspace, the values of the previous hours are included in the calculation (if there is any data).

The following terms are important to understand and are referenced in the descriptions of the MSU calculations performed by Capacity Management Analytics.

LPAR 4HRA MSU

The four-hour rolling average of LPAR Hourly MSU Utilization of a LPAR at each hour. In the implementation of Observed scenario, this value is not calculated by Capacity Management Analytics, but by DRL.MVSPM_LPAR_MSU_T.MSU_4HRA, which is from SMF70LAC directly.

LPAR BILLABLE MSU

The minimum value of LPAR 4HRA MSU and defined capacity.

Defined capacity is from DRL. MVSPM_LPAR_MSU_T.CAPACITY_LIMIT_MSU. If defined capacity is not set or the value is 0, then use the value from DRL.MVSPM_LPAR_MSU_T.PROC_CAPACITY_MSU to compare with LPAR 4HRA MSU.

If the minimum value is not an integer, rounding is performed:

If value is < 1, then use 1. For example, 0.2 is rounded up to 1.

If value >= 1, then round the value down to the nearest integer. For example, 2.6 is rounded down to 2.

If the value is very near to an integer (less than 10^-4), then use the integer value. For example, 3.9999 is rounded up to 4.

GROUP CAPACITY LIMIT

The capacity limit of the group; an LPAR can be set to a group. The value is from DRL.MVSPM_LPAR_MSU_T.CAPACITY_GRP_LMT.

PRODUCT HOURLY MSU UTILIZATION

The MSU that the product utilized at each hour.

Each hour’s value is the sum of each SMF collection interval’s value within an hour.

PRODUCT 4HRA MSU

The four hour rolling average of PRODUCT HOURLY MSU UTILIZATION of the product on the LPAR at each hour.

MLC & IPLA Execution-based calculation

MLC & IPLA Execution-based is calculated as follows:

Calculate TOTAL BILLABLE MSU PER CPC, which is the sum of LPAR BILLABLE MSU of each LPAR the product is running on at each hour.

GROUP CAPACITY LIMIT is considered. If one or more LPARs on which the product runs belong to a group, then the sum of the LPAR BILLABLE MSU in the group must be equal to or less than the GROUP CAPACITY LIMIT.

Find the peak TOTAL BILLABLE MSU PER CPC during the specified date and time range.

MLC-IWP Adjusted calculation

MLC-IWP Adjusted is calculated as follows:

For each LPAR on which the product runs, calculate the TOTAL MSU UTILIZATION OF IWP DEFINING PRODUCTS. This value is the sum of PRODUCT 4HRA MSU of IWP defining products which are identified in the HCM.SUBCAP_PROGRAMS table and are running on the same LPAR.

For each LPAR on which the product runs, calculate the LPAR ADJUSTED BILLABLE MSU. This value equals the rounded value of (LPAR BILLABLE MSU) – (TOTAL MSU UTILIZATION OF IWP DEFINING PRODUCTS). In this formula, LPAR BILLABLE MSU and TOTAL MSU UTILIZATION OF IWP DEFINING PRODUCTS are not rounded prior to calculation.

Calculate TOTAL ADJUSTED BILLABLE MSU PER CPC, which is the sum of LPAR ADJUSTED BILLABLE MSU of each LPAR on which the product is running at each hour.

GROUP CAPACITY LIMIT is considered. If one or more LPARs on which the product runs belong to a group, then the sum of the LPAR ADJUSTED BILLABLE MSU in the group must be equal to or less than the GROUP CAPACITY LIMIT.

Find the peak TOTAL BILLABLE MSU PER CPC during the specified date and time range.

IPLA Execution-based GSSP calculation

IPLA Execution-based GSSP is calculated as follows:

For each LPAR on which the product runs, get the PRODUCT 4HRA MSU of this product, and PRODUCT INT 4HRA MSU.

PRODUCT INT 4HRA MSU is the four hour rolling average utilization of the DB2® time, which is used by this product via the DB2 JDBC type 2 connector. The one hour utilization value is calculated by the same formula of PRODUCT HOURLY MSU UTILIZATION, but the parameters are from the DRL.MVSPM_PROD_INT_T table instead of the DRL.MVSPM_PROD_T table.

For each LPAR on which the product runs, calculate LPAR GSSP MSU1, which is equal to (PRODUCT 4HRA MSU) – (PRODUCT INT 4HRA MSU).

For each LPAR on which the product runs, calculate LPAR GSSP BILLABLE MSU, which is the smaller of LPAR BILLABLE MSU and (LPAR GSSP MSU1) * (GSSP SCALING FACTOR). GSSP SCALING FACTOR is from the HCM.SUBCAP_PROGRAMS table.

Calculate TOTAL GSSP BILLABLE MSU PER CPC, which is the sum of LPAR GSSP BILLABLE MSU of each LPAR on which the product is running at each hour.

GROUP CAPACITY LIMIT is considered. If one or more LPARs on which the product runs belong to a group, then the sum of the LPAR GSSP BILLABLE MSU in the group must be equal to or less than the GROUP CAPACITY LIMIT.

Find the peak TOTAL GSSP BILLABLE MSU PER CPC during the specified date and time range.

IPLA z/OS-based calculation

IPLA z/OS-based is calculated as follows:

Calculate TOTAL ZOS BILLABLE MSU PER CPC, which is the sum of LPAR BILLABLE MSU of all LPARs that runs z/OS® at each hour. Because only z/OS system can collect SMF70 and SMF89 records, all the LPARs for which data is collected are included.

GROUP CAPACITY LIMIT is considered. If one or more LPARs belong to a group, then sum of the LPAR BILLABLE MSU in the group must be equal to or less than the GROUP CAPACITY LIMIT.

Find the peak TOTAL ZOS BILLABLE MSU PER CPC as ZOS MSU PER CPC during the specified date and time range.

IPLA Reference-based calculation

IPLA Reference-based is calculated as follows:

Find the parent products of this product. This product's parent names are from HCM.SUBCAP_PROGRAMS.PARENT_NAME. The parent products can be identified by mapping PROD_ID to PARENT_NAME in the HCM.PARENT_PROGRAMS table.

Calculate the HIGHEST MSU PER CPC of all the parents.

Calculate TOTAL PARENT MSU, which is the sum of HIGHEST MSU PER CPC of all this product’s parents.

Calculate the HIGHEST MSU PER CPC of this child product by getting the minimum value of TOTAL PARENT MSU PER CPC and ZOS MSU PER CPC.

Parent topic: Software cost analysis

Product display namesTo help you select products easily in report prompts, Capacity Management Analytics shows products using the product display name. A product display name uses this syntax: [PRODUCT_ID]_[PRODUCT_NAME]_[PRODUCT_PRICING_STRUCTURE].The components of a product display name are described in the following table.Product display name componentsDisplay name componentDescriptionPRODUCT_IDIs unique per product.For registered products, the ID is retrieved from DRL. MVSPM_PROD_T.PROD_ID. For NO89 or unregistered products, the ID is retrieved from HCM.NO89_PRODUCTS.PRODUCT_ID.PRODUCT_NAMEIf DRL.MVSPM_PROD_T.PROD_OWNER contains IBM, then get product name from HCM.SUBCAP_PROGRAMS. If this product ID is not found or the value is null in HCM.SUBCAP_PROGRAMS table, then use Not Defined.If DRL.MVSPM_PROD_T.PROD_OWNER does not contain IBM, then get product name from DRL.MVSPM_PROD_T.PROD_NAME.PRODUCT_PRICING_STRUCTUREFor registered products, if DRL.MVSPM_PROD_T.PROD_OWNER contains IBM, then check the product ID in HCM.SUBCAP_PROGRAMS. For IBM NO89 or unregistered products, check the product ID in HCM.SUBCAP_PROGRAMS. If the product ID exists in HCM.SUBCAP_PROGRAMS, then the possible values are: MLCMLC-IWP AdjustedExecution-basedExecution-based GSSPz/OS-basedReference-basedIf the product ID does not exist in the HCM.SUBCAP_PROGRAMS table, then use Execution-based.If DRL.MVSPM_PROD_T.PROD_OWNER does not contain IBM, then use the DRL.MVSPM_PROD_T.PROD_OWNER value. If the value is null, then set to Non-IBM.Examples of product display names include:5605-DB2_DB2 10 for z/OS_MLC 5635-A02_IMS V11_MLC-IWP Adjusted 5655-W65_WebSphere Application Server for z/OS V8_Execution-based GSSP 5655-DGS_InfoSphere Optim Data Growth Solution for z/OS with Classic Fed V7_z/OS-based 5655-H91_CICS VSAM Recovery V3_Reference-based 5655-XXX_Not Defined_Execution-based XX_XXXX_XX CORPSoftware cost analysisSoftware cost analysis allows you to better manage z/OS software costs and identify where and when workloads need to be adjusted and when more capacity is required.

Product display names

To help you select products easily in report prompts, Capacity Management Analytics shows products using the product display name.

A product display name uses this syntax: [PRODUCT_ID]_[PRODUCT_NAME]_[PRODUCT_PRICING_STRUCTURE].

The components of a product display name are described in the following table.

Table 18. Product display name components.

Display name component
Description

PRODUCT_ID

Is unique per product.

For registered products, the ID is retrieved from DRL. MVSPM_PROD_T.PROD_ID.

For NO89 or unregistered products, the ID is retrieved from HCM.NO89_PRODUCTS.PRODUCT_ID.

PRODUCT_NAME

If DRL.MVSPM_PROD_T.PROD_OWNER contains "IBM®", then get product name from HCM.SUBCAP_PROGRAMS. If this product ID is not found or the value is null in HCM.SUBCAP_PROGRAMS table, then use "Not Defined".

If DRL.MVSPM_PROD_T.PROD_OWNER does not contain "IBM", then get product name from DRL.MVSPM_PROD_T.PROD_NAME.

PRODUCT_PRICING_STRUCTURE

For registered products, if DRL.MVSPM_PROD_T.PROD_OWNER contains "IBM", then check the product ID in HCM.SUBCAP_PROGRAMS. For IBM NO89 or unregistered products, check the product ID in HCM.SUBCAP_PROGRAMS. If the product ID exists in HCM.SUBCAP_PROGRAMS, then the possible values are:

MLC

MLC-IWP Adjusted

Execution-based

Execution-based GSSP

z/OS-based

Reference-based

If the product ID does not exist in the HCM.SUBCAP_PROGRAMS table, then use Execution-based.

If DRL.MVSPM_PROD_T.PROD_OWNER does not contain "IBM", then use the DRL.MVSPM_PROD_T.PROD_OWNER value. If the value is null, then set to "Non-IBM".

Examples of product display names include:

5605-DB2_DB2 10 for z/OS_MLC

5635-A02_IMS V11_MLC-IWP Adjusted

5655-W65_WebSphere Application Server for z/OS® V8_Execution-based GSSP

5655-DGS_InfoSphere Optim™ Data Growth Solution for z/OS with Classic Fed V7_z/OS-based

5655-H91_CICS VSAM Recovery V3_Reference-based

5655-XXX_Not Defined_Execution-based

XX_XXXX_XX CORP

Parent topic: Software cost analysis

z/OS product identificationz/OS products are identified by the product ID in SMF89 which has the z/OS feature.Only 5650-ZOS and 5694-A01 are z/OS products. If you register another product ID and name it as z/OS, Capacity Management Analytics does not consider it as z/OS and it is treated as a normal product in either the MLC or IPLA categories. Accordingly, only 5650-ZOS and 5694-A01 will have zNALC consideration.Software cost analysisSoftware cost analysis allows you to better manage z/OS software costs and identify where and when workloads need to be adjusted and when more capacity is required.

z/OS product identification

z/OS® products are identified by the product ID in SMF89 which has the "z/OS" feature.

Only 5650-ZOS and 5694-A01 are z/OS products. If you register another product ID and name it as "z/OS", Capacity Management Analytics does not consider it as z/OS and it is treated as a normal product in either the MLC or IPLA categories. Accordingly, only 5650-ZOS and 5694-A01 will have zNALC consideration.

Parent topic: Software cost analysis

Historical data support for pricing information and NO89 Product Matrix informationHistorical information is not considered for pricing-related calculations or in the NO89 Product Matrix report.Software cost analysisSoftware cost analysis allows you to better manage z/OS software costs and identify where and when workloads need to be adjusted and when more capacity is required.

Historical data support for pricing information and NO89 Product Matrix information

Historical information is not considered for pricing-related calculations or in the NO89 Product Matrix report.

Parent topic: Software cost analysis

LPAR weight optimizationIBM Capacity Management Analytics uses IBM ILOG CPLEX Optimization Studio to provide optimal LPAR weight values for your environment. The objective of the optimization is to minimize the maximum or sum of the over weight amounts, in terms of % or number of engines, for all LPARs on a CPC while also considering the constraints set out by the relationships among the workload and weight for each LPAR and the total weights for all of the LPARs on the CPC. Weight values for LPARs on a CPC are important when the CPC gets very busy; for example, when the LPARs are over 80% busy overall. The optimization model can help you determine more effective weights for the LPARs. The LPAR weight optimization model uses the data from the SMF records for all the LPARs of the same processor type in the shared pool. The model expects a consistent list of LPARs during all intervals of the day for each date in the date list. The objective is to be able to use days that have the higher peak usage and represent the amount of workload on each LPAR that is expected to be seen repeatedly on those peak days. With 2 to 5 dates of similar peak usage of the same LPARs, the optimizer can better create a best fit set of weights that support the peak usage of all of the LPARs on the CPC. It is important to note that it is not the consistency of the total workload of the LPAR that is important. It is the target workload amount that must fit within the guaranteed weight amount. As you select the dates, ensure that you look at the usage of each LPAR in the MIPS Used - Service Class Period Level report. Each service class is stacked on the chart by its importance level. You can also see the current guaranteed weight. By understanding what business work runs in each service class and what importance level it is running at, you can determine what portion of the total workload usage on the LPAR that you want to ensure has enough guaranteed capacity to complete when it wants to (this is also known as the demand workload). The other amount of workload above the target amount is considered deferrable.To use the LPAR weight optimization program, you must use Java version 7. Java version 7 is provided with SPSS Modeler 17. If you are using SPSS Modeler 16, you must install Java version 7.For more information about the LPAR weight optimization report, see CPU: LPAR Weight Optimization Run ResultThis report shows the available LPAR Weight Optimization runs. It also shows the input settings and optimized weight results for a run selected for each CPC and processor type..Optimization information is also available in the CPU: MIPS Used - LPAR Level by WLM ImportanceThis report shows the observed average or peak CPU MIPS used at the LPAR level and the forecasted average or peak with their confidence intervals. The CPU MIPS used (both observed and forecasted) can be displayed for each processor type at each interval band. The observed CPU MIPS used is also broken down by the WLM importance level. The optimized LPAR weight and the target workload, according to the settings in the selected optimization run, can be also displayed. and CPU: Over/Under Share Weight - CPC by LPARThis report shows the stacked usage of each LPAR against the total number of engines of each type in the shared pool of a CPC. It also shows the usage percentage over or under its guaranteed share of each LPAR, according to both the observed and optimized weights, due to the competition among LPARs. The target workload, according to the selected optimization run, can also be displayed. reports.Creating optimization programs IBM Capacity Management Analytics provides an optimization program for LPAR weights. You can create your own optimization programs by using IBM ILOG CPLEX Optimization Studio. For more information about using Optimization Studio, see the IBM ILOG CPLEX Optimization Studio Getting Started with CPLEX guide (www.ibm.com/support/knowledgecenter/SSSA5P_12.6.1/ilog.odms.studio.help/pdf/gscplex.pdf). When you create an optimization program in the version of ILOG CPLEX Optimization Studio that is provided with Capacity Management Analytics, you can use up to 1000 variables and 1000 constraints. You must ensure that you are within this limitation before you run the program to solve the problem. You can include a check and an audit in the program, and if the number of variables or constraints is over 1000, then the programs must stop. If you must use more than 1000 variables or constraints, you can purchase the full version of IBM ILOG CPLEX Optimization Studio. You can use the following formula to calculate the number of constraints and variables: # of constraints = IloCplex.getNcols + IloCplex.getNrows + IloCplex.getNQCs + IloCplex.getNSOSs Where: IloCplex.getNrows queries the number of rows of A (that is, the number of linear constraints)IloCplex.getNQCs queries the number of quadratic constraintsIloCplex.getNcols queries the number of special ordered sets (SOSs) # of variables = IloCplex.getNcols Where: IloCplex.getNcols queries the number of columns of A (that is, the number of variables) For more information about these classes, see the ILOG CPLEX documentation (www.ibm.com/support/knowledgecenter/SSSA5P_12.5.1/ilog.odms.cplex.help/refjavacplex/html/ilog/cplex/IloCplex.html). More documentation about classes is provided with the product installation. This documentation is available in CMA_ROOT/documents/optimizations/index.html.Choosing dates for the optimization Selecting the dates to use as input involves looking at the target amount of workload for each LPAR on the CPC and ensuring that the dates that you select best represent the peak and the competition among LPARs that you want optimized. For example, Monday is the peak day of the week for having multiple applications that are running on the CPC. And a holiday falls on a Monday so the applications are not nearly as busy as they would be on a regular workday. Therefore, choosing four recent Mondays but avoiding any Monday that was a holiday could be a good set of dates to use for the optimization. Or, say that your application peak is even higher during the first of each quarter and Monday is the busiest day of the week for your applications. If your last quarter did not perform as desired during the peak Mondays and you expect the next quarter to be similar and want to have the optimizer provide a different set of weight values for the competing LPARs. You could choose one or 2 of the Mondays that occurred during that last quarter. Also, keep in mind that any day that had an abnormally high amount of target workload, which is not expected to be normal, should not be included in the date list. The optimizer attempts to support that full amount of target workload under the weight value and this impacts the other LPARs ability to fit their target workload under the weight value. The reverse is also true. If you have weight values set such that an abnormally large amount of total workload was able to run on one LPAR and thus forcing another LPAR's target workload to not be able to fit within its weight. This means that the target workload amount that was accomplished is lower than what is expected to occur. You don’t want to include that date in the date list as it is not representative of the amount of work you want to optimize for.Objectives and constraintsThe LPAR weight optimization model produces optimized weights based on your objectives and within the bounds of your constraints.Configuring input files for LPAR weight optimizationTo run the IBM Capacity Management Analytics LPAR weight optimization scripts, you must provide input data in separate CSV files.

LPAR weight optimization

IBM® Capacity Management Analytics uses IBM ILOG® CPLEX® Optimization Studio to provide optimal LPAR weight values for your environment.

The objective of the optimization is to minimize the maximum or sum of the over weight amounts, in terms of % or number of engines, for all LPARs on a CPC while also considering the constraints set out by the relationships among the workload and weight for each LPAR and the total weights for all of the LPARs on the CPC.

Weight values for LPARs on a CPC are important when the CPC gets very busy; for example, when the LPARs are over 80% busy overall. The optimization model can help you determine more effective weights for the LPARs.

The LPAR weight optimization model uses the data from the SMF records for all the LPARs of the same processor type in the shared pool. The model expects a consistent list of LPARs during all intervals of the day for each date in the date list. The objective is to be able to use days that have the higher peak usage and represent the amount of workload on each LPAR that is expected to be seen repeatedly on those peak days. With 2 to 5 dates of similar peak usage of the same LPARs, the optimizer can better create a best fit set of weights that support the peak usage of all of the LPARs on the CPC.

It is important to note that it is not the consistency of the total workload of the LPAR that is important. It is the target workload amount that must fit within the guaranteed weight amount. As you select the dates, ensure that you look at the usage of each LPAR in the MIPS Used - Service Class Period Level report. Each service class is stacked on the chart by its importance level. You can also see the current guaranteed weight. By understanding what business work runs in each service class and what importance level it is running at, you can determine what portion of the total workload usage on the LPAR that you want to ensure has enough guaranteed capacity to complete when it wants to (this is also known as the demand workload). The other amount of workload above the target amount is considered deferrable.

Note: To use the LPAR weight optimization program, you must use Java version 7. Java version 7 is provided with SPSS Modeler 17. If you are using SPSS Modeler 16, you must install Java version 7.

For more information about the LPAR weight optimization report, see CPU: LPAR Weight Optimization Run Result.

Optimization information is also available in the CPU: MIPS Used - LPAR Level by WLM Importance and CPU: Over/Under Share Weight - CPC by LPAR reports.

Creating optimization programs

IBM Capacity Management Analytics provides an optimization program for LPAR weights. You can create your own optimization programs by using IBM ILOG CPLEX Optimization Studio. For more information about using Optimization Studio, see the IBM ILOG CPLEX Optimization Studio Getting Started with CPLEX guide (www.ibm.com/support/knowledgecenter/SSSA5P_12.6.1/ilog.odms.studio.help/pdf/gscplex.pdf).

Important: When you create an optimization program in the version of ILOG CPLEX Optimization Studio that is provided with Capacity Management Analytics, you can use up to 1000 variables and 1000 constraints. You must ensure that you are within this limitation before you run the program to solve the problem. You can include a check and an audit in the program, and if the number of variables or constraints is over 1000, then the programs must stop.

If you must use more than 1000 variables or constraints, you can purchase the full version of IBM ILOG CPLEX Optimization Studio.

You can use the following formula to calculate the number of constraints and variables:

# of constraints = IloCplex.getNcols + IloCplex.getNrows + IloCplex.getNQCs + IloCplex.getNSOSs

Where:

IloCplex.getNrows queries the number of rows of A (that is, the number of linear constraints)

IloCplex.getNQCs queries the number of quadratic constraints

IloCplex.getNcols queries the number of special ordered sets (SOSs)

# of variables = IloCplex.getNcols

Where:

IloCplex.getNcols queries the number of columns of A (that is, the number of variables)

For more information about these classes, see the ILOG CPLEX documentation (www.ibm.com/support/knowledgecenter/SSSA5P_12.5.1/ilog.odms.cplex.help/refjavacplex/html/ilog/cplex/IloCplex.html).

More documentation about classes is provided with the product installation. This documentation is available in CMA_ROOT/documents/optimizations/index.html.

Choosing dates for the optimization

Selecting the dates to use as input involves looking at the target amount of workload for each LPAR on the CPC and ensuring that the dates that you select best represent the peak and the competition among LPARs that you want optimized.

For example, Monday is the peak day of the week for having multiple applications that are running on the CPC. And a holiday falls on a Monday so the applications are not nearly as busy as they would be on a regular workday. Therefore, choosing four recent Mondays but avoiding any Monday that was a holiday could be a good set of dates to use for the optimization. Or, say that your application peak is even higher during the first of each quarter and Monday is the busiest day of the week for your applications. If your last quarter did not perform as desired during the peak Mondays and you expect the next quarter to be similar and want to have the optimizer provide a different set of weight values for the competing LPARs. You could choose one or 2 of the Mondays that occurred during that last quarter.

Also, keep in mind that any day that had an abnormally high amount of target workload, which is not expected to be normal, should not be included in the date list. The optimizer attempts to support that full amount of target workload under the weight value and this impacts the other LPARs ability to fit their target workload under the weight value.

The reverse is also true. If you have weight values set such that an abnormally large amount of total workload was able to run on one LPAR and thus forcing another LPAR's target workload to not be able to fit within its weight. This means that the target workload amount that was accomplished is lower than what is expected to occur. You don’t want to include that date in the date list as it is not representative of the amount of work you want to optimize for.

Objectives and constraints
The LPAR weight optimization model produces optimized weights based on your objectives and within the bounds of your constraints.

Configuring input files for LPAR weight optimization
To run the IBM Capacity Management Analytics LPAR weight optimization scripts, you must provide input data in separate CSV files.

Objectives and constraints The LPAR weight optimization model produces optimized weights based on your objectives and within the bounds of your constraints.ObjectivesThe objective of the optimization is to find the best weight value for each LPAR within a CPC. For example:Minimize (max_over_target_weight * max_over_target_lpar_weight + max_over_total_weight * max_over_total_lpar_weight + sum_over_target_weight * sum_over_target_lpar_weight + sum_over_total_weight * sum_over_total_lpar_weight)Where:max_over_target_weightThe weight or punishment for the sub-objective max_over_target_lpar_weight. The higher the value, the more important the sub-objective is.max_over_target_lpar_weightThe total maximum amount in % of the total physical shared CPUs that the target workload is over the guaranteed capacity that is set by LPAR weight on all LPARs.max_over_total_weightThe weight or punishment for the sub-objective max_over_total_lpar_weight. The higher the value, the more important the sub-objective is.max_over_total_lpar_weightThe total maximum amount in % of the total physical shared CPUs that the total workload is over the guaranteed capacity that is set by LPAR weight on all LPARs.sum_over_target_weightThe weight or punishment for the sub-objective sum_over_target_lpar_weight. The higher the value is, the more important the sub-objective is.sum_over_target_lpar_weightThe total amount in % of the total physical shared CPUs that the target workload is over the guaranteed capacity that is set by LPAR weight on all LPARs at all hours and on all days.sum_over_total_weightThe weight or punishment for the sub-objective sum_over_total_lpar_weight. The higher the value, the more important the sub-objective is.sum_over_total_lpar_weightThe total amount in % of the total physical shared CPUs that the total workload is over the guaranteed capacity that is set by LPAR weight on all LPARs at all hours and on all days.Constraints The basic constraints that are used in the optimization are the relationships between the workload and weight, and the 100% of total weights. Objective related constraintsct_sum_target_wl_over_weight presents the relationship between the decision expression sum_over_target_lpar_weight (used in objective) and the decision variable over_target_lpar_weight. sum_over_target_lpar_weight is the sum of over_target_lpar_weight for all LPARs at all hours and on all days.ct_sum_total_wl_over_weight presents the relationship between the decision expression sum_over_total_lpar_weight (used in objective) and the decision variable over_total_lpar_weight. sum_over_total_lpar_weight is the sum of over_total_lpar_weight for all LPARs at all hours and on all days.ct_max_target_wl_over_weight presents the relationship among the decision expression max_over_target_lpar_weight (used in objective), the decision variable max_over_target_lpar_weight, and the input data lpar_priority. max_over_target_lpar_weight is the sum of max_over_target_lpar_weight times lpar_priority for all LPARs.ct_max_total_wl_over_weight presents the relationship among the decision expression max_over_total_lpar_weight (used in objective), the decision variable max_over_total_lpar_weight, and the input data lpar_priority. max_over_total_lpar_weight is the sum of max_over_total_lpar_weight times lpar_priority for all LPARs.Relationship constraintsct_target_wl_over_under_weight presents the relationship between the target workload and the LPAR weight on each LPAR at each hour of each day. When the target workload is higher than the LPAR weight, then the decision variable over_target_lpar_weight gets the over value. When the workload is less than the weight, then the decision variable under_target_lpar_weight gets the under value. The over values are used in objective.ct_total_wl_over_under_weight presents the relationship between the total workload and the LPAR weight on each LPAR at each hour of each day. When the total workload is higher than the LPAR weight, then the decision variable over_total_lpar_weight gets the over value. When the workload is less than the weight, then the decision variable under_total_lpar_weight variable gets the under value. The over values are used in objective.ct_max_target_wl_over_weight_per_lpar presents the relationship between the decision variable max_over_target_lpar_weight and the decision variable over_target_lpar_weight. max_over_target_lpar_weight is the maximum value of over_target_lpar_weight for each LPAR across all hours and days.ct_max_total_wl_over_weight_per_lpar presents the relationship between the decision variable max_over_total_lpar_weight and the decision variable over_total_lpar_weight. max_over_total_lpar_weight is the maximum value of over_total_lpar_weight for each LPAR across all hours and days.Total weight constraintsct_sum_weight presents the relationship among the weight of all LPARs. The total weight of all LPARs should be 100%.Logical engine number constraintct_weight_lessthan_logic_proc presents the relationship between the guaranteed capacity set by LPAR weight and logical processor capacity. The LPAR weight in engine numbers should not be more than the logical process numbers set for the LPAR.Fixed weight constraintct_fixed_weight fixes the LPAR weight for an LPAR if the user provides a fixed_weight to this LPAR in the input file lwo_parameters_per_lpar.LPAR weight optimizationIBM Capacity Management Analytics uses IBM ILOG CPLEX Optimization Studio to provide optimal LPAR weight values for your environment.

Objectives and constraints

The LPAR weight optimization model produces optimized weights based on your objectives and within the bounds of your constraints.

Objectives

The objective of the optimization is to find the best weight value for each LPAR within a CPC. For example:

Minimize (max_over_target_weight * max_over_target_lpar_weight +
      max_over_total_weight * max_over_total_lpar_weight +
      sum_over_target_weight * sum_over_target_lpar_weight +
      sum_over_total_weight * sum_over_total_lpar_weight)

Where:

max_over_target_weight

The weight or punishment for the sub-objective max_over_target_lpar_weight. The higher the value, the more important the sub-objective is.

max_over_target_lpar_weight

The total maximum amount in % of the total physical shared CPUs that the target workload is over the guaranteed capacity that is set by LPAR weight on all LPARs.

max_over_total_weight

The weight or punishment for the sub-objective max_over_total_lpar_weight. The higher the value, the more important the sub-objective is.

max_over_total_lpar_weight

The total maximum amount in % of the total physical shared CPUs that the total workload is over the guaranteed capacity that is set by LPAR weight on all LPARs.

sum_over_target_weight

The weight or punishment for the sub-objective sum_over_target_lpar_weight. The higher the value is, the more important the sub-objective is.

sum_over_target_lpar_weight

The total amount in % of the total physical shared CPUs that the target workload is over the guaranteed capacity that is set by LPAR weight on all LPARs at all hours and on all days.

sum_over_total_weight

The weight or punishment for the sub-objective sum_over_total_lpar_weight. The higher the value, the more important the sub-objective is.

sum_over_total_lpar_weight

The total amount in % of the total physical shared CPUs that the total workload is over the guaranteed capacity that is set by LPAR weight on all LPARs at all hours and on all days.

Constraints

The basic constraints that are used in the optimization are the relationships between the workload and weight, and the 100% of total weights.

Objective related constraints

ct_sum_target_wl_over_weight presents the relationship between the decision expression sum_over_target_lpar_weight (used in objective) and the decision variable over_target_lpar_weight. sum_over_target_lpar_weight is the sum of over_target_lpar_weight for all LPARs at all hours and on all days.

ct_sum_total_wl_over_weight presents the relationship between the decision expression sum_over_total_lpar_weight (used in objective) and the decision variable over_total_lpar_weight. sum_over_total_lpar_weight is the sum of over_total_lpar_weight for all LPARs at all hours and on all days.

ct_max_target_wl_over_weight presents the relationship among the decision expression max_over_target_lpar_weight (used in objective), the decision variable max_over_target_lpar_weight, and the input data lpar_priority. max_over_target_lpar_weight is the sum of max_over_target_lpar_weight times lpar_priority for all LPARs.

ct_max_total_wl_over_weight presents the relationship among the decision expression max_over_total_lpar_weight (used in objective), the decision variable max_over_total_lpar_weight, and the input data lpar_priority. max_over_total_lpar_weight is the sum of max_over_total_lpar_weight times lpar_priority for all LPARs.

Relationship constraints

ct_target_wl_over_under_weight presents the relationship between the target workload and the LPAR weight on each LPAR at each hour of each day. When the target workload is higher than the LPAR weight, then the decision variable over_target_lpar_weight gets the over value. When the workload is less than the weight, then the decision variable under_target_lpar_weight gets the under value. The over values are used in objective.

ct_total_wl_over_under_weight presents the relationship between the total workload and the LPAR weight on each LPAR at each hour of each day. When the total workload is higher than the LPAR weight, then the decision variable over_total_lpar_weight gets the over value. When the workload is less than the weight, then the decision variable under_total_lpar_weight variable gets the under value. The over values are used in objective.

ct_max_target_wl_over_weight_per_lpar presents the relationship between the decision variable max_over_target_lpar_weight and the decision variable over_target_lpar_weight. max_over_target_lpar_weight is the maximum value of over_target_lpar_weight for each LPAR across all hours and days.

ct_max_total_wl_over_weight_per_lpar presents the relationship between the decision variable max_over_total_lpar_weight and the decision variable over_total_lpar_weight. max_over_total_lpar_weight is the maximum value of over_total_lpar_weight for each LPAR across all hours and days.

Total weight constraints

ct_sum_weight presents the relationship among the weight of all LPARs. The total weight of all LPARs should be 100%.

Logical engine number constraint

ct_weight_lessthan_logic_proc presents the relationship between the guaranteed capacity set by LPAR weight and logical processor capacity. The LPAR weight in engine numbers should not be more than the logical process numbers set for the LPAR.

Fixed weight constraint

ct_fixed_weight fixes the LPAR weight for an LPAR if the user provides a fixed_weight to this LPAR in the input file lwo_parameters_per_lpar.

Parent topic: LPAR weight optimization

Configuring input files for LPAR weight optimizationTo run the IBM Capacity Management Analytics LPAR weight optimization scripts, you must provide input data in separate CSV files. The files that you must provide inputs in are: lwo_parameters.csvThis file contains the following fields: EXECUTION_NAME—A name for the optimization run. The default value is modelname_timestamp. The optimization records are recognized by execution_name, processor_type, and cpu_serial_no.HOUR_START—The start time for the run. An integer number between 0-23.HOUR_END—The end time for the run. An integer number between 0-23. This value should be greater than or equal to the HOUR_START value. HOUR_END means another full hour. For example, if HOUR_START = 1 and HOUR_END = 13, then the time range is 01:00:00 - 13:59:59.CPU_SERIAL_NO—An identifier for the CPC. The last 4 digits of the CPU serial number taken from SMF70SER. This value can contain only English and numeric characters.PROCESSOR_TYPE—The name of the logical processor type. This value can be CP, zIIP, zAAP, or IFL.FILE_NAME_MODEL_PARA—The path and file name for the model parameter file. For example, CMAINSTANCE/data/lwo_parameters_model.csv. If the model parameter file is in the default location, you do not need the path.FILE_NAME_DATE_LIST—The path and file name for the date parameter file. For example, CMAINSTANCE/data/lwo_parameters_date_list.csv. If the model parameter file is in the default location, you do not need the path.FILE_NAME_PARA_PER_LPAR—The path and file name for the model parameter file. For example, CMAINSTANCE/data/lwo_parameters_per_lpar.csv. If the model parameter file is in the default location, you do not need the path.lwo_parameters_model.csvThis file contains the sub-objective names and weight values for the optimization run. The sub-objective names include MAX_OVER_TARGET_WEIGHT, MAX_OVER_TOTAL_WEIGHT, SUM_OVER_TARGET_WEIGHT, and SUM_OVER_TOTAL_WEIGHT. The sub-objective weight values can be any positive number, where the larger the number means a higher priority.lwo_parameters_date_list.csvThis file contains the dates to use as inputs for the optimization run. The dates should be representative of the usage you are trying to optimize. For more information, see Choosing dates for the optimization.lwo_parameters_per_lpar.csvThis file contains the following fields: LPAR_NAME The name for each LPAR to be optimized.LPAR_PRIORITY The current priority for the LPAR. This value should be a positive integer number. The larger the number, the higher the priority. The default value is 1.FIXED_WEIGHT The fixed weight assigned to the LPAR, if any. This value is the percentage, such as 0.25 (25%), of the shared processor pool that you want the LPAR to have. This value can be a number between 0-1.TARGET_WORKLOAD_IMPORTANCE_LEVEL This value can be an integer number between 0-6 if the target workload of this LPAR is set by the WLM importance level from the SMF72 records. This value is null if the target workload of this LPAR is not set by the total LPAR CPU usage from the SMF70 records. This value is used to calculate the target workload.TARGET_WORKLOAD_PERCENT A number between 0-1. The default value is 1. For example, 0.25 is 25%. This value is used to calculate the target workload. The target workload is calculated by the value in the TARGET_WORKLOAD_IMPORTANCE_LEVEL field with the value in the TARGET_WORKLOAD_PERCENT field. If TARGET_WORKLOAD_IMPORTANCE_LEVEL is an integer number between 0-6, then the target workload = workload under the TARGET_WORKLOAD_IMPORTANCE_LEVEL + workload of the TARGET_WORKLOAD_IMPORTANCE_LEVEL * TARGET_WORKLOAD_PERCENT. For example, if the TARGET_WORKLOAD_IMPORTANCE_LEVEL is 5, and TARGET_WORKLOAD_PERCENT is 80%, then the target workload = (workload of WLM importance level: 0 - 4) + ((workload of WLM importance level: 5) * 80%). If TARGET_WORKLOAD_IMPORTANCE_LEVEL is null, then the target workload = LPAR CPU usage * TARGET_WORKLOAD_PERCENT. Target workload calculation examplesTARGET_WORKLOAD_ IMPORTANCE_LEVELTARGET_WORKLOAD_ PERCENTTarget workload50.2= sum of (imp level 0-4)'s workload + (20% imp level 5)'s workload60.2= sum of (imp level 0-5)'s workload + (20% imp level 6)'s workload60= sum of (imp level 0-5)'s workload61= sum of (imp level 0-6)'s workloadnull0.8= total workload * 80%Go to the CMAINSTANCE/data directory.Open and edit the contents of the lwo_parameters.csv, lwo_parameters_model.csv, lwo_parameters_date_list.csv, and lwo_parameters_per_lpar.csv files.Save and close the files.Update the files you have changed to read only.LPAR weight optimizationIBM Capacity Management Analytics uses IBM ILOG CPLEX Optimization Studio to provide optimal LPAR weight values for your environment.

Configuring input files for LPAR weight optimization

To run the IBM® Capacity Management Analytics LPAR weight optimization scripts, you must provide input data in separate CSV files.

The files that you must provide inputs in are:

lwo_parameters.csv

This file contains the following fields:

EXECUTION_NAME—A name for the optimization run. The default value is modelname_timestamp. The optimization records are recognized by execution_name, processor_type, and cpu_serial_no.

HOUR_START—The start time for the run. An integer number between 0-23.

HOUR_END—The end time for the run. An integer number between 0-23. This value should be greater than or equal to the HOUR_START value.

HOUR_END means another full hour. For example, if HOUR_START = 1 and HOUR_END = 13, then the time range is 01:00:00 - 13:59:59.

CPU_SERIAL_NO—An identifier for the CPC. The last 4 digits of the CPU serial number taken from SMF70SER. This value can contain only English and numeric characters.

PROCESSOR_TYPE—The name of the logical processor type. This value can be CP, zIIP, zAAP, or IFL.

FILE_NAME_MODEL_PARA—The path and file name for the model parameter file. For example, CMAINSTANCE/data/lwo_parameters_model.csv. If the model parameter file is in the default location, you do not need the path.

FILE_NAME_DATE_LIST—The path and file name for the date parameter file. For example, CMAINSTANCE/data/lwo_parameters_date_list.csv. If the model parameter file is in the default location, you do not need the path.

FILE_NAME_PARA_PER_LPAR—The path and file name for the model parameter file. For example, CMAINSTANCE/data/lwo_parameters_per_lpar.csv. If the model parameter file is in the default location, you do not need the path.

lwo_parameters_model.csv

This file contains the sub-objective names and weight values for the optimization run. The sub-objective names include MAX_OVER_TARGET_WEIGHT, MAX_OVER_TOTAL_WEIGHT, SUM_OVER_TARGET_WEIGHT, and SUM_OVER_TOTAL_WEIGHT. The sub-objective weight values can be any positive number, where the larger the number means a higher priority.

lwo_parameters_date_list.csv

This file contains the dates to use as inputs for the optimization run. The dates should be representative of the usage you are trying to optimize. For more information, see Choosing dates for the optimization.

lwo_parameters_per_lpar.csv

This file contains the following fields:

LPAR_NAME

The name for each LPAR to be optimized.

LPAR_PRIORITY

The current priority for the LPAR. This value should be a positive integer number. The larger the number, the higher the priority. The default value is 1.

FIXED_WEIGHT

The fixed weight assigned to the LPAR, if any. This value is the percentage, such as 0.25 (25%), of the shared processor pool that you want the LPAR to have. This value can be a number between 0-1.

TARGET_WORKLOAD_IMPORTANCE_LEVEL

This value can be an integer number between 0-6 if the target workload of this LPAR is set by the WLM importance level from the SMF72 records.

This value is null if the target workload of this LPAR is not set by the total LPAR CPU usage from the SMF70 records.

This value is used to calculate the target workload.

TARGET_WORKLOAD_PERCENT

A number between 0-1. The default value is 1. For example, 0.25 is 25%.

This value is used to calculate the target workload.

The target workload is calculated by the value in the TARGET_WORKLOAD_IMPORTANCE_LEVEL field with the value in the TARGET_WORKLOAD_PERCENT field.

If TARGET_WORKLOAD_IMPORTANCE_LEVEL is an integer number between 0-6, then the target workload = workload under the TARGET_WORKLOAD_IMPORTANCE_LEVEL + workload of the TARGET_WORKLOAD_IMPORTANCE_LEVEL * TARGET_WORKLOAD_PERCENT.

For example, if the TARGET_WORKLOAD_IMPORTANCE_LEVEL is 5, and TARGET_WORKLOAD_PERCENT is 80%, then the target workload = (workload of WLM importance level: 0 - 4) + ((workload of WLM importance level: 5) * 80%).

If TARGET_WORKLOAD_IMPORTANCE_LEVEL is null, then the target workload = LPAR CPU usage * TARGET_WORKLOAD_PERCENT.

Target workload calculation examples

TARGET_WORKLOAD_ IMPORTANCE_LEVEL | TARGET_WORKLOAD_ PERCENT | Target workload
5 | 0.2 | = sum of (imp level 0-4)'s workload + (20% imp level 5)'s workload
6 | 0.2 | = sum of (imp level 0-5)'s workload + (20% imp level 6)'s workload
6 | 0 | = sum of (imp level 0-5)'s workload
6 | 1 | = sum of (imp level 0-6)'s workload
null | 0.8 | = total workload * 80%

Procedure

Go to the CMAINSTANCE/data directory.

Open and edit the contents of the lwo_parameters.csv, lwo_parameters_model.csv, lwo_parameters_date_list.csv, and lwo_parameters_per_lpar.csv files.

Save and close the files.

Update the files you have changed to read only.

Parent topic: LPAR weight optimization

SPSS Modeler stream referenceMuch of the data in IBM Capacity Management Analytics reports and workspaces is processed using IBM SPSS Modeler. This software uses programs that are referred to as streams, which contain the instructions required to retrieve, prepare, model, and forecast the data.MIPS forecasting streamSoftware Cost Analysis forecasting streamsCICS anomaly detection streamsCustomer Information Control System (CICS) anomaly detection helps to improve system management by proactively detecting anomalies in forecast capacity so that you can identify issues before they affect the business.Billable MSU optimization streamThis stream is used to determine how to rearrange qualified products, or parts of products, among LPARs to lower the total cost of the billable MSUs. A qualified product is an IBM product that can produce billable MSUs in the forecasted data. If the forecasted MSU usage for a product is zero or if it is not an IBM product, then it is not included in the optimization.Application average forecast streamThe appl_avg_hourly_forecast_timeseries.str and appl_avg_daily_forecast_timeseries.str streams are used to predict the application future values of CP_SECONDS, IFA_ SECONDS, and IIP_ SECONDS for hourly and daily levels. The Application Analytics reports use the forecast data that is provided by these streams to calculate MIPS values.Application peak value forecast streamThe application peak value forecast stream (appl_peak_daily_forecast_timeseries.str) analyzes a defined time range of the history data for CPU seconds, calculates its peak value of MIPS, and forecast its one year peak value at an application level for the future.Mapping table definitions for application analytics reportingThe application analytics reports categorize the application information by using mapping tables. The application mapping is defined in two spreadsheet files that are provided with the Capacity Management Analytics Solution Kit installation. one file (appl_lob_z.csv) is used for z/OS systems. The other (appl_lob.csv) is used for distributed systems.

SPSS Modeler stream reference

Much of the data in IBM® Capacity Management Analytics reports and workspaces is processed using IBM SPSS® Modeler. This software uses programs that are referred to as "streams," which contain the instructions required to retrieve, prepare, model, and forecast the data.

MIPS forecasting stream

Software Cost Analysis forecasting streams

CICS anomaly detection streams
Customer Information Control System (CICS®) anomaly detection helps to improve system management by proactively detecting anomalies in forecast capacity so that you can identify issues before they affect the business.

Billable MSU optimization stream
This stream is used to determine how to rearrange qualified products, or parts of products, among LPARs to lower the total cost of the billable MSUs. A qualified product is an IBM product that can produce billable MSUs in the forecasted data. If the forecasted MSU usage for a product is zero or if it is not an IBM product, then it is not included in the optimization.

Application average forecast stream
The appl_avg_hourly_forecast_timeseries.str and appl_avg_daily_forecast_timeseries.str streams are used to predict the application future values of CP_SECONDS, IFA_ SECONDS, and IIP_ SECONDS for hourly and daily levels. The Application Analytics reports use the forecast data that is provided by these streams to calculate MIPS values.

Application peak value forecast stream
The application peak value forecast stream (appl_peak_daily_forecast_timeseries.str) analyzes a defined time range of the history data for CPU seconds, calculates its peak value of MIPS, and forecast its one year peak value at an application level for the future.

Mapping table definitions for application analytics reporting
The application analytics reports categorize the application information by using mapping tables. The application mapping is defined in two spreadsheet files that are provided with the Capacity Management Analytics Solution Kit installation. one file (appl_lob_z.csv) is used for z/OS® systems. The other (appl_lob.csv) is used for distributed systems.

MIPS forecasting streamThe predicted scores for CPU_BUSY_MIPS shown in the reports that include forecasts are produced using the MIPS forecasting stream.SPSS Modeler stream referenceMuch of the data in IBM Capacity Management Analytics reports and workspaces is processed using IBM SPSS Modeler. This software uses programs that are referred to as streams, which contain the instructions required to retrieve, prepare, model, and forecast the data.Creating forecastsThe process of creating the forecast values that are provided as part of the IBM Capacity Management Analytics solution follows a standard approach used to forecast future values of things like stock prices, interest rates, and population. The process does not need to be understood at a technical level to make use of the predictions provided. The description of this process focuses on the how the data is handled and provides some basic information on evaluating the accuracy and utility of the predictions.The evaluation of modelsAs models are produced independently for each LPAR/PROCESSOR TYPE combination, they need to be evaluated separately to validate their utility. Details for hourly forecastsTime Interval node settings for the hourly forecast are based on a 24x7 time period. Note that the Time Interval is set to Hours per day and the timestamp field provides the information necessary to identify the date, day of the week, and hour of the day.Details for monthly forecastsTime Interval node settings for the monthly forecast is set to months and the date field provides the information necessary to assign each row of data to the appropriate month.Details for the CPC daily peak forecast streamThe CPC daily peak forecast stream (mvspm-cpc-daily-peak-forecast-timeseries.str) is used to produce daily CPU_BUSY_MIPS :)peak forecasts at the CPC level. The stream analyzes a defined time range of the historical data, calculates its peak value of MIPS, and forecasts its one year peak value at the CPC level for the future.Details for LPAR peak daily forecast streamThe LPAR peak daily forecast stream uses the historical daily peak MIPS value for each CPC, LPAR, and processor type combination and forecasts the daily peak MIPS for one year into the future. The stream requires at least 60 days of continuous data, without any missing values, to do the forecasting. Error handling for model buildingIf an error is encountered while building a model for a specific LPAR/PROCESSOR TYPE combination, tables are generated that contain details related to the error.

MIPS forecasting stream

The predicted scores for CPU_BUSY_MIPS shown in the reports that include forecasts are produced using the MIPS forecasting stream.

Creating forecasts
The process of creating the forecast values that are provided as part of the IBM Capacity Management Analytics solution follows a standard approach used to forecast future values of things like stock prices, interest rates, and population. The process does not need to be understood at a technical level to make use of the predictions provided. The description of this process focuses on the how the data is handled and provides some basic information on evaluating the accuracy and utility of the predictions.

The evaluation of models
As models are produced independently for each LPAR/PROCESSOR TYPE combination, they need to be evaluated separately to validate their utility.

Details for hourly forecasts
Time Interval node settings for the hourly forecast are based on a 24x7 time period. Note that the Time Interval is set to Hours per day and the timestamp field provides the information necessary to identify the date, day of the week, and hour of the day.

Details for monthly forecasts
Time Interval node settings for the monthly forecast is set to months and the date field provides the information necessary to assign each row of data to the appropriate month.

Details for the CPC daily peak forecast stream
The CPC daily peak forecast stream (mvspm-cpc-daily-peak-forecast-timeseries.str) is used to produce daily CPU_BUSY_MIPS :)peak forecasts at the CPC level. The stream analyzes a defined time range of the historical data, calculates its peak value of MIPS, and forecasts its one year peak value at the CPC level for the future.

Details for LPAR peak daily forecast stream
The LPAR peak daily forecast stream uses the historical daily peak MIPS value for each CPC, LPAR, and processor type combination and forecasts the daily peak MIPS for one year into the future. The stream requires at least 60 days of continuous data, without any missing values, to do the forecasting.

Error handling for model building
If an error is encountered while building a model for a specific LPAR/PROCESSOR TYPE combination, tables are generated that contain details related to the error.

Parent topic: SPSS Modeler stream reference

Creating forecastsThe process of creating the forecast values that are provided as part of the IBM Capacity Management Analytics solution follows a standard approach used to forecast future values of things like stock prices, interest rates, and population. The process does not need to be understood at a technical level to make use of the predictions provided. The description of this process focuses on the how the data is handled and provides some basic information on evaluating the accuracy and utility of the predictions.SPSScreating forecastsThere are four basic tasks involved in building the forecasts for each LPAR/Processor type combination found on your system.Preparing the data by aggregating it up to the appropriate levelDefining the time period for the forecast, including the number of rows to be used for validation of the predictionsBuilding the actual forecast modelSaving the predicted scores for use in reportingFor more information about IBM SPSS Modeler, including procedural information on modifying streams, see the product documentation (pic.dhe.ibm.com/infocenter/spssmodl/v15r0m0/index.jsp).MIPS forecasting streamData aggregationThe forecasts provided as part of the IBM Capacity Management Analytics solution are produced using the IBM SPSS Modeler application. The program code is contained in streams, including the stream used to produce the Capacity Management Analytics daily forecast. There are corresponding streams for the hourly and monthly forecasts which are similar except for the level of aggregation used in the data preparation and a few other details.Time period definitionDefining the time period encompasses several distinct tasks, including modifying the holiday.csv file, modifying the Time Interval node, and setting the number of time periods.Forecast model creationThe forecast model is built based on the specifications in the Time Series node in each stream.Predicted scoresThe predictions that are produced by the models are saved to the database and stored in the HCM.MVSPM_LPAR_CPU_BUSY_MIPS_PRED table.

Creating forecasts

The process of creating the forecast values that are provided as part of the IBM® Capacity Management Analytics solution follows a standard approach used to forecast future values of things like stock prices, interest rates, and population. The process does not need to be understood at a technical level to make use of the predictions provided. The description of this process focuses on the how the data is handled and provides some basic information on evaluating the accuracy and utility of the predictions.

There are four basic tasks involved in building the forecasts for each LPAR/Processor type combination found on your system.

Preparing the data by aggregating it up to the appropriate level

Defining the time period for the forecast, including the number of rows to be used for validation of the predictions

Building the actual forecast model

Saving the predicted scores for use in reporting

For more information about IBM SPSS® Modeler, including procedural information on modifying streams, see the product documentation (pic.dhe.ibm.com/infocenter/spssmodl/v15r0m0/index.jsp).

Data aggregation
The forecasts provided as part of the IBM Capacity Management Analytics solution are produced using the IBM SPSS Modeler application. The program code is contained in "streams", including the stream used to produce the Capacity Management Analytics daily forecast. There are corresponding streams for the hourly and monthly forecasts which are similar except for the level of aggregation used in the data preparation and a few other details.

Time period definition
Defining the time period encompasses several distinct tasks, including modifying the holiday.csv file, modifying the Time Interval node, and setting the number of time periods.

Forecast model creation
The forecast model is built based on the specifications in the Time Series node in each stream.

Predicted scores
The predictions that are produced by the models are saved to the database and stored in the HCM.MVSPM_LPAR_CPU_BUSY_MIPS_PRED table.

Parent topic: MIPS forecasting stream

Data aggregationThe forecasts provided as part of the IBM Capacity Management Analytics solution are produced using the IBM SPSS Modeler application. The program code is contained in streams, including the stream used to produce the Capacity Management Analytics daily forecast. There are corresponding streams for the hourly and monthly forecasts which are similar except for the level of aggregation used in the data preparation and a few other details.SPSSaggregating dataThe Daily Forecast streamThe IBM SPSS Modeler stream used to produce the daily forecast is named mvspm-lpar-daily-forecast-timeseries.str. The flow of the program is essentially left to right, following the arrows that connect the nodes that make up the stream, as shown in the following figure. This stream contains a script that drives the process and adds connections dynamically.SPSS Modeler daily forecast streamImage of daily forecast stream.The first step in creating a forecast is to retrieve the data from the database and aggregate it up to the appropriate level. System Management Facilities data is often captured at 10 or 15 minute intervals and needs to be aggregated to the daily level for use in IBM Capacity Management Analytics. The following figure illustrates how this aggregation is configured in SPSS Modeler.Aggregation to the daily level in SPSS ModelerImage of the Roll up to Day dialog box configured to aggregate data at the daily level.Creating forecastsThe process of creating the forecast values that are provided as part of the IBM Capacity Management Analytics solution follows a standard approach used to forecast future values of things like stock prices, interest rates, and population. The process does not need to be understood at a technical level to make use of the predictions provided. The description of this process focuses on the how the data is handled and provides some basic information on evaluating the accuracy and utility of the predictions.

Data aggregation

The forecasts provided as part of the IBM® Capacity Management Analytics solution are produced using the IBM SPSS® Modeler application. The program code is contained in "streams", including the stream used to produce the Capacity Management Analytics daily forecast. There are corresponding streams for the hourly and monthly forecasts which are similar except for the level of aggregation used in the data preparation and a few other details.

The Daily Forecast stream

The IBM SPSS Modeler stream used to produce the daily forecast is named mvspm-lpar-daily-forecast-timeseries.str. The flow of the program is essentially left to right, following the arrows that connect the nodes that make up the stream, as shown in the following figure. This stream contains a script that drives the process and adds connections dynamically.

Figure 8. SPSS Modeler daily forecast stream

The first step in creating a forecast is to retrieve the data from the database and aggregate it up to the appropriate level. System Management Facilities data is often captured at 10 or 15 minute intervals and needs to be aggregated to the daily level for use in IBM Capacity Management Analytics. The following figure illustrates how this aggregation is configured in SPSS Modeler.

Figure 9. Aggregation to the daily level in SPSS Modeler

Parent topic: Creating forecasts

Time period definitionDefining the time period encompasses several distinct tasks, including modifying the holiday.csv file, modifying the Time Interval node, and setting the number of time periods.SPSSdefining time periodModifying the holiday.csv fileThe holiday.csv file is where you identify, by date, important information that is related to demand on your system. This includes information such as holidays when load is typically lower or days of the year when you expect load to be unusually high or low. These high-demand and low-demand dates are days that you can anticipate based on your experience. A low-demand day might be the day before a major holiday. A high-demand day might be a day following a major holiday or a day at the beginning of a month (or quarter) when you do end-of-month reporting for the previous month. Identifying atypical days gives the forecasting algorithm additional information to use in accounting for fluctuations in CPU usage, which can help you to build more accurate models at the hourly and daily level.Date format in holiday.csv should be ISO format. If using a non-ISO format, the stream properties of forecast streams must be updated to match the date format in holiday.csv. If you use Microsoft Excel to edit csv files and your computer uses a different locale than what is used in the SPSS Modeler streams, you must ensure that the data format in the csv remains consistent with date format that is used in the streams. Microsoft Excel can change the locale that is used in the file to match your computer's locale.Modifying the Time Interval nodeThe Time Interval node, which is labeled hold out 30 days in the flow chart of the Daily Forecast stream, sets up the time-based parameters for your models to use. The time interval for modeling is defined on the Intervals tab in this node. Typically this is 7 days a week, as in the following figure. If your data does not include the full 24x7 period, the settings in this node must be modified. For example, for data that includes only weekdays, the Number of days per week field would be 5 and Week begins on option would be set to Monday. Time interval settings in the Time Interval nodeImage of the Time Interval node that is configured for a seven-day week.A hold out period for validation purposes can also be defined in the Time Interval node. In a daily forecast situation, the last 30 days of actual data are held out, meaning that the forecast model does not use this data to build the model. This allows the last 30 days of known CPU usage to be compared with the predicted values from the forecast model. This comparison serves as a means of evaluating how accurately the model predicted results for the last 30 days of CPU usage. This is the best way to determine how well the model will perform in predicting future events.You can modify the hold out period on the Estimation tab of the Time Interval node.Setting the number of time periodsYou set the number of time periods to be forecast in the future on the Forecast tab of the Time Interval node. This instructs the program to generate rows of data for dates in the future beyond the end of the actual data available in your database. In the following image, the time period is days and a full year (365 days) is requested for the forecast. In the hourly and monthly streams, this value is set in hours and months, respectively. The values of the identifier fields such as LPAR_NAME and PROCESSOR_TYPE are taken from the most recent value in the original data. Forecast settings in the Time Interval nodeImage of the Time Interval node that is configured for a 365-day forecast.It is important to note that the ability to forecast into the future is based in part on the amount of data that is used to construct a model. A year or two of data is desirable if the goal is to project daily and monthly usage for a year into the future. If only a few months of data are used to build a model, it is possible that for some LPAR and PROCESSOR TYPE combinations it is impossible to build a usable model.Also, to prevent the forecast processing from stopping due to no usable model output, a check is made to filter out data for certain combinations. If the number of records in input data of a combination is not above a specific threshold (three records for a monthly forecast, 60 records for a daily forecast, and 720 records for an hourly forecast), forecast processing is not performed for that combination to build the model. If there is no model for certain combinations after forecast processing, you should review the input data for the combinations to ensure that the number of records exceeds the threshold at which forecast processing occurs. Creating forecastsThe process of creating the forecast values that are provided as part of the IBM Capacity Management Analytics solution follows a standard approach used to forecast future values of things like stock prices, interest rates, and population. The process does not need to be understood at a technical level to make use of the predictions provided. The description of this process focuses on the how the data is handled and provides some basic information on evaluating the accuracy and utility of the predictions.

Time period definition

Defining the time period encompasses several distinct tasks, including modifying the holiday.csv file, modifying the Time Interval node, and setting the number of time periods.

Modifying the holiday.csv file

The holiday.csv file is where you identify, by date, important information that is related to demand on your system. This includes information such as holidays when load is typically lower or days of the year when you expect load to be unusually high or low. These high-demand and low-demand dates are days that you can anticipate based on your experience. A low-demand day might be the day before a major holiday. A high-demand day might be a day following a major holiday or a day at the beginning of a month (or quarter) when you do end-of-month reporting for the previous month. Identifying atypical days gives the forecasting algorithm additional information to use in accounting for fluctuations in CPU usage, which can help you to build more accurate models at the hourly and daily level.

Note: Date format in holiday.csv should be ISO format. If using a non-ISO format, the stream properties of forecast streams must be updated to match the date format in holiday.csv.

Tip: If you use Microsoft Excel to edit csv files and your computer uses a different locale than what is used in the SPSS® Modeler streams, you must ensure that the data format in the csv remains consistent with date format that is used in the streams. Microsoft Excel can change the locale that is used in the file to match your computer's locale.

Modifying the Time Interval node

The Time Interval node, which is labeled hold out 30 days in the flow chart of the Daily Forecast stream, sets up the time-based parameters for your models to use. The time interval for modeling is defined on the Intervals tab in this node. Typically this is 7 days a week, as in the following figure. If your data does not include the full 24x7 period, the settings in this node must be modified. For example, for data that includes only weekdays, the Number of days per week field would be 5 and Week begins on option would be set to Monday.

Figure 10. Time interval settings in the Time Interval node

A hold out period for validation purposes can also be defined in the Time Interval node. In a daily forecast situation, the last 30 days of actual data are "held out", meaning that the forecast model does not use this data to build the model. This allows the last 30 days of known CPU usage to be compared with the predicted values from the forecast model. This comparison serves as a means of evaluating how accurately the model predicted results for the last 30 days of CPU usage. This is the best way to determine how well the model will perform in predicting future events.

You can modify the hold out period on the Estimation tab of the Time Interval node.

Setting the number of time periods

You set the number of time periods to be forecast in the future on the Forecast tab of the Time Interval node. This instructs the program to generate rows of data for dates in the future beyond the end of the actual data available in your database. In the following image, the time period is days and a full year (365 days) is requested for the forecast. In the hourly and monthly streams, this value is set in hours and months, respectively. The values of the identifier fields such as LPAR_NAME and PROCESSOR_TYPE are taken from the most recent value in the original data.

Figure 11. Forecast settings in the Time Interval node

It is important to note that the ability to forecast into the future is based in part on the amount of data that is used to construct a model. A year or two of data is desirable if the goal is to project daily and monthly usage for a year into the future. If only a few months of data are used to build a model, it is possible that for some LPAR and PROCESSOR TYPE combinations it is impossible to build a usable model.

Also, to prevent the forecast processing from stopping due to no usable model output, a check is made to filter out data for certain combinations. If the number of records in input data of a combination is not above a specific threshold (three records for a monthly forecast, 60 records for a daily forecast, and 720 records for an hourly forecast), forecast processing is not performed for that combination to build the model. If there is no model for certain combinations after forecast processing, you should review the input data for the combinations to ensure that the number of records exceeds the threshold at which forecast processing occurs.

Parent topic: Creating forecasts

Forecast model creationThe forecast model is built based on the specifications in the Time Series node in each stream.SPSSbuilding the forecast modelThe Time Series node is labelled TSModel in the flow chart of the Daily Forecast stream. This node is where the field to be predicted is identified, along with the fields to be used as inputs to the model. In IBM Capacity Management Analytics, CPU_BUSY_MIPS (summed across the relevant rows to the daily, hourly or monthly level) is the field that is predicted. In addition to the Holiday, high, and low fields from the holiday.csv file, the day of week is included as a predictor to be evaluated by the modeling algorithm.The Time Series nodeImage of the Time Series node showing the field to be predicted.The modeling itself is set on the Model tab of Time Series node. Capacity Management Analytics uses the Expert Modeler facility for time series models, set to evaluate Auto Regressive Integrated Moving Average (ARIMA) models and select the one that yields the most accurate result.Running the streamIBM SPSS Modeler streams for the hourly, daily, and monthly program are designed to run in an automated manner. When they run, all of the LPAR and PROCESSOR TYPE combinations found in the data are identified and a separate model is produced for each. These models are used to calculate the forecast for the original data and produce predicted values for the future. Each stream contains a script that manages this process. When the script is started it prompts for parameters that can be modified as necessary to point to the database source and the output directory for storing the models and the output directory for logs. Also, for hourly and daily forecast, an input PATH for holiday.csv is required. The output directories and the input path must exist before the script runs.Prompts for stream parametersImage of prompts for stream parametersThe script itself is unlikely to ever require modification, but you can click Tools, Stream Properties, then Script in SPSS Modeler to view the script.Creating forecastsThe process of creating the forecast values that are provided as part of the IBM Capacity Management Analytics solution follows a standard approach used to forecast future values of things like stock prices, interest rates, and population. The process does not need to be understood at a technical level to make use of the predictions provided. The description of this process focuses on the how the data is handled and provides some basic information on evaluating the accuracy and utility of the predictions.

Forecast model creation

The forecast model is built based on the specifications in the Time Series node in each stream.

The Time Series node is labelled TSModel in the flow chart of the Daily Forecast stream. This node is where the field to be predicted is identified, along with the fields to be used as inputs to the model. In IBM® Capacity Management Analytics, CPU_BUSY_MIPS (summed across the relevant rows to the daily, hourly or monthly level) is the field that is predicted. In addition to the Holiday, high, and low fields from the holiday.csv file, the day of week is included as a predictor to be evaluated by the modeling algorithm.

Figure 12. The Time Series node

The modeling itself is set on the Model tab of Time Series node. Capacity Management Analytics uses the Expert Modeler facility for time series models, set to evaluate Auto Regressive Integrated Moving Average (ARIMA) models and select the one that yields the most accurate result.

Running the stream

IBM SPSS® Modeler streams for the hourly, daily, and monthly program are designed to run in an automated manner. When they run, all of the LPAR and PROCESSOR TYPE combinations found in the data are identified and a separate model is produced for each. These models are used to calculate the forecast for the original data and produce predicted values for the future. Each stream contains a script that manages this process. When the script is started it prompts for parameters that can be modified as necessary to point to the database source and the output directory for storing the models and the output directory for logs. Also, for hourly and daily forecast, an input PATH for holiday.csv is required. The output directories and the input path must exist before the script runs.

Figure 13. Prompts for stream parameters

The script itself is unlikely to ever require modification, but you can click Tools, Stream Properties, then Script in SPSS Modeler to view the script.

Parent topic: Creating forecasts

Predicted scoresThe predictions that are produced by the models are saved to the database and stored in the HCM.MVSPM_LPAR_CPU_BUSY_MIPS_PRED table. The predicted score field is named CPU_BUSY_MIPS_SUM_PRED, with its confidence interval named CPU_BUSY_MIPS_SUM_PRED_L as the lower bound and CPU_BUSY_MIPS_SUM_PRED_U as the upper bound.There is also a FORECAST_TIME_LEVEL that indicates whether the forecast is for the hourly, daily, or monthly level. This table can be accessed with SPSS Modeler using an SQL source node similar to the node used to retrieve the original data from the same database. Creating forecastsThe process of creating the forecast values that are provided as part of the IBM Capacity Management Analytics solution follows a standard approach used to forecast future values of things like stock prices, interest rates, and population. The process does not need to be understood at a technical level to make use of the predictions provided. The description of this process focuses on the how the data is handled and provides some basic information on evaluating the accuracy and utility of the predictions.

Predicted scores

The predictions that are produced by the models are saved to the database and stored in the HCM.MVSPM_LPAR_CPU_BUSY_MIPS_PRED table.

The predicted score field is named CPU_BUSY_MIPS_SUM_PRED, with its confidence interval named CPU_BUSY_MIPS_SUM_PRED_L as the lower bound and CPU_BUSY_MIPS_SUM_PRED_U as the upper bound.

There is also a FORECAST_TIME_LEVEL that indicates whether the forecast is for the hourly, daily, or monthly level. This table can be accessed with SPSS® Modeler using an SQL source node similar to the node used to retrieve the original data from the same database.

Parent topic: Creating forecasts

The evaluation of modelsAs models are produced independently for each LPAR/PROCESSOR TYPE combination, they need to be evaluated separately to validate their utility. SPSSevaluating modelsThe accuracy of a forecasting model can be assessed using a number of standard criteria. The usefulness of the forecasts is usually related to their ability to predict peak CPU usage with reasonable accuracy. Off-peak usage is generally less critical to predict accurately, which means that the overall accuracy of the model may not be the best gauge of its usefulness. The reports showing predicted values provide you with the best means of evaluating the forecasts. The best way to evaluate the model results is to examine the forecast reports. These reports are available through IBM Cognos Connection. For example, the CPU: MIPS Used - zServer/LPAR LevelThis report shows the observed average or peak CPU MIPS used at the zServer/CPC level and the forecasted average or peak CPU MIPS used along with their confidence interval. The average CPU MIPS used (observed and forecasted) and peak CPU MIPS used (observed and forecasted) can be displayed for each processor type (CP, zIIP, zAAP, IFL, or ICF) at each interval band and is broken down to the LPAR level. The forecasted peak CPU MIPS used can be displayed for each processor type at each interval band on the CPC level. report contains both actual values and forecasted values.The following image is an example of the CPU: MIPS Used -zServer/LPAR Level report with forecasts included. The gap between the dots on the line and the top of the bar indicate the degree to which the predicted value matched actual CPU usage. CPU: MIPS Used -zServer/LPAR Level report with forecastingMIPS Used - zServer/LPAR Level example report.You can use the table that accompanies the graph to make precise comparisons. If your main objective is to accurately identify peak loads, you should confirm that in most cases the predicted value is equal to or greater than actual value. The closer the actual and forecasted values are over the last 30 days, the more confidence you can have in the accuracy of future forecast values. Around any estimate there is a confidence interval and the width of this interval (or band around the predicted value) widens as time extends into the future. This means forecasts closer to the last data point are likely to be more accurate than those farther out in time. MIPS forecasting stream

The evaluation of models

As models are produced independently for each LPAR/PROCESSOR TYPE combination, they need to be evaluated separately to validate their utility.

The accuracy of a forecasting model can be assessed using a number of standard criteria. The usefulness of the forecasts is usually related to their ability to predict peak CPU usage with reasonable accuracy. Off-peak usage is generally less critical to predict accurately, which means that the overall accuracy of the model may not be the best gauge of its usefulness. The reports showing predicted values provide you with the best means of evaluating the forecasts.

The best way to evaluate the model results is to examine the forecast reports. These reports are available through IBM® Cognos® Connection. For example, the CPU: MIPS Used - zServer/LPAR Level report contains both actual values and forecasted values.

The following image is an example of the CPU: MIPS Used -zServer/LPAR Level report with forecasts included. The gap between the dots on the line and the top of the bar indicate the degree to which the predicted value matched actual CPU usage.

Figure 14. CPU: MIPS Used -zServer/LPAR Level report with forecasting

You can use the table that accompanies the graph to make precise comparisons. If your main objective is to accurately identify peak loads, you should confirm that in most cases the predicted value is equal to or greater than actual value. The closer the actual and forecasted values are over the last 30 days, the more confidence you can have in the accuracy of future forecast values. Around any estimate there is a confidence interval and the width of this interval (or band around the predicted value) widens as time extends into the future. This means forecasts closer to the last data point are likely to be more accurate than those farther out in time.

Parent topic: MIPS forecasting stream

Details for hourly forecastsTime Interval node settings for the hourly forecast are based on a 24x7 time period. Note that the Time Interval is set to Hours per day and the timestamp field provides the information necessary to identify the date, day of the week, and hour of the day.SPSShourly forecastsTime Interval node for the hourly forecastImage of the Time Interval node for the hourly forecast.The hold out period for the hourly forecast is 144 hours (6 days). MIPS forecasting stream

Details for hourly forecasts

Time Interval node settings for the hourly forecast are based on a 24x7 time period. Note that the Time Interval is set to Hours per day and the timestamp field provides the information necessary to identify the date, day of the week, and hour of the day.

Figure 15. Time Interval node for the hourly forecast

The hold out period for the hourly forecast is 144 hours (6 days).

Parent topic: MIPS forecasting stream

Details for monthly forecastsTime Interval node settings for the monthly forecast is set to months and the date field provides the information necessary to assign each row of data to the appropriate month.SPSSmonthly forecastsTime Interval node for the monthly forecastImage of the Time Interval node for the monthly forecast.In the monthly forecast, one month of data is held out for validation purposes. Forecasts are provided for twelve months into the future.MIPS forecasting stream

Details for monthly forecasts

Time Interval node settings for the monthly forecast is set to months and the date field provides the information necessary to assign each row of data to the appropriate month.

Figure 16. Time Interval node for the monthly forecast

In the monthly forecast, one month of data is held out for validation purposes. Forecasts are provided for twelve months into the future.

Parent topic: MIPS forecasting stream

Details for the CPC daily peak forecast streamThe CPC daily peak forecast stream (mvspm-cpc-daily-peak-forecast-timeseries.str) is used to produce daily CPU_BUSY_MIPS :)peak forecasts at the CPC level. The stream analyzes a defined time range of the historical data, calculates its peak value of MIPS, and forecasts its one year peak value at the CPC level for the future.CPC level The CPC level is defined by the combination of the CPU_SERIAL_NO and PROCESSOR_TYPE fields. A different combination of the fields result in a different CPC level.Peak values The peak values are defined as a serial that is formed by the Top-N MIPS values in each day, where N is specified as the Peak Highest Rank. You can also input multiple N values to forecast multiple peak value serials in a single run. The maximum value of N is 5.Input table HCM.MVSPM_LPAR: the source of history data for MIPS.Output table HCM.MVSPM_CPC_CPU_BUSY_MIPS_PRED: the forecasted data and the related history data for MIPS of each day by each CPC level for each Peak Highest Rank is stored in this table.HCM.FORECAST_METADATA: the execution information of peak forecasting stream for each Peak Highest Rank is stored in this table.Input parameters CMDW: the CMA database connection. CMASCHEMA: the schema for CMA.UID: the database user.PWD: the database user's password.ENCODED: the encoded database user's password flag. (true/false)OUTPUTMODELPATH: the model output path. TMPPATH: temporary data saved path.INPUTHOLIDAYFILE: the input holiday file path.DATE_START: the start date for the data that is used.DATE_END: the end date for the data that is used.HOUR_START: the start hour for the data that is used. An integer number between 0-23.HOUR_END: the end hour for the data that is used. An integer number between 0-23. This value should be greater than or equal to the HOUR_START value. HOUR_END means another full hour. For example, if HOUR_START = 1 and HOUR_END = 13, then the time range is 01:00:00 - 13:59:59. PEAKRANK: the Nth highest peak rank values. Valid values are integers in the range [1,5]. Use a comma (,) to separate rank values if you are using multiple rank values.Validating inputs The stream verifies that input values are valid. For example, the stream verifies whether the path and database are writable. If not, it stops and an error is written to the log file. The log file is stored in LOGPATH.Validating historical data The stream verifies that the historical data in the HCM.MVSPM_LPAR table is valid to do a forecast. For example, the stream skips the series if the data points of a CPC for a peak are less than 60.Forecast data generation and output There is a Python script in the peak forecast stream that drives the process. The stream finds the peak values by each Peak Highest Rank N by each CPC level within each day. The peak MIPS values are selected from the MIPS values by each hour within a day. After a serial of peak values is produced, the stream analyzes the data by using the Time Serials model, outputs and stores each of the models that are trained in OUTPUTMODELPATH, and uses them to forecast one year of peak values in the future by each CPC level. The Peak Highest Rank N of the forecasted data is labeled in the field FORECAST_LEVEL such as Highest Hourly Average of CPC MIPS Daily for N=1. The saved models are named <CPU_SERIAL_NO>_<PROCESSOR_TYPE>_Peak_<N>_daily_<timestamp>.gm. After the forecast values are produced, they are gathered in a temporary file that is stored in TMPPATH named as CPC_MIPS_DAILY_PEAK_FORECAST_TEMP_<peak_n> and then output to the database. If the database already contains MIPS forecasted data with the same peak level, the new values overwrite the existing values. Values that do not have the same peak level as the new values are kept in the database.Estimation for missing data If there are any missing data points in a series, the stream generates an estimator by the average of the other data points in the series.Adjusting outliers If any prediction is greater than 2 times the max value of the historical data in a series, then that prediction value, the upper bound and the lower bound of the confidence level, are adjusted to 2 times the max value of the historical data. If any prediction (including the 2 bounds of the confidence level) is less than 0, then the value is adjusted to 0.Execution data generation and output The information of the latest stream execution time and the data time range for history data and forecast data along with the related Peak Highest Rank N (represented by filed AGGREGATION in the table, for example, Highest Hourly Average of CPC MIPS Daily) are gathered when the forecasting runs. This information is written to the database after the forecasting finishes. Each running for each Peak Highest Rank makes one record in the table. If there are old records in the database, any values with the same Peak Highest Rank are overwritten if new values are produced. Any existing values that do not have the same Peak Highest Rank are kept in the database.Log file and error handling You can find the IBM SPSS Modeler stream log files in the log folder that you specify in the LOGPATH parameter. The log file is named mvspm_cpc_daily_peak_forecast_timeseries_<timestamp>.log. For information about error and warning messages, see Error, warning, and installation messagesError, warning, and installation messages are displayed for IBM Capacity Management Analytics..MIPS forecasting stream

Details for the CPC daily peak forecast stream

The CPC daily peak forecast stream (mvspm-cpc-daily-peak-forecast-timeseries.str) is used to produce daily CPU_BUSY_MIPS :)peak forecasts at the CPC level. The stream analyzes a defined time range of the historical data, calculates its peak value of MIPS, and forecasts its one year peak value at the CPC level for the future.

CPC level

The CPC level is defined by the combination of the CPU_SERIAL_NO and PROCESSOR_TYPE fields. A different combination of the fields result in a different CPC level.

Peak values

The peak values are defined as a serial that is formed by the Top-N MIPS values in each day, where N is specified as the Peak Highest Rank. You can also input multiple N values to forecast multiple peak value serials in a single run. The maximum value of N is 5.

Input table

HCM.MVSPM_LPAR: the source of history data for MIPS.

Output table

HCM.MVSPM_CPC_CPU_BUSY_MIPS_PRED: the forecasted data and the related history data for MIPS of each day by each CPC level for each Peak Highest Rank is stored in this table.

HCM.FORECAST_METADATA: the execution information of peak forecasting stream for each Peak Highest Rank is stored in this table.

Input parameters

CMDW: the CMA database connection.

CMASCHEMA: the schema for CMA.

UID: the database user.

PWD: the database user's password.

ENCODED: the encoded database user's password flag. (true/false)

OUTPUTMODELPATH: the model output path.

TMPPATH: temporary data saved path.

INPUTHOLIDAYFILE: the input holiday file path.

DATE_START: the start date for the data that is used.

DATE_END: the end date for the data that is used.

HOUR_START: the start hour for the data that is used. An integer number between 0-23.

HOUR_END: the end hour for the data that is used. An integer number between 0-23.

This value should be greater than or equal to the HOUR_START value. HOUR_END means another full hour. For example, if HOUR_START = 1 and HOUR_END = 13, then the time range is 01:00:00 - 13:59:59.

PEAKRANK: the Nth highest peak rank values. Valid values are integers in the range [1,5]. Use a comma (,) to separate rank values if you are using multiple rank values.

Validating inputs

The stream verifies that input values are valid. For example, the stream verifies whether the path and database are writable. If not, it stops and an error is written to the log file. The log file is stored in LOGPATH.

Validating historical data

The stream verifies that the historical data in the HCM.MVSPM_LPAR table is valid to do a forecast. For example, the stream skips the series if the data points of a CPC for a peak are less than 60.

Forecast data generation and output

There is a Python script in the peak forecast stream that drives the process. The stream finds the peak values by each Peak Highest Rank N by each CPC level within each day. The peak MIPS values are selected from the MIPS values by each hour within a day. After a serial of peak values is produced, the stream analyzes the data by using the Time Serials model, outputs and stores each of the models that are trained in OUTPUTMODELPATH, and uses them to forecast one year of peak values in the future by each CPC level. The Peak Highest Rank N of the forecasted data is labeled in the field FORECAST_LEVEL such as Highest Hourly Average of CPC MIPS Daily for N=1. The saved models are named <CPU_SERIAL_NO>_<PROCESSOR_TYPE>_Peak_<N>_daily_<timestamp>.gm.

After the forecast values are produced, they are gathered in a temporary file that is stored in TMPPATH named as CPC_MIPS_DAILY_PEAK_FORECAST_TEMP_<peak_n> and then output to the database. If the database already contains MIPS forecasted data with the same peak level, the new values overwrite the existing values. Values that do not have the same peak level as the new values are kept in the database.

Estimation for missing data

If there are any missing data points in a series, the stream generates an estimator by the average of the other data points in the series.

Adjusting outliers

If any prediction is greater than 2 times the max value of the historical data in a series, then that prediction value, the upper bound and the lower bound of the confidence level, are adjusted to 2 times the max value of the historical data. If any prediction (including the 2 bounds of the confidence level) is less than 0, then the value is adjusted to 0.

Execution data generation and output

The information of the latest stream execution time and the data time range for history data and forecast data along with the related Peak Highest Rank N (represented by filed AGGREGATION in the table, for example, Highest Hourly Average of CPC MIPS Daily) are gathered when the forecasting runs. This information is written to the database after the forecasting finishes. Each running for each Peak Highest Rank makes one record in the table. If there are old records in the database, any values with the same Peak Highest Rank are overwritten if new values are produced. Any existing values that do not have the same Peak Highest Rank are kept in the database.

Log file and error handling

You can find the IBM SPSS Modeler stream log files in the log folder that you specify in the LOGPATH parameter. The log file is named mvspm_cpc_daily_peak_forecast_timeseries_<timestamp>.log. For information about error and warning messages, see Error, warning, and installation messages.

Parent topic: MIPS forecasting stream

Details for LPAR peak daily forecast streamThe LPAR peak daily forecast stream uses the historical daily peak MIPS value for each CPC, LPAR, and processor type combination and forecasts the daily peak MIPS for one year into the future. The stream requires at least 60 days of continuous data, without any missing values, to do the forecasting. You can specify the date and time range that you want to use as the historical data when you run the stream. This stream also supports different ranks of peak calculations; for example, highest peak, 2nd highest peak.The stream takes external factors as input values; for example, holiday information is used as an external factor. You can input your own external factors to replace the holiday files. When you use customized external factors, these factors must contain a value for all future records. You can specify customized external factors in a text file. The text file must use the same format as the sample holiday file. To use the customized external factors, you must specify the path to the text file in the INPUTHOLIDAYFILE parameter when you run the stream.MIPS forecasting stream

Details for LPAR peak daily forecast stream

The LPAR peak daily forecast stream uses the historical daily peak MIPS value for each CPC, LPAR, and processor type combination and forecasts the daily peak MIPS for one year into the future. The stream requires at least 60 days of continuous data, without any missing values, to do the forecasting.

You can specify the date and time range that you want to use as the historical data when you run the stream. This stream also supports different ranks of peak calculations; for example, highest peak, 2nd highest peak.

The stream takes external factors as input values; for example, holiday information is used as an external factor. You can input your own external factors to replace the holiday files. When you use customized external factors, these factors must contain a value for all future records. You can specify customized external factors in a text file. The text file must use the same format as the sample holiday file.

To use the customized external factors, you must specify the path to the text file in the INPUTHOLIDAYFILE parameter when you run the stream.

Parent topic: MIPS forecasting stream

Error handling for model buildingIf an error is encountered while building a model for a specific LPAR/PROCESSOR TYPE combination, tables are generated that contain details related to the error. SPSSerror handlingThese tables are saved into the Log directory that was created as part of the IBM Capacity Management Analytics configuration process. The name of a given table contains the LPAR/PROCESSOR TYPE that produced the error. The information in these tables is useful for diagnosing underlying issues with your model. In most cases, the problem will be related to insufficient data on which to build a model. This can occur if a LPAR/PROCESSOR TYPE combination has only a few months of data available. MIPS forecasting stream

Error handling for model building

If an error is encountered while building a model for a specific LPAR/PROCESSOR TYPE combination, tables are generated that contain details related to the error.

These tables are saved into the Log directory that was created as part of the IBM® Capacity Management Analytics configuration process. The name of a given table contains the LPAR/PROCESSOR TYPE that produced the error. The information in these tables is useful for diagnosing underlying issues with your model. In most cases, the problem will be related to insufficient data on which to build a model. This can occur if a LPAR/PROCESSOR TYPE combination has only a few months of data available.

Parent topic: MIPS forecasting stream

Software Cost Analysis forecasting streamsProduct forecastThere are three forecasting streams in the sca folder:msu_prod_forecast_timeseries.strmsu_lpar_forecast_timeseries.strmsu_gssp_forecast_timeseries.str. These forecast streams are used to predict the future MSU usage of a machine for a certain product or certain LPAR, which is the basis for billable MSU calculation and optimization.Approach for forecastingThe product forecast streams and the GSSP forecast stream use similar approaches to forecasting MSU. The LPAR stream aggregates MSU from product level to LPAR level.MSU (Millions of Service Units) refers to software pricing capacity units. Product MSU, which refers to product consumed in millions of service units hourly, is the target used to forecast. The IBM SPSS Modeler stream used to forecast MSU for each product on each LPAR is msu_prod_forecast_timeseries.str. A product named ‘9999-OTH’, also called Others, represents the remaining MSUs that are unaccounted for, such as NO89 products. This Other category is also forecast. The first step in creating a forecast is to retrieve the data from the database and aggregate it up to the hourly level. PROD_MSU is the field name of the forecasting target, while PROD_MSU_FORECAST is what is forecast from the built model. PROD_MSU_4HRA_FORECAST is the average of PROD_MSU_FORECAST over the past four hours and PROD_MSU_4HRA_UTILRA_FORECAST is the minimum of PROD_MSU_4HRA_FORECAST and defines capacity.SCA forecast is different from the MIPS forecast stream in structure. To improve the accuracy of the model, the data is split to the daily level. This means the data is put together for Monday and then forecast. So there are seven models in each product on each LPAR level in general. All models are stored in a specified location. This location is specified in the prompt window when starting running the stream in client mode or in the CMAINSTANCE/data/cmasca.parms file if running the stream in shell mode. The default value for the location is the CMAINSTANCE/model folder.The time interval nodes in SCA don’t need to be modified manually, but can be managed by scripts automatically instead. Product MSU is forecast for the next year by default, that is, the forecast is performed for the next 52 weeks multiplied by 24 hours, which equals 1248 records. You can optimize this but you need to forecast at least one month's (4x24=96 records) data.Inputs for modelThe holiday file (CMAINSTANCE/data/holiday.csv) is used to input for the build model for MIPS forecasting. For data that has a large enough time range, you can use data from the same time the previous year as another input for forecasting. Scripts can manage this automatically. For data consisting of one day of the week on each product and an LPAR combination that has more than an 18 month time range, holiday and PROD_MSU_LAST_YEAR are used as input fields. For data that is less than 18 months, only holiday is used as an input field. Time series nodeForecast protectionAn interruption can occur if a time series is incorrect for the model used or when Modeler stops because there is not enough data for building or has too many null values for the target field. In SCA, forecasting streams address some of these types of interruptions.For models that are built for hours of one day of the week, the time series used builds the model if the data used for the last day of building has 24 records and there is no null value in target field. So data previous to the 24 hour period that has no null value is used to build a model for each day of the week on each product and LPAR combination.In MSU data, some of the LPARs or products run only for a short time and then stop. The ODD parameter is used to handle these obsoleted products. If a product no longer exists or does not run for recent ODD days in one LPAR, then we assume that this combination is obsolete and the stream does not forecast this combination.You can view a prompt table that includes all the combinations that are used to forecast which meet the previously mentioned conditions if running the stream in the client.The SCA product forecast stream uses the date from DATEMIN to the latest date to forecast all of the product and LPAR combinations. The forecast stream uses the latest operation system version that is in the observed data to perform a forecast for each LPAR, and ignores any other operation system versions previously run by the LPAR.The combinations that cannot meet the previously mentioned conditions are written into the log file where you entered the parameter of Log Path in the prompt window. The default is CMAINSTANCE/log, at the beginning of running the stream. there is no combination message if there is no record of Sunday in a product on one LPAR. You can fill in the corresponding missing values in the database if you want to forecast the MSU for a combination in the log file. For more information view the Troubleshooting section. NOTE TO BRIAN: link is needed to troubleshooting section in this guideMonthly levelAggregate the MSU hourly forecast data to a monthly level using the max method and insert it into the same table where hourly forecast data exists in the database for the SCA forecast procedure. You can distinguish the hourly data from the monthly data with the FORECAST_TIME_LEVEL field.Aggregate to monthly levelThe LPAR Forecast StreamThe IBM SPSS Modeler stream used to forecast MSU for each LPAR is msu_lpar_forecast_timeseries.str. Aggregate all product MSU and Others MSU to the LPAR level and get the MSU usage of each LPAR in one CPC (CEC).The GSSP Forecast StreamThe IBM SPSS Modeler stream used to forecast the GSSP MSU for each product on each LPAR is msu_gssp_forecast_timeseries.str. As the use of the GSSP forecast stream and the product forecast stream are almost the same, refer to the Product forecast section for information.The GSSP forecast data isn't inserted into a separate table in the database. Instead, it is saved in a temporary file and merged with product forecast data. It is then inserted into a table in PROD_MSU_FORECAST in the product forecast stream database. So you should run GSSP forecast stream before running product forecast stream.SPSS Modeler stream referenceMuch of the data in IBM Capacity Management Analytics reports and workspaces is processed using IBM SPSS Modeler. This software uses programs that are referred to as streams, which contain the instructions required to retrieve, prepare, model, and forecast the data.

Software Cost Analysis forecasting streams

Product forecast

There are three forecasting streams in the sca folder:

msu_prod_forecast_timeseries.str

msu_lpar_forecast_timeseries.str

msu_gssp_forecast_timeseries.str.

These forecast streams are used to predict the future MSU usage of a machine for a certain product or certain LPAR, which is the basis for billable MSU calculation and optimization.

Approach for forecasting

The product forecast streams and the GSSP forecast stream use similar approaches to forecasting MSU. The LPAR stream aggregates MSU from product level to LPAR level.

MSU (Millions of Service Units) refers to software pricing capacity units. Product MSU, which refers to product consumed in millions of service units hourly, is the target used to forecast. The IBM SPSS Modeler stream used to forecast MSU for each product on each LPAR is msu_prod_forecast_timeseries.str. A product named ‘9999-OTH’, also called Others, represents the remaining MSUs that are unaccounted for, such as NO89 products. This Other category is also forecast. The first step in creating a forecast is to retrieve the data from the database and aggregate it up to the hourly level. PROD_MSU is the field name of the forecasting target, while PROD_MSU_FORECAST is what is forecast from the built model. PROD_MSU_4HRA_FORECAST is the average of PROD_MSU_FORECAST over the past four hours and PROD_MSU_4HRA_UTILRA_FORECAST is the minimum of PROD_MSU_4HRA_FORECAST and defines capacity.

SCA forecast is different from the MIPS forecast stream in structure. To improve the accuracy of the model, the data is split to the daily level. This means the data is put together for Monday and then forecast. So there are seven models in each product on each LPAR level in general.

All models are stored in a specified location. This location is specified in the prompt window when starting running the stream in client mode or in the CMAINSTANCE/data/cmasca.parms file if running the stream in shell mode. The default value for the location is the CMAINSTANCE/model folder.

The time interval nodes in SCA don’t need to be modified manually, but can be managed by scripts automatically instead. Product MSU is forecast for the next year by default, that is, the forecast is performed for the next 52 weeks multiplied by 24 hours, which equals 1248 records. You can optimize this but you need to forecast at least one month's (4x24=96 records) data.

Inputs for model

The holiday file (CMAINSTANCE/data/holiday.csv) is used to input for the build model for MIPS forecasting. For data that has a large enough time range, you can use data from the same time the previous year as another input for forecasting. Scripts can manage this automatically. For data consisting of one day of the week on each product and an LPAR combination that has more than an 18 month time range, holiday and PROD_MSU_LAST_YEAR are used as input fields. For data that is less than 18 months, only holiday is used as an input field.

Forecast protection

An interruption can occur if a time series is incorrect for the model used or when Modeler stops because there is not enough data for building or has too many null values for the target field. In SCA, forecasting streams address some of these types of interruptions.

For models that are built for hours of one day of the week, the time series used builds the model if the data used for the last day of building has 24 records and there is no null value in target field. So data previous to the 24 hour period that has no null value is used to build a model for each day of the week on each product and LPAR combination.

In MSU data, some of the LPARs or products run only for a short time and then stop. The ODD parameter is used to handle these obsoleted products. If a product no longer exists or does not run for recent ODD days in one LPAR, then we assume that this combination is obsolete and the stream does not forecast this combination.

You can view a prompt table that includes all the combinations that are used to forecast which meet the previously mentioned conditions if running the stream in the client.

The SCA product forecast stream uses the date from DATEMIN to the latest date to forecast all of the product and LPAR combinations. The forecast stream uses the latest operation system version that is in the observed data to perform a forecast for each LPAR, and ignores any other operation system versions previously run by the LPAR.

The combinations that cannot meet the previously mentioned conditions are written into the log file where you entered the parameter of Log Path in the prompt window. The default is CMAINSTANCE/log, at the beginning of running the stream.

Note: there is no combination message if there is no record of Sunday in a product on one LPAR. You can fill in the corresponding missing values in the database if you want to forecast the MSU for a combination in the log file.

Monthly level

Aggregate the MSU hourly forecast data to a monthly level using the max method and insert it into the same table where hourly forecast data exists in the database for the SCA forecast procedure. You can distinguish the hourly data from the monthly data with the FORECAST_TIME_LEVEL field.

The LPAR Forecast Stream

The IBM SPSS Modeler stream used to forecast MSU for each LPAR is msu_lpar_forecast_timeseries.str. Aggregate all product MSU and Others MSU to the LPAR level and get the MSU usage of each LPAR in one CPC (CEC).

The GSSP Forecast Stream

The IBM SPSS Modeler stream used to forecast the GSSP MSU for each product on each LPAR is msu_gssp_forecast_timeseries.str. As the use of the GSSP forecast stream and the product forecast stream are almost the same, refer to the Product forecast section for information.

The GSSP forecast data isn't inserted into a separate table in the database. Instead, it is saved in a temporary file and merged with product forecast data. It is then inserted into a table in PROD_MSU_FORECAST in the product forecast stream database. So you should run GSSP forecast stream before running product forecast stream.

Parent topic: SPSS Modeler stream reference

CICS anomaly detection streamsCustomer Information Control System (CICS) anomaly detection helps to improve system management by proactively detecting anomalies in forecast capacity so that you can identify issues before they affect the business. You can identify items that differ from other items or do not fit into the expected pattern in a data set. Items that are found to be anomalous typically indicate a problem such as a structural defect, bank fraud, finding errors in text, or medical problems. Anomalies can also be referred to as outliers, novelties, noise, deviations, and exceptions. The faster you can identify unanticipated transactions, the faster you can take corrective action to circumvent any effects on the performance of the business application. Results of the stream are shown in the Anomaly Detection Analytics: CICS Anomaly DetailsThis report shows the abnormal transactions that were detected by the CICS anomaly detection streams. This report shows a summary of the abnormal transaction counts and the detail transactions that are determined to be abnormal over a short period. report. IBM Capacity Management Analytics provides a scoring stream that reads the model building information, calculates the prediction results for each transaction type, determines which transactions are abnormal, and then exports the results to the database. Logging the history of abnormal transactions lets you report on trends and patterns of the anomalies. It is also a source for any specific alerts that are based on different levels or scope of anomalies detected. Image of an SPSS model for anomaly detection.Transaction A transaction is a set of operations that perform a task together. Typically, a transaction is a simple task such as entering an account debit or credit or requesting an inventory list. A transaction should be primarily atomic.Abnormal transaction A transaction is abnormal if its execution characteristics differ by some ratio from its transaction model, based on normal transaction processing. Characteristics that can change only by logic in the program for a transaction would cause the transaction to be detected as abnormal and its logic be investigated. However, a number of characteristics can be influenced in varying degrees by conditions outside the scope of the transaction itself, such as the environment in which the transaction is running (in effect, shared resources such as CPU, memory, storage, and queuing because of different concurrency controls). The characteristics to focus on can be determined from the SMF 110 records that are written by CICS. For more information, see the CICS_T_TRAN_T_table documentation on IBM Knowledge Center.Anomaly model building Capacity Management Analytics has two different model building streams: anomaly_detect_building_c.str and anomaly_detect_building_r.str. The streams contain these steps: Data preparationModel buildingModel evaluation The models are built with default options. You can change the options to customize the models for your environment. Trained models are stored locally in the CMAINSTANCE/model folder and model information is found in a log file in the CMAINSTANCE/log folder. Data sourceThe CICS_T_TRAN_T table provides statistics on CICS transactions. It contains information from CICS performance class monitoring records for CICS/ESA (SMF 110, subtype 1) and CICS/MVS (SMF 110, subtype 0). Related table definitions can be found in the CICS_T_TRAN_T_table documentation.Data preparationCICS transaction data creates records for each transaction with more than 300 columns. In a production environment, records with more than 10 million transactions per day is not uncommon. Most of the columns are not necessary to analyze abnormal transactions, so Capacity Management Analytics includes an iterative process that uses the modeling tool to help you build a detection model that fits your transaction profile. The process helps to speed the process of learning which of those columns are the most important ones for your transactions. Record reduction for an input table you can specify which transaction needs to be examined by using the transaction.csv file.Transactionyou can specify the date range for model building and evaluation by using the date_info.csv file. The stream will use data from the time 00:00:00 in the start date to 23:59:59 in the end date.Date rangeDimension reduction for the input tableReduction dimension of input data by using predefined filter nodeReduction dimension of input data by using feature selection model in model.Adding external factors for model buildingThe day of the week and the time of day need to be introduced because transactions in a production environment have some periodical patterns. For example, many specific types of transactions happen on Mondays. IBM SPSS Modeler has a data limitation for model building. The limitation depends on the size of input data. You can find detailed information from the Ram Requirements section of the SPSS Modeler documentation on IBM Knowledge Center. The stream is protected such as to avoid a memory exceeded error. If an error occurs, you can verify it by viewing the error message in the error log. If the error is because of the size of the data set used, you need to limit the data set to just a sample size. This prevents errors that are caused by data size and memory limits.Model buildingDifferent streams build a model by using different targets.The anomaly_detect_building_c.str stream builds a model on the transactions that detect an anomaly that occurs in CICS. The anomaly_detect_building_r.str stream builds a model on the transactions that detect anomalies in both CICS and in an z/OS environment. Both streams use different data mining algorithms for predicting normal transactions. Models are built with default options. Input field models use results from the feature selection model that is used in the data preparation steps.Model evaluation and selectionThe mean of relative error and standard deviation of relative error is used to evaluate and predict results accuracy. Rank models by accuracy and select the one with best accuracy for scoring. Model evaluation formulaAnomaly model scoring CMA provides a scoring stream to score models built by model building streams. Streams need to read model-building information, calculate prediction results for each transaction type, determine which transaction is abnormal, and then export the result to the database. Scoring streams support SQL push-back in Modeler, and can use the scoring adapter for zEnterprise V16 to increase scoring performance. Refer to the zEnterprise V16 documentation for more information. Define abnormal transactionsAbnormal transactions can run for a long time or for a short time. Store scoring resultA scoring stream stores the scoring result to the database for future reference. The Abnormal field indicates whether the transaction is abnormal or not. Tree-based anomaly detection model There is one tree-based anomaly detection stream: anomaly_detect_treebased.str. Data sourceThe CICS_T_TRAN_T table provides statistics on CICS transactions. It contains information from CICS performance class monitoring records for CICS/ESA (SMF 110, subtype 1) and CICS/MVS (SMF 110, subtype 0). Related table definitions can be found in the CICS_T_TRAN_T_table documentation.Data preparationCICS transaction data has records for each transaction with more than 300 columns. In a production environment, more than 10 million transactions occur in a day and, to analyze abnormal transactions, most of the columns are not required. This tree-based model requires that all input columns are continuous values. Therefore, a data preparation process must be run before the model is built. There is a process of record reduction for the input table: You can specify which transactions need to be examined by using the transaction.csv file. Transactions are specified in the anomaly_input.xls fileYou can specify the date range for model building and scoring in the date_info.csv file. The stream uses data from a time of 00:00:00 on the start date to 23:59:59 on the end date. Date ranges specified in the anomaly_input.xls fileThe tree-based model requires only a sample of the data to build the trees. It uses no more than 2048 records, so a sample function is applied to select the model building data. There is a process of dimension reduction for the input table: Reduction dimension of the input data by using a predefined filter node.Model buildingThis tree-based model is implemented by an IBM SPSS Modeler embedded Jython script. The model builds 10 different trees.Model scoringScoring data feeds this model and it outputs an anomaly score based on the average path length of the input data in the trees. A conversion is applied to the average path length to get the final scoring result. When the score result is greater than 0.5, it means that this transaction is probably an abnormal transaction. In the stream, this ratio is set with 0.5 as the default. The value for this ratio is recommended to be no greater than 0.6. and no less than 0.5 according to this algorithm. The scoring result is saved to the database with anomaly_level = "TREE".Anomaly scoring classification This stream takes the scoring results classified result to a different level of abnormality. Mark all transactions with red, yellow, or green flags, then store the result to the database and output a distribution graph. Before you use this classification stream, the other streams must run for the same transaction in the database or else an error is recorded in the error log and you are prompted for data.CICS Anomaly detection customization stream The anomaly detection stream is used as a general approach for anomaly detection problems. The advantage of the stream is that it can run on a CICS transaction record from a different industry. However, its generic approach means that model accuracy might not be good for different scenarios. This section explains how to customize the stream to fulfill a specific requirement. Model building streams customizationData source customizationThe anomaly detection stream uses the CICS statistic table that is provided by TDSz. You can use your own SMF parser to parse SMF 110 data, and then use the data for anomaly detection. If you choose to do this, you need to change the data source node to the type of data source you are using. Also, in Jython script you need to change the data source definition ("Data_Source_Node=s.findByType("database","Data_Source")" to the type of data source you are using. Keep the node name as "Data_Source" and do not change it.Data source customizationData preparation customizationChange the filter node at the beginning to include or exclude fields that are related with the target on the data set.Add other external factors by using derive nodes. You can insert derive nodes between the filter and day node.Algorithm customizationThe anomaly detection model building stream uses different algorithms to build model candidates for evaluation. According to your specified data, you can select a different modeling node in SPSS Modeler. If a new modeling node is introduced into the stream, the stream script needs to be changed. Use the existing script as an example in developing a new script. Related scripts start from line 137.Model evaluation customizationModel evaluation uses related errors as evaluation criteria. You can specify your own evaluation criteria. To do this, you change the evaluation formula node for each model you build.Model scoring streams customizationThe model scoring stream is related to the model building stream. If you have customized the model building stream, you need to also customize the model scoring stream. Data source customizationUse the data source with the same field that is used in the model building streamUse the same filter node that is used in the model building stream. Filter_Link_C should be the same as what is used in the anomaly_detect_building_c.str stream and Filter_Link_R should be the same as what is used in the anomaly_detect_building_r.str stream.In the date_info.csv file, you can specify the start date for model scoring. The scoring stream uses data according to START_TIMESTAMP >= start_date.Anomaly transaction criteria customizationThe scoring stream uses a formula for its criteria. You can change the script (line 220 and line 233) to apply your own criteria.Anomaly classification stream customizationClassification logic is found in the combine super node. You can open this super node and edit the logic inside to apply your own classification.SPSS Modeler stream referenceMuch of the data in IBM Capacity Management Analytics reports and workspaces is processed using IBM SPSS Modeler. This software uses programs that are referred to as streams, which contain the instructions required to retrieve, prepare, model, and forecast the data.

CICS anomaly detection streams

Customer Information Control System (CICS®) anomaly detection helps to improve system management by proactively detecting anomalies in forecast capacity so that you can identify issues before they affect the business.

You can identify items that differ from other items or do not fit into the expected pattern in a data set. Items that are found to be anomalous typically indicate a problem such as a structural defect, bank fraud, finding errors in text, or medical problems. Anomalies can also be referred to as outliers, novelties, noise, deviations, and exceptions. The faster you can identify unanticipated transactions, the faster you can take corrective action to circumvent any effects on the performance of the business application.

Results of the stream are shown in the Anomaly Detection Analytics: CICS Anomaly Details report.

IBM® Capacity Management Analytics provides a scoring stream that reads the model building information, calculates the prediction results for each transaction type, determines which transactions are abnormal, and then exports the results to the database. Logging the history of abnormal transactions lets you report on trends and patterns of the anomalies. It is also a source for any specific alerts that are based on different levels or scope of anomalies detected.

Transaction

A transaction is a set of operations that perform a task together. Typically, a transaction is a simple task such as entering an account debit or credit or requesting an inventory list. A transaction should be primarily atomic.

Abnormal transaction

A transaction is abnormal if its execution characteristics differ by some ratio from its transaction model, based on normal transaction processing. Characteristics that can change only by logic in the program for a transaction would cause the transaction to be detected as abnormal and its logic be investigated. However, a number of characteristics can be influenced in varying degrees by conditions outside the scope of the transaction itself, such as the environment in which the transaction is running (in effect, shared resources such as CPU, memory, storage, and queuing because of different concurrency controls). The characteristics to focus on can be determined from the SMF 110 records that are written by CICS. For more information, see the CICS_T_TRAN_T_table documentation on IBM Knowledge Center.

Anomaly model building

Capacity Management Analytics has two different model building streams: anomaly_detect_building_c.str and anomaly_detect_building_r.str.

The streams contain these steps:

Data preparation

Model building

Model evaluation

The models are built with default options. You can change the options to customize the models for your environment.

Trained models are stored locally in the CMAINSTANCE/model folder and model information is found in a log file in the CMAINSTANCE/log folder.

Data source

The CICS_T_TRAN_T table provides statistics on CICS transactions. It contains information from CICS performance class monitoring records for CICS/ESA (SMF 110, subtype 1) and CICS/MVS™ (SMF 110, subtype 0). Related table definitions can be found in the CICS_T_TRAN_T_table documentation.

Data preparation

CICS transaction data creates records for each transaction with more than 300 columns. In a production environment, records with more than 10 million transactions per day is not uncommon. Most of the columns are not necessary to analyze abnormal transactions, so Capacity Management Analytics includes an iterative process that uses the modeling tool to help you build a detection model that fits your transaction profile. The process helps to speed the process of learning which of those columns are the most important ones for your transactions.

Record reduction for an input table

you can specify which transaction needs to be examined by using the transaction.csv file.

you can specify the date range for model building and evaluation by using the date_info.csv file. The stream will use data from the time 00:00:00 in the start date to 23:59:59 in the end date.

Dimension reduction for the input table

Reduction dimension of input data by using predefined filter node

Reduction dimension of input data by using feature selection model in model.

Adding external factors for model building

The day of the week and the time of day need to be introduced because transactions in a production environment have some periodical patterns. For example, many specific types of transactions happen on Mondays.

Note: IBM SPSS® Modeler has a data limitation for model building. The limitation depends on the size of input data. You can find detailed information from the Ram Requirements section of the SPSS Modeler documentation on IBM Knowledge Center. The stream is protected such as to avoid a memory exceeded error. If an error occurs, you can verify it by viewing the error message in the error log. If the error is because of the size of the data set used, you need to limit the data set to just a sample size. This prevents errors that are caused by data size and memory limits.

Model building

Different streams build a model by using different targets.

The anomaly_detect_building_c.str stream builds a model on the transactions that detect an anomaly that occurs in CICS.

The anomaly_detect_building_r.str stream builds a model on the transactions that detect anomalies in both CICS and in an z/OS® environment.

Both streams use different data mining algorithms for predicting normal transactions. Models are built with default options. Input field models use results from the feature selection model that is used in the data preparation steps.

Model evaluation and selection

The mean of relative error and standard deviation of relative error is used to evaluate and predict results accuracy. Rank models by accuracy and select the one with best accuracy for scoring.

Anomaly model scoring

CMA provides a scoring stream to score models built by model building streams. Streams need to read model-building information, calculate prediction results for each transaction type, determine which transaction is abnormal, and then export the result to the database. Scoring streams support SQL push-back in Modeler, and can use the scoring adapter for zEnterprise® V16 to increase scoring performance. Refer to the zEnterprise V16 documentation for more information.

Define abnormal transactions

Abnormal transactions can run for a long time or for a short time.

Store scoring result

A scoring stream stores the scoring result to the database for future reference. The Abnormal field indicates whether the transaction is abnormal or not.

Tree-based anomaly detection model

There is one tree-based anomaly detection stream: anomaly_detect_treebased.str.

Data source

The CICS_T_TRAN_T table provides statistics on CICS transactions. It contains information from CICS performance class monitoring records for CICS/ESA (SMF 110, subtype 1) and CICS/MVS (SMF 110, subtype 0). Related table definitions can be found in the CICS_T_TRAN_T_table documentation.

Data preparation

CICS transaction data has records for each transaction with more than 300 columns. In a production environment, more than 10 million transactions occur in a day and, to analyze abnormal transactions, most of the columns are not required. This tree-based model requires that all input columns are continuous values. Therefore, a data preparation process must be run before the model is built.

There is a process of record reduction for the input table:

You can specify which transactions need to be examined by using the transaction.csv file.

You can specify the date range for model building and scoring in the date_info.csv file. The stream uses data from a time of 00:00:00 on the start date to 23:59:59 on the end date.

The tree-based model requires only a sample of the data to build the trees. It uses no more than 2048 records, so a sample function is applied to select the model building data.

There is a process of dimension reduction for the input table:

Reduction dimension of the input data by using a predefined filter node.

Model building

This tree-based model is implemented by an IBM SPSS Modeler embedded Jython script. The model builds 10 different trees.

Model scoring

Scoring data feeds this model and it outputs an anomaly score based on the average path length of the input data in the trees. A conversion is applied to the average path length to get the final scoring result. When the score result is greater than 0.5, it means that this transaction is probably an abnormal transaction. In the stream, this ratio is set with 0.5 as the default. The value for this ratio is recommended to be no greater than 0.6. and no less than 0.5 according to this algorithm.

The scoring result is saved to the database with anomaly_level = "TREE".

Anomaly scoring classification

This stream takes the scoring results classified result to a different level of abnormality. Mark all transactions with red, yellow, or green flags, then store the result to the database and output a distribution graph.

Note: Before you use this classification stream, the other streams must run for the same transaction in the database or else an error is recorded in the error log and you are prompted for data.

CICS Anomaly detection customization stream

The anomaly detection stream is used as a general approach for anomaly detection problems. The advantage of the stream is that it can run on a CICS transaction record from a different industry. However, its generic approach means that model accuracy might not be good for different scenarios. This section explains how to customize the stream to fulfill a specific requirement.

Model building streams customization

Data source customization

The anomaly detection stream uses the CICS statistic table that is provided by TDSz. You can use your own SMF parser to parse SMF 110 data, and then use the data for anomaly detection. If you choose to do this, you need to change the data source node to the type of data source you are using. Also, in Jython script you need to change the data source definition ("Data_Source_Node=s.findByType("database","Data_Source")" to the type of data source you are using. Keep the node name as "Data_Source" and do not change it.

Data preparation customization

Change the filter node at the beginning to include or exclude fields that are related with the target on the data set.

Add other external factors by using derive nodes. You can insert derive nodes between the filter and day node.

Algorithm customization

The anomaly detection model building stream uses different algorithms to build model candidates for evaluation. According to your specified data, you can select a different modeling node in SPSS Modeler. If a new modeling node is introduced into the stream, the stream script needs to be changed. Use the existing script as an example in developing a new script. Related scripts start from line 137.

Model evaluation customization

Model evaluation uses related errors as evaluation criteria. You can specify your own evaluation criteria. To do this, you change the evaluation formula node for each model you build.

Model scoring streams customization

The model scoring stream is related to the model building stream. If you have customized the model building stream, you need to also customize the model scoring stream.

Data source customization

Use the data source with the same field that is used in the model building stream

Use the same filter node that is used in the model building stream. Filter_Link_C should be the same as what is used in the anomaly_detect_building_c.str stream and Filter_Link_R should be the same as what is used in the anomaly_detect_building_r.str stream.

In the date_info.csv file, you can specify the start date for model scoring. The scoring stream uses data according to START_TIMESTAMP >= start_date.

Anomaly transaction criteria customization

The scoring stream uses a formula for its criteria. You can change the script (line 220 and line 233) to apply your own criteria.

Anomaly classification stream customization

Classification logic is found in the combine super node. You can open this super node and edit the logic inside to apply your own classification.

Parent topic: SPSS Modeler stream reference

Billable MSU optimization streamThis stream is used to determine how to rearrange qualified products, or parts of products, among LPARs to lower the total cost of the billable MSUs. A qualified product is an IBM product that can produce billable MSUs in the forecasted data. If the forecasted MSU usage for a product is zero or if it is not an IBM product, then it is not included in the optimization.This optimization stream is named prod_bill_msu_optimization.str and is in the CMAHOME/features/cmasca/analytics folder.The main objective of the optimization is to move each qualified product, or part of the product, to one or more suitable LPARs to reduce the billable MSUs and total cost for the product. The report includes suggestions to help you determine where you can move the product. If the suggestion is to move part of the product to an LPAR, the suggestion also includes a recommendation for the amount of the product to move. The amount is measured in MSUs, with a granularity of one MSU, and it is recommended that you move the product’s workload, equal to this MSU amount, to the suggested LPAR.Billable MSU optimization streamBillable MSU optimization streamBillable MSU optimization streamBillable MSU optimization stream The results of this stream are output to the database and stored in four tables individually: PROD_MSU_OPTIMIZATION—for new distribution of the products among the LPARs, at the product level.LPAR_MSU_OPTIMIZATION—for new distribution of the products among the LPARs, at the LPAR level. OPTIMIZATION_MOVEMENT—for moving suggestions that are used for the new distribution. OPTIMIZATION_BILLABLE—for billable MSU calculated from the new distribution. This stream performs the optimization based on the forecasted MSU usage of the products and LPARs. there could be one year for the forecasted data, but the optimization stream uses the first month of the forecasted data to determine the new distribution. You can assume that the configuration for this new distribution would be applied only for the next two months from the beginning of the forecasted data. It is better to have a short range for the optimization suggestion as the performance of the computer that is running the optimization might change significantly during a longer run and the forecasted data needs to be updated continually to give a more accurate prediction. The second day in each month is when IBM SCRT billing occurs, therefore the starting day of the forecasted data that is input into the optimization stream is also the second day in each month. The stream prolongs the starting day to the second day in the next month if the actual beginning of the forecasted day is later than second day in the current month. The second day in the current month is used by the stream if the actual beginning of the forecasted day is the first or second day in the current month. Any forecasted data earlier than the starting day is ignored. Capping strategyDuring the optimization of the arrangement for one product to an LPAR, the usage of MSU of the product should not exceed the cap value of the LPAR. The cap value is calculated from the defined capacity of the LPAR and the capacity that is limited by the shared weight of the LPAR. It is assumed that these two values are near to each other in most cases, and the capping value would be the minimum one of these two values. However, if the defined capacity is significantly bigger than the other (for example, five percent for the difference part), there is a parameter to control the capping value calculation strategy. There are two choices:"weight": means to use the capacity that is limited by the shared weight as the capacity capping of the LPAR, but this selection might waste available capability that the LPAR has from the defined capacity and, therefore, decreases the possibility of an optimized result.“define”: means to use the define capacity as the capacity capping of the LPAR, but this selection might cause certain products to not get the capability expected from the define capacity when CEC is busy and the product's performance will be degraded.The parameter “PUSERSELECTION” is used to specify the choice.The cared productBy default, the stream is trying to reduce the billable MSU for all qualified products. You can also designate important products as cared products to notify the stream to lower the cost of the products in priority. This is because there could be a case for an optimization result where the total cost of the summed billable MSU of all the products is reduced with no cost reduction or increase for some of its products. Therefore, this feature can identify these products to guarantee cost reduction for cared products with no guarantee for cost reduction for a non-cared product. You can specify a cared product by inputting the product ID and set the cared flag in the input file. The cared productThe bounded productIn some cases, products can be related and should appear together in the LPARs arranged for them. For example, product A relies on product B, therefore, in each LPAR where product A is located, product B must exist. The optimization stream supports this scenario by using a bounding feature. For each product group that contains products that are related, you can input them in the input file and allocate a bound ID for them. The file is read during optimization and the products are located together after the arrangement of the optimization. Bounded productThe reserved productIn some cases, you might want certain products to be arranged in specific LPARs after optimization. The optimization stream supports this scenario by using a reserving feature. For each product expected to be arranged in each LPAR, in the input file you can input the product ID, set the reserved flag, and specify the reserved amount. The new distribution of the products is then contained in the product-LPAR combinations that are specified in the reserved information in the input file. Also, the MSU amount of the products in a reserved LPAR is the amount that is specified in the reserved information. Reserved productLogging and error handlingWhether the optimization is successful or not, a log is provided to view the running status. The log information is found in the file that is named prod_bill_msu_optimization_xxx.log, where xxx is the timestamp. The location of the log is specified by the LOGPATH parameter. If the optimization is completed successfully, the final log shows an entry similar to: 2014-07-11 19:28:31, "Stream execution completed successfully." To provide better product performance, in most cases, the optimization doesn't include any products that have an MSU that is above the defined capacity of the LPAR or the capacity that is limited by the shared weight of the LPAR. This is to avoid delays in processing. However, before optimization, it is possible that the MSU usage of any product is already above the defined capacity of the LPAR or the capacity that is limited by the shared weight of the LPAR, so there might be a case that not all products can be arranged when the optimization is performed. Currently, the stream tries to increase the capacity of the LPAR and host the rest of the products in the LPAR. If the attempt fails, the stream produces a log entry and no result is output in the table. A log entry for increasing capacity successfully is similar to: 2014-07-11 17:20:40, "CMA-MW012: The total capacity of the LPARs is not enough for Product ID 5655-Y04, the optimization is now trying to increase the capacity of certain LPAR... " 2014-07-11 17:20:57,"CMA-MW009: The total capacity of the LPAR: JF0 is increased 17.86% percent to host the remaining of the Product ID 5655-Y04." In this case, the optimization is still considered as being successful, but certain LPARs mentioned in the log might have an MSU above the define capacity or weight limited capacity in peak hour. The percentage of the exceeded part is also given in the log. A log entry for increasing capacity unsuccessfully is similar to: 2014-07-09 04:35:07, "CMA-MW012: The total capacity of the LPARs is not enough for Product ID 5655-Y04, the optimization is now trying to increase the capacity of certain LPAR..." 2014-07-09 04:35:23, "CMA-ME013: The total capacity of the LPARs is not enough. No optimization suggestion could be given and the optimization related reports could not be generated." In this case, the optimization is unsuccessful. For more information about the error messages, see Error, warning, and installation messagesError, warning, and installation messages are displayed for IBM Capacity Management Analytics.. Input parametersThere are two types of inputs for the optimization stream: Input file: provides the stream the product information that is related to features used for optimization. Its name and location is CMAINSTANCE/data/opt_care_prod.csv, CMAINSTANCE/data/opt_reserved_prod.csv, and CMAINSTANCE/data/data/opt_bound_prod.csv. You can change the name of the file but the tab name and its format in each tab must be kept.opt_care_prod.csv: if there is no requirement for setting products as cared, do not input anything in this tab.PROD_ID: the IBM product ID of the cared product.CARED: the flag that indicates that the product is to be cared.Input any product ID that needs to lower cost in priority in PROD_ID field and set the CARED field as 1. If PROD_ID is entered but the corresponding CARED field is blank or is set to 0, the product is considered to be not cared.opt_bound_prod.csv: if there is no requirement for setting products as bounded, do not input anything in this tab.PROD_ID: the IBM product ID of the product that needs to be bounded in a group.BOUND_ID: the unique number that is allocated to the group.If two or more products need to be bounded, input the ID in the PROD_ID field and allocate a number for them. For each bounded group, the number should be the same for each product in the group.opt_reserved_prod.csv: if there is no requirement for reserving any products, do not input anything in this tab. This tab is used to input any product that needs to be reserved in any LPAR. PROD_ID: the IBM product ID of the reserved product.LPAR_NAME: the name of the LPAR that is reserved for the product.CPC_SERIAL_NO: the CPC number of the LPAR.RESERVED: the reserved flag.RESERVED_MSU: the reserved amount by MSU.If RESERVED is set as 1 and RESERVED_MSU is X MSU, the product is arranged in the LPAR with an amount equal or above X.If RESERVED is 1 and RESERVED_MSU is 0, the product is arranged in the LPAR with an amount above 0.If RESERVED is 1 and RESERVED_MSU is X MSU, but X is beyond the MSU the product can produce, then the product is kept in its original LPARs location after optimization is completed.If the product is reserved in an LPAR that cannot host it (for example, no capacity for the LPAR), then the reserve setting is ignored. However, the stream looks for any LPAR that is suitable for the product.Runtime parametersIf you are using the scripts, you can specify these runtime parameters in the cmasca.parms file that is located in the CMAINSTANCE/data folder: PUSERSELECTION: the capping calculation option, set from two strategies that are used to apply capping: “weight” or “define”. The default setting is "weight".PCAREFILE: the location of the opt_care_prod.csv input file.PRESERVEDFILE: the location of the opt_reserved_prod.csv input file.PBOUNDFILE: the location of the opt_bound_prod.csv input file.PODD: obsolete detection days.The opt_care_prod.csv, opt_reserved_prod.csv, or opt_bound_prod.csv file names can be changed if required.if you are using Modeler Client, you can specify the same runtime parameters as in a shell: Specify prod_bill_msu_optimization in ModellerMoving suggestionAfter the optimization stream is executed successfully, the moving suggestion of each product in the optimization can be viewed in this report: SCA Workspace, SCA LPAR MSU Utilization & SCA Product MSU Utilization. Suggested actions reportBillable MSUOnce the optimization stream is executed successfully, the billable MSU of each product after the optimization can be viewed this report: Billable MSUSPSS Modeler stream referenceMuch of the data in IBM Capacity Management Analytics reports and workspaces is processed using IBM SPSS Modeler. This software uses programs that are referred to as streams, which contain the instructions required to retrieve, prepare, model, and forecast the data.

Billable MSU optimization stream

This stream is used to determine how to rearrange qualified products, or parts of products, among LPARs to lower the total cost of the billable MSUs. A qualified product is an IBM product that can produce billable MSUs in the forecasted data. If the forecasted MSU usage for a product is zero or if it is not an IBM product, then it is not included in the optimization.

This optimization stream is named prod_bill_msu_optimization.str and is in the CMAHOME/features/cmasca/analytics folder.

The main objective of the optimization is to move each qualified product, or part of the product, to one or more suitable LPARs to reduce the billable MSUs and total cost for the product. The report includes suggestions to help you determine where you can move the product. If the suggestion is to move part of the product to an LPAR, the suggestion also includes a recommendation for the amount of the product to move. The amount is measured in MSUs, with a granularity of one MSU, and it is recommended that you move the product’s workload, equal to this MSU amount, to the suggested LPAR.

Figure 17. Billable MSU optimization stream

Figure 18. Billable MSU optimization stream

The results of this stream are output to the database and stored in four tables individually:

PROD_MSU_OPTIMIZATION—for new distribution of the products among the LPARs, at the product level.

LPAR_MSU_OPTIMIZATION—for new distribution of the products among the LPARs, at the LPAR level.

OPTIMIZATION_MOVEMENT—for moving suggestions that are used for the new distribution.

OPTIMIZATION_BILLABLE—for billable MSU calculated from the new distribution.

This stream performs the optimization based on the forecasted MSU usage of the products and LPARs.

Note: there could be one year for the forecasted data, but the optimization stream uses the first month of the forecasted data to determine the new distribution. You can assume that the configuration for this new distribution would be applied only for the next two months from the beginning of the forecasted data. It is better to have a short range for the optimization suggestion as the performance of the computer that is running the optimization might change significantly during a longer run and the forecasted data needs to be updated continually to give a more accurate prediction.

The second day in each month is when IBM SCRT billing occurs, therefore the starting day of the forecasted data that is input into the optimization stream is also the second day in each month. The stream prolongs the starting day to the second day in the next month if the actual beginning of the forecasted day is later than second day in the current month. The second day in the current month is used by the stream if the actual beginning of the forecasted day is the first or second day in the current month. Any forecasted data earlier than the starting day is ignored.

Capping strategy

During the optimization of the arrangement for one product to an LPAR, the usage of MSU of the product should not exceed the cap value of the LPAR. The cap value is calculated from the defined capacity of the LPAR and the capacity that is limited by the shared weight of the LPAR. It is assumed that these two values are near to each other in most cases, and the capping value would be the minimum one of these two values. However, if the defined capacity is significantly bigger than the other (for example, five percent for the difference part), there is a parameter to control the capping value calculation strategy. There are two choices:

"weight": means to use the capacity that is limited by the shared weight as the capacity capping of the LPAR, but this selection might waste available capability that the LPAR has from the defined capacity and, therefore, decreases the possibility of an optimized result.

“define”: means to use the define capacity as the capacity capping of the LPAR, but this selection might cause certain products to not get the capability expected from the define capacity when CEC is busy and the product's performance will be degraded.

The parameter “PUSERSELECTION” is used to specify the choice.

The cared product

By default, the stream is trying to reduce the billable MSU for all qualified products. You can also designate important products as cared products to notify the stream to lower the cost of the products in priority. This is because there could be a case for an optimization result where the total cost of the summed billable MSU of all the products is reduced with no cost reduction or increase for some of its products. Therefore, this feature can identify these products to guarantee cost reduction for cared products with no guarantee for cost reduction for a non-cared product. You can specify a cared product by inputting the product ID and set the cared flag in the input file.

The bounded product

In some cases, products can be related and should appear together in the LPARs arranged for them. For example, product A relies on product B, therefore, in each LPAR where product A is located, product B must exist. The optimization stream supports this scenario by using a bounding feature. For each product group that contains products that are related, you can input them in the input file and allocate a bound ID for them. The file is read during optimization and the products are located together after the arrangement of the optimization.

The reserved product

In some cases, you might want certain products to be arranged in specific LPARs after optimization. The optimization stream supports this scenario by using a reserving feature. For each product expected to be arranged in each LPAR, in the input file you can input the product ID, set the reserved flag, and specify the reserved amount. The new distribution of the products is then contained in the product-LPAR combinations that are specified in the reserved information in the input file. Also, the MSU amount of the products in a reserved LPAR is the amount that is specified in the reserved information.

Logging and error handling

Whether the optimization is successful or not, a log is provided to view the running status. The log information is found in the file that is named prod_bill_msu_optimization_xxx.log, where xxx is the timestamp. The location of the log is specified by the LOGPATH parameter. If the optimization is completed successfully, the final log shows an entry similar to:

2014-07-11 19:28:31, "Stream execution completed successfully."

To provide better product performance, in most cases, the optimization doesn't include any products that have an MSU that is above the defined capacity of the LPAR or the capacity that is limited by the shared weight of the LPAR. This is to avoid delays in processing.

However, before optimization, it is possible that the MSU usage of any product is already above the defined capacity of the LPAR or the capacity that is limited by the shared weight of the LPAR, so there might be a case that not all products can be arranged when the optimization is performed. Currently, the stream tries to increase the capacity of the LPAR and host the rest of the products in the LPAR. If the attempt fails, the stream produces a log entry and no result is output in the table.

A log entry for increasing capacity successfully is similar to:

2014-07-11 17:20:40, "CMA-MW012: The total capacity of the LPARs is not enough for Product ID 5655-Y04, the optimization is now trying to increase the capacity of certain LPAR... "

2014-07-11 17:20:57,"CMA-MW009: The total capacity of the LPAR: JF0 is increased 17.86% percent to host the remaining of the Product ID 5655-Y04."

In this case, the optimization is still considered as being successful, but certain LPARs mentioned in the log might have an MSU above the define capacity or weight limited capacity in peak hour. The percentage of the exceeded part is also given in the log.

A log entry for increasing capacity unsuccessfully is similar to:

2014-07-09 04:35:07, "CMA-MW012: The total capacity of the LPARs is not enough for Product ID 5655-Y04, the optimization is now trying to increase the capacity of certain LPAR..."

2014-07-09 04:35:23, "CMA-ME013: The total capacity of the LPARs is not enough. No optimization suggestion could be given and the optimization related reports could not be generated."

In this case, the optimization is unsuccessful.

For more information about the error messages, see Error, warning, and installation messages.

Input parameters

There are two types of inputs for the optimization stream:

Input file: provides the stream the product information that is related to features used for optimization. Its name and location is CMAINSTANCE/data/opt_care_prod.csv, CMAINSTANCE/data/opt_reserved_prod.csv, and CMAINSTANCE/data/data/opt_bound_prod.csv. You can change the name of the file but the tab name and its format in each tab must be kept.

opt_care_prod.csv: if there is no requirement for setting products as cared, do not input anything in this tab.

PROD_ID: the IBM product ID of the cared product.

CARED: the flag that indicates that the product is to be cared.

Input any product ID that needs to lower cost in priority in PROD_ID field and set the CARED field as 1. If PROD_ID is entered but the corresponding CARED field is blank or is set to 0, the product is considered to be not cared.

opt_bound_prod.csv: if there is no requirement for setting products as bounded, do not input anything in this tab.

PROD_ID: the IBM product ID of the product that needs to be bounded in a group.

BOUND_ID: the unique number that is allocated to the group.

If two or more products need to be bounded, input the ID in the PROD_ID field and allocate a number for them. For each bounded group, the number should be the same for each product in the group.

opt_reserved_prod.csv: if there is no requirement for reserving any products, do not input anything in this tab. This tab is used to input any product that needs to be reserved in any LPAR.

PROD_ID: the IBM product ID of the reserved product.

LPAR_NAME: the name of the LPAR that is reserved for the product.

CPC_SERIAL_NO: the CPC number of the LPAR.

RESERVED: the reserved flag.

RESERVED_MSU: the reserved amount by MSU.

If RESERVED is set as 1 and RESERVED_MSU is X MSU, the product is arranged in the LPAR with an amount equal or above X.

If RESERVED is 1 and RESERVED_MSU is 0, the product is arranged in the LPAR with an amount above 0.

If RESERVED is 1 and RESERVED_MSU is X MSU, but X is beyond the MSU the product can produce, then the product is kept in its original LPARs location after optimization is completed.

If the product is reserved in an LPAR that cannot host it (for example, no capacity for the LPAR), then the reserve setting is ignored. However, the stream looks for any LPAR that is suitable for the product.

Runtime parameters

If you are using the scripts, you can specify these runtime parameters in the cmasca.parms file that is located in the CMAINSTANCE/data folder:

PUSERSELECTION: the capping calculation option, set from two strategies that are used to apply capping: “weight” or “define”. The default setting is "weight".

PCAREFILE: the location of the opt_care_prod.csv input file.

PRESERVEDFILE: the location of the opt_reserved_prod.csv input file.

PBOUNDFILE: the location of the opt_bound_prod.csv input file.

PODD: obsolete detection days.

Note: The opt_care_prod.csv, opt_reserved_prod.csv, or opt_bound_prod.csv file names can be changed if required.

if you are using Modeler Client, you can specify the same runtime parameters as in a shell:

Moving suggestion

After the optimization stream is executed successfully, the moving suggestion of each product in the optimization can be viewed in this report: SCA Workspace, SCA LPAR MSU Utilization & SCA Product MSU Utilization.

Billable MSU

Once the optimization stream is executed successfully, the billable MSU of each product after the optimization can be viewed this report:

Parent topic: SPSS Modeler stream reference

Application average forecast streamThe appl_avg_hourly_forecast_timeseries.str and appl_avg_daily_forecast_timeseries.str streams are used to predict the application future values of CP_SECONDS, IFA_ SECONDS, and IIP_ SECONDS for hourly and daily levels. The Application Analytics reports use the forecast data that is provided by these streams to calculate MIPS values.Forecasting approach The first step in creating a forecast is to retrieve the data from the database and aggregate it up to the appropriate level. In the hourly forecast stream, the data is aggregated by function key, environment key, APPL key, CPC, MVS_SYSTEM_ID, DATE, HOUR to hourly level to sum the MEASURED_SEC, CP_SECONDS, IIP_SECONDS, and IFA_SECONDS, which are marked as MEASURED_SEC_Sum, CP_SECONDS_Sum, IIP_SECONDS_Sum, and IFA_SECONDS_Sum accordingly. Then, the stream uses time series to forecast CP_SECONDS_Sum, IIP_SECONDS_Sum and IFA_SECONDS_Sum. The daily forecast stream aggregates the data to the daily level.Prompt parameters CMDW: the CMA database connection.CMASCHEMA: the schema for CMA.UID: the database user.PWD: the database user's password.OUTPUTMODELPATH: model output path.LOGPATH: the log path.INPUTHOLIDAYFILE: the input holiday file.TMPPATH: temporary data saved path.DATE_START: the start date for the data that is used.DATE_END: the end date for the data that is used.HOUR_START: the start hour for the data that is used.HOUR_END: the end hour for the data that is used. If you do not specify all of the parameters or if you specify incorrect parameters, the stream stops and an error is written to the logs specified in LOGPATH. If you have not specifies the LOGPATH or the path is no valid, then the stream stops and a message is displayed. The stream verifies the database connection and the values for the INPUTHOLIDAYFILE and TMPPATH parameters before the stream is run into the main branch. For the special parameters DATE_START, DATE_END, HOUR_START, and HOUR_END, the stream verifies that DATE_START < DATE_END, HOUR_START <= HOUR_END, and HOUR_START and HOUR_END are in range[0,23]. If the values that you specified for parameters DATE_START and DATE_END are out of range between the actual history start date and the history end date, then the stream forecasts using the actual history start or actual history end date and a warning message shows the date range that is used by the stream.Source table CMASCHEMA.APPL_MIPS_UTIL_ZOS_VIEWExport table CMASCHEMA.APPL_ZOS_FORECAST CMASCHEMA.FORECAST_METADATA These two streams export forecast seconds data to the CMASCHEMA.APPL_ZOS_FORECAST table with FORECAST_LEVEL= "avg_hourly" or FORECAST_LEVEL="avg_daily". These two streams each insert one record of forecast stream metadata properties into the CMASCHEMA.FORECAST_METADATA table. For hourly forecast stream, STREAM_TYPE="appl_sec_avg_hourly" and AGG_METHOD="avg_hourly", and for the daily forecast stream, STREAM_TYPE="appl_sec_avg_daily" and FORECAST_LEVEL="avg_daily". When the streams insert new data into the tables, the data from the last time the stream was run for the FORECAST_LEVEL is deleted.Time interval For the hourly forecast stream, the time interval nodes on “Number of hours in a day” and “Day begins at” can be automatically set so that they are consistent with the vales that are specified in the HOUR_START and HOUR_END parameters by using scripts. The number of hours in a day = HOUR_END - HOUR_START + 1, and Day begins at = HOUR_START. For the daily forecast stream, because we forecast seconds in daily level, we use the observed data from HOUR_START to HOUR_END to summarize to daily level and then predict the daily seconds. We do not have to specify the time interval by using scripts. The hourly stream forecast seconds for next 60 days. If the specified time range is 0 to 23 for HOUR_START and HOUR_END, then the stream predicts for a future 24 multiplied by 60 records, which is equal to 1440 records. The stream adjusts the predicted records number by using scripts automatically. The daily forecast stream predicts seconds for the next 1 year, which is always 365 records.Inputs for time series The holiday file that you specified in the INPUTHOLIDAYFILE parameter. For these two streams, the holiday date file is the only default input for the time series.Log file and error handling You can find the IBM SPSS Modeler stream log files in the log folder that you specify in the LOGPATH parameter. The log file is named appl_avg_hourly_forecast_timeseries_<timestamp>.log or appl_avg_daily_forecast_timeseries_<timestamp>.log. For information about error and warning messages, see Error, warning, and installation messagesError, warning, and installation messages are displayed for IBM Capacity Management Analytics..SPSS Modeler stream referenceMuch of the data in IBM Capacity Management Analytics reports and workspaces is processed using IBM SPSS Modeler. This software uses programs that are referred to as streams, which contain the instructions required to retrieve, prepare, model, and forecast the data.

Application average forecast stream

The appl_avg_hourly_forecast_timeseries.str and appl_avg_daily_forecast_timeseries.str streams are used to predict the application future values of CP_SECONDS, IFA_ SECONDS, and IIP_ SECONDS for hourly and daily levels. The Application Analytics reports use the forecast data that is provided by these streams to calculate MIPS values.

Forecasting approach

The first step in creating a forecast is to retrieve the data from the database and aggregate it up to the appropriate level. In the hourly forecast stream, the data is aggregated by function key, environment key, APPL key, CPC, MVS_SYSTEM_ID, DATE, HOUR to hourly level to sum the MEASURED_SEC, CP_SECONDS, IIP_SECONDS, and IFA_SECONDS, which are marked as MEASURED_SEC_Sum, CP_SECONDS_Sum, IIP_SECONDS_Sum, and IFA_SECONDS_Sum accordingly. Then, the stream uses time series to forecast CP_SECONDS_Sum, IIP_SECONDS_Sum and IFA_SECONDS_Sum. The daily forecast stream aggregates the data to the daily level.

Prompt parameters

CMDW: the CMA database connection.

CMASCHEMA: the schema for CMA.

UID: the database user.

PWD: the database user's password.

OUTPUTMODELPATH: model output path.

LOGPATH: the log path.

INPUTHOLIDAYFILE: the input holiday file.

TMPPATH: temporary data saved path.

DATE_START: the start date for the data that is used.

DATE_END: the end date for the data that is used.

HOUR_START: the start hour for the data that is used.

HOUR_END: the end hour for the data that is used.

If you do not specify all of the parameters or if you specify incorrect parameters, the stream stops and an error is written to the logs specified in LOGPATH. If you have not specifies the LOGPATH or the path is no valid, then the stream stops and a message is displayed. The stream verifies the database connection and the values for the INPUTHOLIDAYFILE and TMPPATH parameters before the stream is run into the main branch.

For the special parameters DATE_START, DATE_END, HOUR_START, and HOUR_END, the stream verifies that DATE_START < DATE_END, HOUR_START <= HOUR_END, and HOUR_START and HOUR_END are in range[0,23]. If the values that you specified for parameters DATE_START and DATE_END are out of range between the actual history start date and the history end date, then the stream forecasts using the actual history start or actual history end date and a warning message shows the date range that is used by the stream.

Source table

CMASCHEMA.APPL_MIPS_UTIL_ZOS_VIEW

Export table

CMASCHEMA.APPL_ZOS_FORECAST

CMASCHEMA.FORECAST_METADATA

These two streams export forecast seconds data to the CMASCHEMA.APPL_ZOS_FORECAST table with FORECAST_LEVEL= "avg_hourly" or FORECAST_LEVEL="avg_daily".

These two streams each insert one record of forecast stream metadata properties into the CMASCHEMA.FORECAST_METADATA table. For hourly forecast stream, STREAM_TYPE="appl_sec_avg_hourly" and AGG_METHOD="avg_hourly", and for the daily forecast stream, STREAM_TYPE="appl_sec_avg_daily" and FORECAST_LEVEL="avg_daily".

When the streams insert new data into the tables, the data from the last time the stream was run for the FORECAST_LEVEL is deleted.

Time interval

For the hourly forecast stream, the time interval nodes on “Number of hours in a day” and “Day begins at” can be automatically set so that they are consistent with the vales that are specified in the HOUR_START and HOUR_END parameters by using scripts. The number of hours in a day = HOUR_END - HOUR_START + 1, and Day begins at = HOUR_START.

For the daily forecast stream, because we forecast seconds in daily level, we use the observed data from HOUR_START to HOUR_END to summarize to daily level and then predict the daily seconds. We do not have to specify the time interval by using scripts.

The hourly stream forecast seconds for next 60 days. If the specified time range is 0 to 23 for HOUR_START and HOUR_END, then the stream predicts for a future 24 multiplied by 60 records, which is equal to 1440 records. The stream adjusts the predicted records number by using scripts automatically. The daily forecast stream predicts seconds for the next 1 year, which is always 365 records.

Inputs for time series

The holiday file that you specified in the INPUTHOLIDAYFILE parameter. For these two streams, the holiday date file is the only default input for the time series.

Log file and error handling

You can find the IBM SPSS Modeler stream log files in the log folder that you specify in the LOGPATH parameter. The log file is named appl_avg_hourly_forecast_timeseries_<timestamp>.log or appl_avg_daily_forecast_timeseries_<timestamp>.log.

For information about error and warning messages, see Error, warning, and installation messages.

Parent topic: SPSS Modeler stream reference

Application peak value forecast streamThe application peak value forecast stream (appl_peak_daily_forecast_timeseries.str) analyzes a defined time range of the history data for CPU seconds, calculates its peak value of MIPS, and forecast its one year peak value at an application level for the future.CPU seconds and MIPS The CPU seconds refer to three types of processor seconds: CP_SECONDS, IFA_ SECONDS, and IIP_ SECONDS. MIPS is calculated based on the CPU seconds, so there are three types of MIPS derived from three types of the CPU seconds. Application level The application level is defined by the field APPL_NAME. A different value in the field results in a different application level.Peak values The peak values are defined as a serial formed by the Top-N MIPS values in each day, where N is specified as the Peak Highest Rank within the range [1,24]. You can also input multiple N values to forecast multiple peak value serials in a single run. The maximum value of N is 5.Input table APPL_MIPS_UTIL_ZOS_VIEW: the source of history data for CPU seconds in each measurement interval.MIPS_CAPACITY: this table is used to look up the MIPS_TOTAL_CAPACITY to calculate the MIPS.APPL_MAPPING: this table is used to derive the value of MAPPING_TIME.Output table APPL_ZOS_FORECAST: the forecasted data and the related history data for MIPS of each day by each application level for each Peak Highest Rank is stored in this table.FORECAST_METADATA: the execution information of peak forecasting stream for each Peak Highest Rank is stored in this table.Input parameters CMDW: the CMA database connection.UID: the database user.PWD: the database user's password.CMASCHEMA: the schema for CMA.OUTPUTMODELPATH: the model output pathLOGPATH: the logging path. INPUTHOLIDAYFILE: the input holiday file.TMPPATH: temporary data saved path.DATE_START: the start date for the data that is used.DATE_END: the end date for the data that is used.HOUR_START: the start hour for the data that is used.HOUR_END: the end hour for the data that is used.PEAKRANK: the Nth highest peak rank values. Valid values are in the range [1,5]. You can also input multiple rank values. For the DATE_END and HOUR_END parameter, the inputted value must be greater than the DATE_START and HOUR_START values, respectively. For PEAKRANK, use a comma (,) to separate rank values if you are using multiple rank values.Validating inputs The stream verifies that input values are valid. For example, the stream verifies whether the path and database are writable. If not, it stops and an error is written to the log file. The log file is stored in LOGPATH.Forecast data generation and output There is a Python script in the peak forecast stream that drives the process. The stream finds the peak values for each type of MIPS by each Peak Highest Rank N by each application level within each day. The peak MIPS values are selected from the MIPS values by each hour within a day. After a serial of peak values is produced, the stream analyzes the data by using the Time Serials model, outputs and stores each of the models that are trained in OUTPUTMODELPATH, and uses them to forecast one year of peak values in the future for each type of MIPS by each application level. For each related Peak Highest Rank N of the forecasted data, peak_n is labeled in the field FORECAST_LEVEL. The saved models are named APPL_NAME_<timestamp>.gm. After the forecast values are produced, they are gathered in a temporary file that is stored in TMPPATH and output to the database. If the database already contains MIPS forecasted data with the same “peak_n”, the new values overwrite the existing values. Values that do not have the same “peak_n” as the new values are kept in the database. Execution data generation and output The information of the latest stream execution time and the data time range for history data and forecast data along with related Peak Highest Rank “peak_n” (represented by filed AGGREGATION in the table) are gathered when the forecasting runs. This information is written to the database after the forecasting finishes. Each running for each Peak Highest Rank makes one record in the table. If there are old records in the database, values with the same Peak Highest Rank are overwritten if new values are produced. Any existing values that do not have the same Peak Highest Rank are kept in the database.Log file and error handling You can find the IBM SPSS Modeler stream log files in the log folder that you specify in the LOGPATH parameter. The log file is named appl_peak_daily_forecast_timeseries_<<timestamp>.log. For information about error and warning messages, see Error, warning, and installation messagesError, warning, and installation messages are displayed for IBM Capacity Management Analytics..SPSS Modeler stream referenceMuch of the data in IBM Capacity Management Analytics reports and workspaces is processed using IBM SPSS Modeler. This software uses programs that are referred to as streams, which contain the instructions required to retrieve, prepare, model, and forecast the data.

Application peak value forecast stream

The application peak value forecast stream (appl_peak_daily_forecast_timeseries.str) analyzes a defined time range of the history data for CPU seconds, calculates its peak value of MIPS, and forecast its one year peak value at an application level for the future.

CPU seconds and MIPS

The CPU seconds refer to three types of processor seconds: CP_SECONDS, IFA_ SECONDS, and IIP_ SECONDS. MIPS is calculated based on the CPU seconds, so there are three types of MIPS derived from three types of the CPU seconds.

Application level

The application level is defined by the field APPL_NAME. A different value in the field results in a different application level.

Peak values

The peak values are defined as a serial formed by the Top-N MIPS values in each day, where N is specified as the Peak Highest Rank within the range [1,24]. You can also input multiple N values to forecast multiple peak value serials in a single run. The maximum value of N is 5.

Input table

APPL_MIPS_UTIL_ZOS_VIEW: the source of history data for CPU seconds in each measurement interval.

MIPS_CAPACITY: this table is used to look up the MIPS_TOTAL_CAPACITY to calculate the MIPS.

APPL_MAPPING: this table is used to derive the value of MAPPING_TIME.

Output table

APPL_ZOS_FORECAST: the forecasted data and the related history data for MIPS of each day by each application level for each Peak Highest Rank is stored in this table.

FORECAST_METADATA: the execution information of peak forecasting stream for each Peak Highest Rank is stored in this table.

Input parameters

CMDW: the CMA database connection.

UID: the database user.

PWD: the database user's password.

CMASCHEMA: the schema for CMA.

OUTPUTMODELPATH: the model output path

LOGPATH: the logging path.

INPUTHOLIDAYFILE: the input holiday file.

TMPPATH: temporary data saved path.

DATE_START: the start date for the data that is used.

DATE_END: the end date for the data that is used.

HOUR_START: the start hour for the data that is used.

HOUR_END: the end hour for the data that is used.

PEAKRANK: the Nth highest peak rank values. Valid values are in the range [1,5]. You can also input multiple rank values.

Note: For the DATE_END and HOUR_END parameter, the inputted value must be greater than the DATE_START and HOUR_START values, respectively.

For PEAKRANK, use a comma (,) to separate rank values if you are using multiple rank values.

Validating inputs

The stream verifies that input values are valid. For example, the stream verifies whether the path and database are writable. If not, it stops and an error is written to the log file. The log file is stored in LOGPATH.

Forecast data generation and output

There is a Python script in the peak forecast stream that drives the process. The stream finds the peak values for each type of MIPS by each Peak Highest Rank N by each application level within each day. The peak MIPS values are selected from the MIPS values by each hour within a day. After a serial of peak values is produced, the stream analyzes the data by using the Time Serials model, outputs and stores each of the models that are trained in OUTPUTMODELPATH, and uses them to forecast one year of peak values in the future for each type of MIPS by each application level. For each related Peak Highest Rank N of the forecasted data, peak_n is labeled in the field FORECAST_LEVEL. The saved models are named APPL_NAME_<timestamp>.gm.

After the forecast values are produced, they are gathered in a temporary file that is stored in TMPPATH and output to the database. If the database already contains MIPS forecasted data with the same “peak_n”, the new values overwrite the existing values. Values that do not have the same “peak_n” as the new values are kept in the database.

Execution data generation and output

The information of the latest stream execution time and the data time range for history data and forecast data along with related Peak Highest Rank “peak_n” (represented by filed AGGREGATION in the table) are gathered when the forecasting runs. This information is written to the database after the forecasting finishes. Each running for each Peak Highest Rank makes one record in the table. If there are old records in the database, values with the same Peak Highest Rank are overwritten if new values are produced. Any existing values that do not have the same Peak Highest Rank are kept in the database.

Log file and error handling

You can find the IBM® SPSS® Modeler stream log files in the log folder that you specify in the LOGPATH parameter. The log file is named appl_peak_daily_forecast_timeseries_<<timestamp>.log.

For information about error and warning messages, see Error, warning, and installation messages.

Parent topic: SPSS Modeler stream reference

Mapping table definitions for application analytics reportingThe application analytics reports categorize the application information by using mapping tables. The application mapping is defined in two spreadsheet files that are provided with the Capacity Management Analytics Solution Kit installation. one file (appl_lob_z.csv) is used for z/OS systems. The other (appl_lob.csv) is used for distributed systems.The appl_lob_z.csv and appl_lob.csv files are in the CMAINSTANCE/data directory.You must modify the files to suit your environment before you run the application analytics streams.Application mapping for z/OS systems The appl_lob_z.csv file is used for z/OS systems and allows you to map workloads based on their report class and job name into three categories that describe the application. The categories are application, environment, and function. The input is determined by either the REPORT_CLASS or the JOB_NAME from the SMF records. The user-defined values for APPL_NAME and FUNCTION determine a hierarchy where the APPL_NAME is the high-level name and FUNCTION is a sub-category of that application. The ENVIRONMENT value allows you to separate out different environments for the same application with multiple functions; for example, Prod, QA, Test, Devl, or whatever you define. The mapping file uses the following columns for classification: APPL_NAMEThe name of the application or collection of applications. This can be a single application or a collection of applications, depending on how you want to roll up the applications. This functional grouping allows you to subset large applications into smaller logical groups that map to business or application functions.ENVIRONMENTFor example, use Prod for production, Test or QA for quality assurance, Devl for development, or Sand for sandbox environments. You can use any environment classification that supports your separation of the workload of a single application.FUNCTIONThis is a second-level categorization for an application that allows you to have a group of applications with a single name but still have separate sub-application names in the FUNCTION field.REPORT_CLASSThe report class definition that is used in IBM Workload Manager for z/OS (WLM). You can define the report class for a function of an application. The default value is *. If you used * in the REPORT_CLASS column of the mapping table, use a specific value in the JOB_NAME column. The report class is specified in SMF 72 records, and the job name is specified in SMF 30 records. Do not use both a REPORT_CLASS and a JOB_NAME value for the same entry.JOB_NAMEThe detail address space name. The default value is *. If you used * in the JOB_NAME column of the mapping table, use a specific value in the REPORT_CLASS column. You can use wild-card characters for the JOB_NAME column. For example, you can enter values such as ABC*, ABC%, or AB%C. * represents any characters, whereas % represents only a single character. You cannot use both * and % in the same JOB_NAME value, and they cannot be used as the first character in a JOB_NAME value. * should be used only at the end of a string of characters, rather than in the middle. If you want to use multiple % characters in a string, you must use them only in adjacent positions in the string. You cannot separate % characters within the string. For example, you can use A%%D. But, you cannot use A%C%. If you specify a JOB_NAME of ABC* or ABC% and you also specify a JOB_NAME of ABCD, that is rolled up into the same FUNCTION or APPL_NAME, it results in ABCD being counted twice.TRANS_CAPTURE_RATIOThe ratio of the CPU time that is accounted-for in the SMF 30 records for the CICS address space to the total processor time that is measured in the SMF 110 records for the transactions that map to the REPORT_CLASS. If a report class is tracking a subset of transactions within a CICS region, both the SMF 30 records for that CICS region and the SMF 110 records are used to identify the transactions that use that report class. Capacity Management Analytics develops the TRANS_CAPTURE_RATIO and the Trans_Rate value by using a separate utility. Alternatively, you can provide the values in the mapping file. In this case, it is possible to see both REPORT_CLASS and JOB_NAME values used together. However, it applies only to CICS or IMS applications where you want to breakout some transactions into separate applications or functions. The default value for TRANS_CAPTURE_RATIO is 1.0.PROGRAM_TYPEThe report class or address space program type. For example, CICS, IMS, WAS, DB2. The default is blank.TRANS_RATECICS or IMS transaction counts per CPU second. The default is 0.FLAGIndicates that record is to be added, updated, or deleted from the database. The values can be ADD, UPDATE, or DELETE.Application mapping for distributed systems For distributed systems, a similar mapping is done, except that you can use only server names as input. A server is assumed to be running only one application or function. However, many servers can map to the same application or function. Use the appl_lob.csv mapping file to provide the server names, and to specify the values for the application name, environment, and function. APPL_NAMEThe name of the application or collection of applications. This can be a single application or a collection of applications, depending on how you want to roll up the applications. This functional grouping allows you to subset large applications into smaller logical groups that map to business or application functions.ENVIRONMENTFor example, use Prod for production, Test or QA for quality assurance, Devl for development, or Sand for sandbox environments. You can use any environment classification that supports your separation of the workload of a single application.FUNCTIONThis is a second-level categorization for an application that allows you to have a group of applications with a single name but still have separate sub-application names in the FUNCTION field.SERVERThe name of the server on which the application is running.SPSS Modeler stream referenceMuch of the data in IBM Capacity Management Analytics reports and workspaces is processed using IBM SPSS Modeler. This software uses programs that are referred to as streams, which contain the instructions required to retrieve, prepare, model, and forecast the data.

Mapping table definitions for application analytics reporting

The application analytics reports categorize the application information by using mapping tables. The application mapping is defined in two spreadsheet files that are provided with the Capacity Management Analytics Solution Kit installation. one file (appl_lob_z.csv) is used for z/OS® systems. The other (appl_lob.csv) is used for distributed systems.

The appl_lob_z.csv and appl_lob.csv files are in the CMAINSTANCE/data directory.

You must modify the files to suit your environment before you run the application analytics streams.

Application mapping for z/OS systems

The appl_lob_z.csv file is used for z/OS systems and allows you to map workloads based on their report class and job name into three categories that describe the application. The categories are application, environment, and function.

The input is determined by either the REPORT_CLASS or the JOB_NAME from the SMF records. The user-defined values for APPL_NAME and FUNCTION determine a hierarchy where the APPL_NAME is the high-level name and FUNCTION is a sub-category of that application. The ENVIRONMENT value allows you to separate out different environments for the same application with multiple functions; for example, Prod, QA, Test, Devl, or whatever you define.

The mapping file uses the following columns for classification:

APPL_NAME

The name of the application or collection of applications. This can be a single application or a collection of applications, depending on how you want to roll up the applications. This functional grouping allows you to subset large applications into smaller logical groups that map to business or application functions.

ENVIRONMENT

For example, use Prod for production, Test or QA for quality assurance, Devl for development, or Sand for sandbox environments. You can use any environment classification that supports your separation of the workload of a single application.

FUNCTION

This is a second-level categorization for an application that allows you to have a group of applications with a single name but still have separate sub-application names in the FUNCTION field.

REPORT_CLASS

The report class definition that is used in IBM® Workload Manager for z/OS (WLM). You can define the report class for a function of an application. The default value is *.

If you used * in the REPORT_CLASS column of the mapping table, use a specific value in the JOB_NAME column.

Note: The report class is specified in SMF 72 records, and the job name is specified in SMF 30 records. Do not use both a REPORT_CLASS and a JOB_NAME value for the same entry.

JOB_NAME

The detail address space name. The default value is *.

If you used * in the JOB_NAME column of the mapping table, use a specific value in the REPORT_CLASS column.

Tip: You can use wild-card characters for the JOB_NAME column. For example, you can enter values such as ABC*, ABC%, or AB%C. * represents any characters, whereas % represents only a single character. You cannot use both * and % in the same JOB_NAME value, and they cannot be used as the first character in a JOB_NAME value. * should be used only at the end of a string of characters, rather than in the middle.

If you want to use multiple % characters in a string, you must use them only in adjacent positions in the string. You cannot separate % characters within the string. For example, you can use A%%D. But, you cannot use A%C%.

If you specify a JOB_NAME of ABC* or ABC% and you also specify a JOB_NAME of ABCD, that is rolled up into the same FUNCTION or APPL_NAME, it results in ABCD being counted twice.

TRANS_CAPTURE_RATIO

The ratio of the CPU time that is accounted-for in the SMF 30 records for the CICS® address space to the total processor time that is measured in the SMF 110 records for the transactions that map to the REPORT_CLASS.

If a report class is tracking a subset of transactions within a CICS region, both the SMF 30 records for that CICS region and the SMF 110 records are used to identify the transactions that use that report class. Capacity Management Analytics develops the TRANS_CAPTURE_RATIO and the Trans_Rate value by using a separate utility. Alternatively, you can provide the values in the mapping file.

In this case, it is possible to see both REPORT_CLASS and JOB_NAME values used together. However, it applies only to CICS or IMS™ applications where you want to breakout some transactions into separate applications or functions.

The default value for TRANS_CAPTURE_RATIO is 1.0.

PROGRAM_TYPE

The report class or address space program type. For example, CICS, IMS, WAS, DB2. The default is blank.

TRANS_RATE

CICS or IMS transaction counts per CPU second. The default is 0.

FLAG

Indicates that record is to be added, updated, or deleted from the database. The values can be ADD, UPDATE, or DELETE.

Application mapping for distributed systems

For distributed systems, a similar mapping is done, except that you can use only server names as input. A server is assumed to be running only one application or function. However, many servers can map to the same application or function.

Use the appl_lob.csv mapping file to provide the server names, and to specify the values for the application name, environment, and function.

APPL_NAME

The name of the application or collection of applications. This can be a single application or a collection of applications, depending on how you want to roll up the applications. This functional grouping allows you to subset large applications into smaller logical groups that map to business or application functions.

ENVIRONMENT

For example, use Prod for production, Test or QA for quality assurance, Devl for development, or Sand for sandbox environments. You can use any environment classification that supports your separation of the workload of a single application.

FUNCTION

This is a second-level categorization for an application that allows you to have a group of applications with a single name but still have separate sub-application names in the FUNCTION field.

SERVER

The name of the server on which the application is running.

Parent topic: SPSS Modeler stream reference

Framework Manager model referenceThe IBM Cognos Framework Manager models used in IBM Capacity Management Analytics consists of several namespace/folder hierarchies that group tables and objects into a logically identifiable way within IBM Cognos Framework Manager. Framework ManagerThere are four data source connections available in the Capacity Management Analytics model: HCM—References the Capacity Management Analytics data store tables. The default schema is HCM.DRL—References the Tivoli Decision Support for z/OS (TDSz) data store tables. The default schema is DRL.DRLSYS—References the Tivoli Decision Support for z/OS metadata tables. The default schema is DRLSYS.There are two packages available in the Capacity Management Analytics model:Capacity Management Analytics—The Capacity Management Analytics package consists of only the tables, filters, and other components that are used in the final reports.Capacity Management Analytics Development—The Capacity Management Analytics Development package provides full access to all the tables and columns used to develop reports. When you verify the Development package, you may see some warning messages. Framework Manager repairs the errors automatically, so no action is required. The Capacity Management Analytics model is segmented into four layers: Physical View, Logical View, Presentation View, and Dimensional View. The four layers are built upon one another, with the Physical View being the foundation of the model.Layers within the Capacity Management Analytics modelImage of the Capacity Management Analytics model with four view layers: Physical View, Logical View, Presentation View, and Dimensional View.The Capacity Management Analytics Instance is the high-level container for Capacity Management Analytics which holds the four model layers.Capacity Management Analytics InstanceThe IBM Capacity Management Analytics Instance is the high-level container for Capacity Management Analytics which holds the four model layers.Opening the Capacity Management Analytics Model in Framework ManagerYou can view the IBM Capacity Management Analytics model in IBM Cognos Framework Manager. The Framework Manager project file (.cpf) for the Capacity Management Analytics model is included in the Capacity Management Analytics installation.Enabling a Capacity Management Analytics package with Dynamic Query ModeTo optimize performance, configure your IBM Capacity Management Analytics package to use Dynamic Query Mode (DQM).

Framework Manager model reference

The IBM® Cognos® Framework Manager models used in IBM Capacity Management Analytics consists of several namespace/folder hierarchies that group tables and objects into a logically identifiable way within IBM Cognos Framework Manager.

There are four data source connections available in the Capacity Management Analytics model:

HCM—References the Capacity Management Analytics data store tables. The default schema is HCM.

DRL—References the Tivoli® Decision Support for z/OS® (TDSz) data store tables. The default schema is DRL.

DRLSYS—References the Tivoli Decision Support for z/OS metadata tables. The default schema is DRLSYS.

There are two packages available in the Capacity Management Analytics model:

Capacity Management Analytics—The Capacity Management Analytics package consists of only the tables, filters, and other components that are used in the final reports.

Capacity Management Analytics Development—The Capacity Management Analytics Development package provides full access to all the tables and columns used to develop reports. When you verify the Development package, you may see some warning messages. Framework Manager repairs the errors automatically, so no action is required.

The Capacity Management Analytics model is segmented into four layers: Physical View, Logical View, Presentation View, and Dimensional View. The four layers are built upon one another, with the Physical View being the foundation of the model.

Figure 19. Layers within the Capacity Management Analytics model

The Capacity Management Analytics Instance is the high-level container for Capacity Management Analytics which holds the four model layers.

Capacity Management Analytics Instance
The IBM Capacity Management Analytics Instance is the high-level container for Capacity Management Analytics which holds the four model layers.

Opening the Capacity Management Analytics Model in Framework Manager
You can view the IBM Capacity Management Analytics model in IBM Cognos Framework Manager. The Framework Manager project file (.cpf) for the Capacity Management Analytics model is included in the Capacity Management Analytics installation.

Enabling a Capacity Management Analytics package with Dynamic Query Mode
To optimize performance, configure your IBM Capacity Management Analytics package to use Dynamic Query Mode (DQM).

Capacity Management Analytics InstanceThe IBM Capacity Management Analytics Instance is the high-level container for Capacity Management Analytics which holds the four model layers.Framework Manager model referenceThe IBM Cognos Framework Manager models used in IBM Capacity Management Analytics consists of several namespace/folder hierarchies that group tables and objects into a logically identifiable way within IBM Cognos Framework Manager. Dimensional viewThe Dimensional View contains regular dimensions, measure dimensions, and scope relationships that are created in Framework Manager.Presentation ViewThe Presentation View includes shortcuts to the existing items in the Logical View.Logical ViewThe Logical View organizes elements into logical query subjects.Physical ViewThe physical view, using SQL (“select * from TableName”) to retrieve table information, reflects the underlying tables and join relationships in the data source.

Capacity Management Analytics Instance

The IBM® Capacity Management Analytics Instance is the high-level container for Capacity Management Analytics which holds the four model layers.

Dimensional view
The Dimensional View contains regular dimensions, measure dimensions, and scope relationships that are created in Framework Manager.

Presentation View
The Presentation View includes shortcuts to the existing items in the Logical View.

Logical View
The Logical View organizes elements into logical query subjects.

Physical View
The physical view, using SQL (“select * from TableName”) to retrieve table information, reflects the underlying tables and join relationships in the data source.

Parent topic: Framework Manager model reference

Dimensional viewThe Dimensional View contains regular dimensions, measure dimensions, and scope relationships that are created in Framework Manager.Dimensional ViewThe dimensional reporting style is recommended for dimensionally-modeled relational (DMR) and Online Analytical Processing (OLAP) data sources. Dimensional data is best represented by crosstabs, maps, and charts. This data is shown in IBM Cognos Report Studio in dimensions, hierarchies, levels, and members. In dimensional reporting, you summarize data by using member summaries and within set aggregation. You focus data in dimensional reporting by adding only the relevant members to the edge of a crosstab or to the context filter. You can also enable drilling up and drilling down in dimensional reports.Shortcuts in the Dimensional ViewShortcut NameDescriptionTarget Object Reference Linux for System Z DIST DateThis table provides yearly, monthly, weekly and daily dimension from the ZLINUX_IMAGE resource used by RMF XP for Linux on System z. Data is taken from SMF 104 records subtype 43.[Dimensional view]. [DIST_DATE]Linux for System Z DIST HourThis table provides hourly dimension from the ZLINUX_IMAGE resource used by RMF XP for Linux on System z. Data is taken from SMF 104 records subtype 43.[Dimensional view]. [DIST_HOUR]Linux for System Z DIST TimeThis table provides minutely dimension from the ZLINUX_IMAGE resource used by RMF XP for Linux on System z. Data is taken from SMF 104 records subtype 43.[Dimensional view]. [DIST_Z_TIME]Linux for System Z ImageThis table provides image dimension from the ZLINUX_IMAGE resource used by RMF XP for Linux on System z. Data is taken from SMF 104 records subtype 43.[Dimensional view]. [Linux for System Z IMAGE]Linux for System Z CPU FactThis table provides CPU measure from the ZLINUX_IMAGE resource used by RMF XP for Linux on System z. Data is taken from SMF 104 records subtype 43.[Dimensional view]. [Linux for System Z CPU]Linux for System Z Memory FACTThis table provides Memory measure from the ZLINUX_IMAGE resource used by RMF XP for Linux on System z. Data is taken from SMF 104 records.[Dimensional view]. [Linux for System Z Memory]Linux for System Z CPU Hour FACTThis table provides hourly statistics on CPU measure from the ZLINUX_IMAGE resource used by RMF XP for Linux on System z. Data is taken from SMF 104 records subtype 43.[Dimensional view]. [Linux for System Z CPU Hour]Linux for System Z Image NameThis table provides image metrics from the ZLINUX_IMAGE resource used by RMF XP for Linux on System z. Data is taken from SMF 104 records subtype 43.[Logical View]. [Z_IMAGE]Linux for System Z CPU Raw MetricsThis table provides CPU metrics from the ZLINUX_IMAGE resource used by RMF XP for Linux on System z. Data is taken from SMF 104 records subtype 43.[Logical View]. [Z_OPERATING_SYS_T_CPU]Linux for System Z Memory Raw MetricsThis table provides Memory metrics from the ZLINUX_IMAGE resource used by RMF XP for Linux on System z. Data is taken from SMF 104 records subtype 43.[Logical View]. [Z_OPERATING_SYS_T_MEM]Linux for System Z CPU Hour MetricsThis table provides hourly statistics on CPU metrics from the ZLINUX_IMAGE resource used by RMF XP for Linux on System z. Data is taken from SMF 104 records subtype 43.[Logical View]. [Z_OPERATING_SYS_H_CPU]Linux for System X DIST DateThis table provides yearly, monthly, weekly and daily dimension from the XLINUX_IMAGE resource used by RMF XP for Linux on System z. Data is taken from SMF 104 records subtype 23.[Dimensional view]. [DIST_DATE]Linux for System X DIST HourThis table provides hourly dimension from the XLINUX_IMAGE resource used by RMF XP for Linux on System x. Data is taken from SMF 104 records subtype 23.[Dimensional view]. [DIST_HOUR]Linux for System X DIST TimeThis table provides minutely dimension from the XLINUX_IMAGE resource used by RMF XP for Linux on System x. Data is taken from SMF 104 records subtype 23.[Dimensional view]. [DIST_X_TIME]Linux for System X ImageThis table provides image dimension from the XLINUX_IMAGE resource used by RMF XP for Linux on System x. Data is taken from SMF 104 records subtype 23.[Dimensional view]. [Linux for System X IMAGE]Linux for System X CPU FactThis table provides CPU measure from the XLINUX_IMAGE resource used by RMF XP for Linux on System x. Data is taken from SMF 104 records subtype 23.[Dimensional view]. [Linux for System X CPU]Linux for System X Memory FACTThis table provides Memory measure from the XLINUX_IMAGE resource used by RMF XP for Linux on System x. Data is taken from SMF 104 records subtype 23.[Dimensional view]. [Linux for System X Memory]Linux for System X CPU Hour FACTThis table provides hourly statistics on CPU measure from the XLINUX_IMAGE resource used by RMF XP for Linux on System x. Data is taken from SMF 104 records subtype 23.[Dimensional view]. [Linux for System X CPU Hour]Linux for System X Image NameThis table provides image metrics from the XLINUX_IMAGE resource used by RMF XP for Linux on System x. Data is taken from SMF 104 records subtype 23.[Logical View]. [X_IMAGE]Linux for System X CPU Raw MetricsThis table provides CPU metrics from the XLINUX_IMAGE resource used by RMF XP for Linux on System x. Data is taken from SMF 104 records subtype 23.[Logical View]. [X_OPERATING_SYS_T_CPU]Linux for System X Memory Raw MetricsThis table provides Memory metrics from the XLINUX_IMAGE resource used by RMF XP for Linux on System x. Data is taken from SMF 104 records subtype 23.[Logical View]. [X_OPERATING_SYS_T_MEM]Linux for System X CPU Hour MetricsThis table provides hourly statistics on CPU metrics from the XLINUX_IMAGE resource used by RMF XP for Linux on System x. Data is taken from SMF 104 records subtype 23.[Logical View]. [X_OPERATING_SYS_H_CPU]AIX DIST_DateThis table provides yearly, monthly, weekly and daily dimension from the PARTITION resource used by RMF XP for AIX on System p. Data is taken from SMF 104 records subtype 3.[Dimensional view]. [DIST_DATE]AIX DIST_HourThis table provides hourly dimension from the PARTITION resource used by RMF XP for AIX on System p. Data is taken from SMF 104 records subtype 3.[Dimensional view]. [DIST_HOUR]AIX DIST TIMEThis table provides minutely dimension from the PARTITION resource used by RMF XP for AIX on System p. Data is taken from SMF 104 records subtype 3.[Dimensional view]. [DIST AIX TIME]AIX IMAGEThis table provides image dimension from the PARTITION resource used by RMF XP for AIX on System p. Data is taken from SMF 104 records subtype 3.[Dimensional view]. [AIX IMAGE]AIX CPU FactThis table provides CPU measure from the PARTITION resource used by RMF XP for AIX on System p. Data is taken from SMF 104 records subtype 3.[Dimensional view]. [AIX CPU]AIX CPU Hour FactThis table provides hourly statistics on CPU measure from the PARTITION resource used by RMF XP for AIX on System p. Data is taken from SMF 104 records subtype 3.[Dimensional view]. [AIX CPU Hour]AIX Memory FactThis table provides memory metrics from the AIX_IMAGE resource used by RMF XP for AIX on System p. Data is taken from SMF 104 records subtype 8, the MEMORY resource used by RMF XP for AIX on System p. Data is taken from SMF 104 records subtype 7 and the ACTIVE_MEMORY_EXPANSION resource used by RMF XP for AIX on System p. Data is taken from SMF 104 records subtype 1.[Dimensional view]. [AIX Memory]AIX CPU Raw MetricsThis table provides CPU metrics from the PARTITION resource used by RMF XP for AIX on System p. Data is taken from SMF 104 records subtype 3.[Logical View]. [A_COMPUTER_SYS_T_CPU]AIX CPU Raw Hour MetricsThis table provides hourly statistics on CPU metrics from the PARTITION resource used by RMF XP for AIX on System p. Data is taken from SMF 104 records subtype 3.[Logical View]. [A_COMPUTER_SYS_H_CPU]AIX OPERATING Memory Raw MetricsThis table provides memory metrics from the AIX_IMAGE resource used by RMF XP for AIX on System p. Data is taken from SMF 104 records subtype 8.[Logical View]. [A_OPERATING_SYS_T_MEM]AIX MEMORY Memory Raw MetricsThis table provides memory metrics from the MEMORY resource used by RMF XP for AIX on System p. Data is taken from SMF 104 records subtype 7.[Logical View]. [A_MEMORY_T_MEM]AIX EXPANSION Memory Raw MetricsThis table provides memory metrics from the ACTIVE_MEMORY_EXPANSION resource used by RMF XP for AIX on System p. Data is taken from SMF 104 records subtype 1.[Logical View]. [A_MEM_EXPANSION_T_MEM]WIN DIST DATEThis table provides yearly, monthly, weekly, and daily dimension from the WINDOWS_IMAGE resource used by RMF XP for Windows. Data is taken from SMF 104 records subtype 62.[Dimensional view]. [DIST_DATE]WIN DIST HOURThis table provides hourly dimension from the WINDOWS_IMAGE resource used by RMF XP for Windows. Data is taken from SMF 104 records subtype 62.[Dimensional view]. [DIST_HOUR]WIN DIST TIMEThis table provides minutely dimension from the WINDOWS_IMAGE resource used by RMF XP for Windows. Data is taken from SMF 104 records subtype 62.[Dimensional view]. [DIST WIN TIME]WIN IMAGEThis table provides image dimension from the WINDOWS_IMAGE resource used by RMF XP for Windows. Data is taken from SMF 104 records subtype 62.[Dimensional view]. [WIN IMAGE]WIN CPU FactThis table provides CPU metrics from the WINDOWS_IMAGE resource used by RMF XP for Windows. Data is taken from SMF 104 records subtype 62 and the LOGICAL_PROCESSOR resource used by RMF XP for Windows. Data is taken from SMF 104 records subtype 63.[Dimensional view]. [WIN CPU]WIN CPU Hour FactThis table provides hourly statistics on CPU measure from the WINDOWS_IMAGE resource used by RMF XP for Windows. Data is taken from SMF 104 records subtype 62.[Dimensional view]. [WIN CPU Hour]WIN Memory FactThis table provides memory measure from the WINDOWS_IMAGE resource used by RMF XP for Windows. Data is taken from SMF 104 records subtype 62.[Dimensional view]. [WIN Memory]WIN OPERATING CPU Raw MetricsThis table provides CPU metrics from the WINDOWS_IMAGE resource used by RMF XP for Windows. Data is taken from SMF 104 records subtype 62.[Logical View]. [W_OPERATING_SYS_T_CPU]WIN OPERATING CPU Hour MetricsThis table provides hourly statistics on CPU metrics from the WINDOWS_IMAGE resource used by RMF XP for Windows. Data is taken from SMF 104 records subtype 62.[Logical View]. [W_OPERATING_SYS_H_CPU]WIN PROCESSOR CPU Raw MetricsThis table provides CPU metrics from the LOGICAL_PROCESSOR resource used by RMF XP for Windows. Data is taken from SMF 104 records subtype 63.[Logical View]. [W_PROCESSOR_T_CPU]WIN MEM Raw MetricsThis table provides memory metrics from the WINDOWS_IMAGE resource used by RMF XP for Windows. Data is taken from SMF 104 records subtype 62.[Logical View]. [W_OPERATING_SYS_T_MEM]z/OS System inventory MetricsThis table provides hourly statistics on address space and processor activity on MVS system. It contains data from SMF type 70 records processed through a record procedure (DRL2S070). For details on this table, search for “MVSPM_SYSTEM_H” in Tivoli Decision Support for z/OS documentation on IBM Knowledge Center (www.ibm.com/support/knowledgecenter/SSH53X_1.8.1/com.ibm.tivoli.dszos.doc_1.8.1/drl5ft14534.htm%23spt0011).[Logical View]. [DIST_MVSPM_SYSTEM_H]Capacity Management Analytics InstanceThe IBM Capacity Management Analytics Instance is the high-level container for Capacity Management Analytics which holds the four model layers.

Dimensional view

The Dimensional View contains regular dimensions, measure dimensions, and scope relationships that are created in Framework Manager.

The dimensional reporting style is recommended for dimensionally-modeled relational (DMR) and Online Analytical Processing (OLAP) data sources. Dimensional data is best represented by crosstabs, maps, and charts. This data is shown in IBM® Cognos® Report Studio in dimensions, hierarchies, levels, and members.

In dimensional reporting, you summarize data by using member summaries and within set aggregation. You focus data in dimensional reporting by adding only the relevant members to the edge of a crosstab or to the context filter. You can also enable drilling up and drilling down in dimensional reports.

Table 19. Shortcuts in the Dimensional View

Shortcut Name
Description
Target Object Reference

Linux for System Z DIST Date
This table provides yearly, monthly, weekly and daily dimension from the ZLINUX_IMAGE resource used by RMF™ XP for Linux on System z®. Data is taken from SMF 104 records subtype 43.

[Dimensional view].
[DIST_DATE]

Linux for System Z DIST Hour
This table provides hourly dimension from the ZLINUX_IMAGE resource used by RMF XP for Linux on System z. Data is taken from SMF 104 records subtype 43.

[Dimensional view].
[DIST_HOUR]

Linux for System Z DIST Time
This table provides minutely dimension from the ZLINUX_IMAGE resource used by RMF XP for Linux on System z. Data is taken from SMF 104 records subtype 43.

[Dimensional view].
[DIST_Z_TIME]

Linux for System Z Image
This table provides image dimension from the ZLINUX_IMAGE resource used by RMF XP for Linux on System z. Data is taken from SMF 104 records subtype 43.

[Dimensional view].
[Linux for System Z IMAGE]

Linux for System Z CPU Fact
This table provides CPU measure from the ZLINUX_IMAGE resource used by RMF XP for Linux on System z. Data is taken from SMF 104 records subtype 43.

[Dimensional view].
[Linux for System Z CPU]

Linux for System Z Memory FACT
This table provides Memory measure from the ZLINUX_IMAGE resource used by RMF XP for Linux on System z. Data is taken from SMF 104 records.

[Dimensional view].
[Linux for System Z Memory]

Linux for System Z CPU Hour FACT
This table provides hourly statistics on CPU measure from the ZLINUX_IMAGE resource used by RMF XP for Linux on System z. Data is taken from SMF 104 records subtype 43.

[Dimensional view].
[Linux for System Z CPU Hour]

Linux for System Z Image Name
This table provides image metrics from the ZLINUX_IMAGE resource used by RMF XP for Linux on System z. Data is taken from SMF 104 records subtype 43.

[Logical View].
[Z_IMAGE]

Linux for System Z CPU Raw Metrics
This table provides CPU metrics from the ZLINUX_IMAGE resource used by RMF XP for Linux on System z. Data is taken from SMF 104 records subtype 43.

[Logical View].
[Z_OPERATING_SYS_T_CPU]

Linux for System Z Memory Raw Metrics
This table provides Memory metrics from the ZLINUX_IMAGE resource used by RMF XP for Linux on System z. Data is taken from SMF 104 records subtype 43.

[Logical View].
[Z_OPERATING_SYS_T_MEM]

Linux for System Z CPU Hour Metrics
This table provides hourly statistics on CPU metrics from the ZLINUX_IMAGE resource used by RMF XP for Linux on System z. Data is taken from SMF 104 records subtype 43.

[Logical View].
[Z_OPERATING_SYS_H_CPU]

Linux for System X DIST Date
This table provides yearly, monthly, weekly and daily dimension from the XLINUX_IMAGE resource used by RMF XP for Linux on System z. Data is taken from SMF 104 records subtype 23.

[Dimensional view].
[DIST_DATE]

Linux for System X DIST Hour
This table provides hourly dimension from the XLINUX_IMAGE resource used by RMF XP for Linux on System x. Data is taken from SMF 104 records subtype 23.

[Dimensional view].
[DIST_HOUR]

Linux for System X DIST Time
This table provides minutely dimension from the XLINUX_IMAGE resource used by RMF XP for Linux on System x. Data is taken from SMF 104 records subtype 23.

[Dimensional view].
[DIST_X_TIME]

Linux for System X Image
This table provides image dimension from the XLINUX_IMAGE resource used by RMF XP for Linux on System x. Data is taken from SMF 104 records subtype 23.

[Dimensional view].
[Linux for System X IMAGE]

Linux for System X CPU Fact
This table provides CPU measure from the XLINUX_IMAGE resource used by RMF XP for Linux on System x. Data is taken from SMF 104 records subtype 23.

[Dimensional view].
[Linux for System X CPU]

Linux for System X Memory FACT
This table provides Memory measure from the XLINUX_IMAGE resource used by RMF XP for Linux on System x. Data is taken from SMF 104 records subtype 23.

[Dimensional view].
[Linux for System X Memory]

Linux for System X CPU Hour FACT
This table provides hourly statistics on CPU measure from the XLINUX_IMAGE resource used by RMF XP for Linux on System x. Data is taken from SMF 104 records subtype 23.

[Dimensional view].
[Linux for System X CPU Hour]

Linux for System X Image Name
This table provides image metrics from the XLINUX_IMAGE resource used by RMF XP for Linux on System x. Data is taken from SMF 104 records subtype 23.

[Logical View].
[X_IMAGE]

Linux for System X CPU Raw Metrics
This table provides CPU metrics from the XLINUX_IMAGE resource used by RMF XP for Linux on System x. Data is taken from SMF 104 records subtype 23.

[Logical View].
[X_OPERATING_SYS_T_CPU]

Linux for System X Memory Raw Metrics
This table provides Memory metrics from the XLINUX_IMAGE resource used by RMF XP for Linux on System x. Data is taken from SMF 104 records subtype 23.

[Logical View].
[X_OPERATING_SYS_T_MEM]

Linux for System X CPU Hour Metrics
This table provides hourly statistics on CPU metrics from the XLINUX_IMAGE resource used by RMF XP for Linux on System x. Data is taken from SMF 104 records subtype 23.

[Logical View].
[X_OPERATING_SYS_H_CPU]

AIX® DIST_Date
This table provides yearly, monthly, weekly and daily dimension from the PARTITION resource used by RMF XP for AIX on System p. Data is taken from SMF 104 records subtype 3.

[Dimensional view].
[DIST_DATE]

AIX DIST_Hour
This table provides hourly dimension from the PARTITION resource used by RMF XP for AIX on System p. Data is taken from SMF 104 records subtype 3.

[Dimensional view].
[DIST_HOUR]

AIX DIST TIME
This table provides minutely dimension from the PARTITION resource used by RMF XP for AIX on System p. Data is taken from SMF 104 records subtype 3.

[Dimensional view].
[DIST AIX TIME]

AIX IMAGE
This table provides image dimension from the PARTITION resource used by RMF XP for AIX on System p. Data is taken from SMF 104 records subtype 3.

[Dimensional view].
[AIX IMAGE]

AIX CPU Fact
This table provides CPU measure from the PARTITION resource used by RMF XP for AIX on System p. Data is taken from SMF 104 records subtype 3.

[Dimensional view].
[AIX CPU]

AIX CPU Hour Fact
This table provides hourly statistics on CPU measure from the PARTITION resource used by RMF XP for AIX on System p. Data is taken from SMF 104 records subtype 3.

[Dimensional view].
[AIX CPU Hour]

AIX Memory Fact
This table provides memory metrics from the AIX_IMAGE resource used by RMF XP for AIX on System p. Data is taken from SMF 104 records subtype 8, the MEMORY resource used by RMF XP for AIX on System p. Data is taken from SMF 104 records subtype 7 and the ACTIVE_MEMORY_EXPANSION resource used by RMF XP for AIX on System p. Data is taken from SMF 104 records subtype 1.

[Dimensional view].
[AIX Memory]

AIX CPU Raw Metrics
This table provides CPU metrics from the PARTITION resource used by RMF XP for AIX on System p. Data is taken from SMF 104 records subtype 3.

[Logical View].
[A_COMPUTER_SYS_T_CPU]

AIX CPU Raw Hour Metrics
This table provides hourly statistics on CPU metrics from the PARTITION resource used by RMF XP for AIX on System p. Data is taken from SMF 104 records subtype 3.

[Logical View].
[A_COMPUTER_SYS_H_CPU]

AIX OPERATING Memory Raw Metrics
This table provides memory metrics from the AIX_IMAGE resource used by RMF XP for AIX on System p. Data is taken from SMF 104 records subtype 8.

[Logical View].
[A_OPERATING_SYS_T_MEM]

AIX MEMORY Memory Raw Metrics
This table provides memory metrics from the MEMORY resource used by RMF XP for AIX on System p. Data is taken from SMF 104 records subtype 7.

[Logical View].
[A_MEMORY_T_MEM]

AIX EXPANSION Memory Raw Metrics
This table provides memory metrics from the ACTIVE_MEMORY_EXPANSION resource used by RMF XP for AIX on System p. Data is taken from SMF 104 records subtype 1.

[Logical View].
[A_MEM_EXPANSION_T_MEM]

WIN DIST DATE
This table provides yearly, monthly, weekly, and daily dimension from the WINDOWS_IMAGE resource used by RMF XP for Windows. Data is taken from SMF 104 records subtype 62.

[Dimensional view].
[DIST_DATE]

WIN DIST HOUR
This table provides hourly dimension from the WINDOWS_IMAGE resource used by RMF XP for Windows. Data is taken from SMF 104 records subtype 62.

[Dimensional view].
[DIST_HOUR]

WIN DIST TIME
This table provides minutely dimension from the WINDOWS_IMAGE resource used by RMF XP for Windows. Data is taken from SMF 104 records subtype 62.

[Dimensional view].
[DIST WIN TIME]

WIN IMAGE
This table provides image dimension from the WINDOWS_IMAGE resource used by RMF XP for Windows. Data is taken from SMF 104 records subtype 62.

[Dimensional view].
[WIN IMAGE]

WIN CPU Fact
This table provides CPU metrics from the WINDOWS_IMAGE resource used by RMF XP for Windows. Data is taken from SMF 104 records subtype 62 and the LOGICAL_PROCESSOR resource used by RMF XP for Windows. Data is taken from SMF 104 records subtype 63.

[Dimensional view].
[WIN CPU]

WIN CPU Hour Fact
This table provides hourly statistics on CPU measure from the WINDOWS_IMAGE resource used by RMF XP for Windows. Data is taken from SMF 104 records subtype 62.

[Dimensional view].
[WIN CPU Hour]

WIN Memory Fact
This table provides memory measure from the WINDOWS_IMAGE resource used by RMF XP for Windows. Data is taken from SMF 104 records subtype 62.

[Dimensional view].
[WIN Memory]

WIN OPERATING CPU Raw Metrics
This table provides CPU metrics from the WINDOWS_IMAGE resource used by RMF XP for Windows. Data is taken from SMF 104 records subtype 62.

[Logical View].
[W_OPERATING_SYS_T_CPU]

WIN OPERATING CPU Hour Metrics
This table provides hourly statistics on CPU metrics from the WINDOWS_IMAGE resource used by RMF XP for Windows. Data is taken from SMF 104 records subtype 62.

[Logical View].
[W_OPERATING_SYS_H_CPU]

WIN PROCESSOR CPU Raw Metrics
This table provides CPU metrics from the LOGICAL_PROCESSOR resource used by RMF XP for Windows. Data is taken from SMF 104 records subtype 63.

[Logical View].
[W_PROCESSOR_T_CPU]

WIN MEM Raw Metrics
This table provides memory metrics from the WINDOWS_IMAGE resource used by RMF XP for Windows. Data is taken from SMF 104 records subtype 62.

[Logical View].
[W_OPERATING_SYS_T_MEM]

z/OS® System inventory Metrics

This table provides hourly statistics on address space and processor activity on MVS™ system. It contains data from SMF type 70 records processed through a record procedure (DRL2S070).

For details on this table, search for “MVSPM_SYSTEM_H” in Tivoli® Decision Support for z/OS documentation on IBM Knowledge Center (www.ibm.com/support/knowledgecenter/SSH53X_1.8.1/com.ibm.tivoli.dszos.doc_1.8.1/drl5ft14534.htm%23spt0011).

[Logical View].
[DIST_MVSPM_SYSTEM_H]

Parent topic: Capacity Management Analytics Instance

Presentation ViewThe Presentation View includes shortcuts to the existing items in the Logical View.Presentation ViewThe shortcuts available in the Presentation View are described in the following table. The Target Object References are shown with line breaks for publication formatting purposes only.Shortcuts in the Presentation ViewShortcut NameDescriptionTarget Object Reference Channel Path Activity MetricsThis table provides statistics on channel path activity on MVS systems. It contains data from SMF type 73 records. This table is updated by Tivoli Decision Support for z/OS MVSPM_CHANNEL_H table[Logical View]. [HCM_MVSPM_CHANNEL]CSA/ECSA/SQA/ESQA Usage MetricsThis table provides hourly statistics on the usage of virtual storage in the CSA and SQA. It contains data from SMF type 78, subtype 2 records. For more information, see MVSPM_VS_CSASQA_H in the Tivoli Decision Support for z/OSdocumentation (www.ibm.com/support/knowledgecenter/SSH53X_1.8.2/com.ibm.tivoli.dszos.doc.1.8.2/SysRef1/DRL5FT16.pdf).[Logical View]. [MVSPM_VS_CSASQA_H]Customer Pricing Information This table contains customer-provided pricing information for the all running products. [Logical View]. [CUSTOMER_PRICE]Device Activity MetricsThis table provides statistics on the I/O activity of MVS devices. The data comes from SMF type 74, subtype 1 records. This table is updated by Tivoli Decision Support for z/OS MVSPM_DEVICE_H table.[Logical View]. [HCM_MVSPM_DEVICE]LPAR Level Forecast MetricsThis table provides forecast results. All the forecast results come from three forecast streams: hourly, daily and monthly. For details of this table, see LPAR Level Forecast Metrics tableThis table provides forecast results. All the forecast results come from three forecast streams: hourly, daily and monthly..[Logical View]. [MVSPM_LPAR_CPU _BUSY_MIPS_PRED]LPAR MSU Forecast Metrics This table contains hourly MSU utilization forecasts for each LPAR.[Logical View]. [LPAR_MSU_FORECAST]LPAR Level MetricsThis table is a consolidated version of DRL.MVSPM_LPAR_H, which eliminates any duplicate entries from DRL.MVSPM_LPAR_H. For details of this table, see LPAR Level Metrics tableThis table is a consolidated version of DRL.MVSPM_LPAR_H, which eliminates any duplicate entries from DRL.MVSPM_LPAR_H..[Logical View]. [HCM_MVSPM_LPAR]LPAR MSU Optimization MetricsThis table contains LPAR MSUs based on the optimized product placement.[Logical View]. [LPAR_MSU_OPTIMIZATION]LPAR MSU Metrics This view returns LPAR MSU metrics between specific dates.[Logical View]. [MVSPM_LPAR_MSU_VIEW]MIPS Capacity RatingsThis table provides the CPU_SERIAL_MIPS rating for your processors. For details of this table, see MIPS Capacity Ratings tableThis table provides the CPU_SERIAL_MIPS rating for your processors..[Logical View]. [MIPS_CAPACITY]Movement Suggestion by Optimization Metrics This table contains suggested product placement after an optimization. [Logical View]. [OPTIMIZATION_MOVEMENT]MVS Paging Activity MetricsThis table provides statistics on paging activity on MVS systems. It contains data from SMF type 71 records. For more information, see MVSPM_PAGING_H in the Tivoli Decision Support for z/OSdocumentation (www.ibm.com/support/knowledgecenter/SSH53X_1.8.2/com.ibm.tivoli.dszos.doc.1.8.2/SysRef1/DRL5FT16.pdf).[Logical View]. [MVSPM_PAGING_H]NO89 Products This table contains customer-provided NO89 unregistered products. [Logical View]. [NO89_PRODUCTS]Parents of Sub-Capacity Reference-based Programs This table includes parent programs for the reference based sub-capacity programs. The table contains programs that are parents of sub-capacity reference-based programs [Logical View]. [PARENT_PROGRAMS]Product Billable MSU Forecast Metrics This table contains forecasted billable product MSU metrics.[Logical View]. [FORECAST_BILLABLE]Product Billable MSU Optimization Metrics This table holds all optimized results. These results are used to perform MSU optimized reporting.[Logical View]. [OPTIMIZATION_BILLABLE]Product MSU Forecast MetricsThis table contains hourly MSU utilization forecasts for each product. [Logical View]. [PROD_MSU_FORECAST]Product MSU Optimization Metrics This table contains products after being optimized across a CPC.[Logical View]. [PROD_MSU_OPTIMIZATION]Product MSU Utilization MetricsThis view shows the MSU utilization metrics for a product. The view contains usage information for sub-capacity products that generate SMF89 records. The view is used to determine the LPARs where sub-capacity products are being used and the amount of CPU capacity consumed by each program within the LPAR. This view joins content from Tivoli Decision Support for z/OS tables MVSPM_PROD_T and MVSPM_LPAR_MSU_T. It calculates MSUs consumed by the product and determines the SCRT reporting hour.[Logical View]. [MVSPM_PROD_VIEW]Prompts/CPC Prompt ViewThis is a united view of CPCs from Observed, Forecasted & Optimized data for the Software Cost Analysis workspace. [Logical View]. [MVSPM_CPC_PROMPT_VIEW]Prompts/LPAR MSU Prompt View This is a united view of CPCs and LPARs from Observed, Forecasted & Optimized data for the LPAR MSU report. [Logical View]. [MVSPM_LPAR_MSU _PROMPT_VIEW]Prompts/Product Billable MSU Prompt View This is a united view of products from Observed, Forecasted & Optimized data for the Product MSU report. [Logical View]. [MVSPM_PROD_BILLABLE _PROMPT_VIEW]Prompts/Product Prompt for LPAR MSU Report View This is a united view of products from Observed, Forecasted & Optimized data for the LPAR MSU report. [Logical View]. [MVSPM_PROD_MSU _PROMPT_VIEW]Product MSU Utilization Metrics Without Z/OS This view includes the same columns as the Product MSU Utilization Metrics view, but using different filters. [Logical View]. [MVSPM_PROD_VIEW _WITHOUT_ZOS]qry_User_InfoGet user name using session parameter $account.defaultName.[Logical View]. [qry_User_Info]Raw Device Activity MetricsThis table provides statistics on the I/O activity of MVS devices. It contains data on all devices that were active during an RMF monitor 1 session. The data comes from SMF type 74, subtype 1 records.[Logical View]. [DRL_MVSPM_DEVICE]Raw LPAR MSU Utilization Metrics This table contains the 4HRA information for each LPAR, irrespective of the individual programs and their pricing structures, running in the LPAR. [Logical View]. [MVSPM_LPAR_MSU_T]Raw LPAR Product MSU Utilization Metrics This view shows the subset of MSU utilization metrics for a product, the actual consumed MSU. [Logical View]. [MVSPM_PROD_RAW_MSU _UTILIZATION_VIEW]Raw Product MSU Utilization Metrics This table contains usage information for sub-capacity products that generate SMF89 records. The table is used to determine the LPARs where the sub-capacity products are being used and the amount of CPU used.[Logical View]. [MVSPM_PROD_T]Sub-Capacity Program Pricing Structures The table contains sub-capacity programs with pricing structure information. The table includes Program Pricing Structure information for IBM programs only. It is used to determine the type of MSU capacity calculation that should be done for the program. [Logical View]. [SUBCAP_PROGRAMS]TCP Server Port Connection MetricsThis table provides hourly TCP/IP statistics about the ports connection activity. Data is based on SMF record type 119 (subtype 7).[Logical View]. [TCP_TCPSERV_PORT_H]Timezone Metrics This table contains a listing of time zones.[Logical View]. [TIMEZONES]WLM Service Class Period Performance MetricsThis table provides statistics on MVS response time goals and actuals by workload type and service class. It contains data from SMF type 72, subtype 3 records. For more information, see MVSPM_GOAL_ACT_H in the Tivoli Decision Support for z/OSdocumentation (www.ibm.com/support/knowledgecenter/SSH53X_1.8.2/com.ibm.tivoli.dszos.doc.1.8.2/SysRef1/DRL5FT16.pdf).[Logical View]. [MVSPM_GOAL_ACT_H]WLM Service Class Period Usage/Delay MetricsThis table provides hourly statistics on MVS activity by workload type and service class, when the system is running in goal mode. It contains data from SMF type 72, subtype 3 records. For more information, see MVSPM_WORKLOAD2_H in the Tivoli Decision Support for z/OSdocumentation (www.ibm.com/support/knowledgecenter/SSH53X_1.8.2/com.ibm.tivoli.dszos.doc.1.8.2/SysRef1/DRL5FT16.pdf).[Logical View]. [MVSPM_WORKLOAD2_H]z/OS System Level MetricsThis table provides hourly statistics on address space and processor activity on MVS system. It contains data from SMF type 70 records processed through a record procedure (DRL2S070). For more information, see MVSPM_SYSTEM_H in the Tivoli Decision Support for z/OSdocumentation (www.ibm.com/support/knowledgecenter/SSH53X_1.8.2/com.ibm.tivoli.dszos.doc.1.8.2/SysRef1/DRL5FT16.pdf).[Logical View]. [MVSPM_SYSTEM_H]Z/OS Product MSU Utilization Metrics This view includes the same columns as the Product MSU Utilization Metrics view, but using different filters. [Logical View]. [MVSPM_PROD_VIEW_FOR_ZOS]Capacity Management Analytics InstanceThe IBM Capacity Management Analytics Instance is the high-level container for Capacity Management Analytics which holds the four model layers.Channel Path Activity Metrics tableThis table provides statistics on channel path activity on MVS systems. It contains data from SMF type 73 records. This table is updated by the Tivoli Decision Support for z/OS MVSPM_CHANNEL_H tableCustomer Pricing Information tableDevice Activity Metrics tableThis table provides statistics on the I/O activity of MVS devices. The data comes from SMF type 74, subtype 1 records. This table is updated by Tivoli Decision Support for z/OS MVSPM_DEVICE_H table.LPAR Level Forecast Metrics tableThis table provides forecast results. All the forecast results come from three forecast streams: hourly, daily and monthly.LPAR Level Metrics tableThis table is a consolidated version of DRL.MVSPM_LPAR_H, which eliminates any duplicate entries from DRL.MVSPM_LPAR_H.LPAR MSU Forecast Metrics tableLPAR MSU Optimization Metrics tableLPAR MSU Metrics tableMIPS Capacity Ratings tableThis table provides the CPU_SERIAL_MIPS rating for your processors.Movement Suggestion by Optimization Metrics tableNO89 Products tableParents of Sub-Capacity Reference-based Programs tableProduct Billable MSU Forecast Metrics tableProduct Billable MSU Optimization Metrics tableProduct MSU Forecast Metrics tableProduct MSU Optimization Metrics tableProduct MSU Utilization Metrics tablesThe Product MSU Utilization Metrics table, z/OS Product MSU Utilization Metrics table, and Product MSU Utilization Metrics without z/OS table all use the same structure.Prompts/CPC Prompt View tablePrompts/LPAR MSU Prompt View tablePrompts/Product Billable MSU Prompt View tablePrompts/Product Prompt for LPAR MSU Report View tableRaw LPAR Product MSU Utilization Metrics tableSub-Capacity Program Pricing Structures tableTimezone Metrics table

Presentation View

The Presentation View includes shortcuts to the existing items in the Logical View.

The shortcuts available in the Presentation View are described in the following table. The Target Object References are shown with line breaks for publication formatting purposes only.

Table 20. Shortcuts in the Presentation View

Shortcut Name
Description
Target Object Reference

Channel Path Activity Metrics
This table provides statistics on channel path activity on MVS™ systems. It contains data from SMF type 73 records. This table is updated by Tivoli® Decision Support for z/OS® MVSPM_CHANNEL_H table

[Logical View].
[HCM_MVSPM_CHANNEL]

CSA/ECSA/SQA/ESQA Usage Metrics
This table provides hourly statistics on the usage of virtual storage in the CSA and SQA. It contains data from SMF type 78, subtype 2 records. For more information, see MVSPM_VS_CSASQA_H in the Tivoli Decision Support for z/OSdocumentation (www.ibm.com/support/knowledgecenter/SSH53X_1.8.2/com.ibm.tivoli.dszos.doc.1.8.2/SysRef1/DRL5FT16.pdf).

[Logical View].
[MVSPM_VS_CSASQA_H]

Customer Pricing Information
This table contains customer-provided pricing information for the all running products.

[Logical View].
[CUSTOMER_PRICE]

Device Activity Metrics
This table provides statistics on the I/O activity of MVS devices. The data comes from SMF type 74, subtype 1 records. This table is updated by Tivoli Decision Support for z/OS MVSPM_DEVICE_H table.

[Logical View].
[HCM_MVSPM_DEVICE]

LPAR Level Forecast Metrics
This table provides forecast results. All the forecast results come from three forecast streams: hourly, daily and monthly. For details of this table, see LPAR Level Forecast Metrics table.

[Logical View].
[MVSPM_LPAR_CPU
_BUSY_MIPS_PRED]

LPAR MSU Forecast Metrics
This table contains hourly MSU utilization forecasts for each LPAR.

[Logical View].
[LPAR_MSU_FORECAST]

LPAR Level Metrics
This table is a consolidated version of DRL.MVSPM_LPAR_H, which eliminates any duplicate entries from DRL.MVSPM_LPAR_H. For details of this table, see LPAR Level Metrics table.

[Logical View].
[HCM_MVSPM_LPAR]

LPAR MSU Optimization Metrics
This table contains LPAR MSUs based on the optimized product placement.

[Logical View].
[LPAR_MSU_OPTIMIZATION]

LPAR MSU Metrics
This view returns LPAR MSU metrics between specific dates.

[Logical View].
[MVSPM_LPAR_MSU_VIEW]

MIPS Capacity Ratings
This table provides the CPU_SERIAL_MIPS rating for your processors. For details of this table, see MIPS Capacity Ratings table.

[Logical View].
[MIPS_CAPACITY]

Movement Suggestion by Optimization Metrics
This table contains suggested product placement after an optimization.

[Logical View].
[OPTIMIZATION_MOVEMENT]

MVS Paging Activity Metrics
This table provides statistics on paging activity on MVS systems. It contains data from SMF type 71 records. For more information, see MVSPM_PAGING_H in the Tivoli Decision Support for z/OSdocumentation (www.ibm.com/support/knowledgecenter/SSH53X_1.8.2/com.ibm.tivoli.dszos.doc.1.8.2/SysRef1/DRL5FT16.pdf).

[Logical View].
[MVSPM_PAGING_H]

NO89 Products
This table contains customer-provided NO89 unregistered products.

[Logical View].
[NO89_PRODUCTS]

Parents of Sub-Capacity Reference-based Programs
This table includes parent programs for the reference based sub-capacity programs. The table contains programs that are parents of sub-capacity reference-based programs

[Logical View].
[PARENT_PROGRAMS]

Product Billable MSU Forecast Metrics
This table contains forecasted billable product MSU metrics.

[Logical View].
[FORECAST_BILLABLE]

Product Billable MSU Optimization Metrics
This table holds all optimized results. These results are used to perform MSU optimized reporting.

[Logical View].
[OPTIMIZATION_BILLABLE]

Product MSU Forecast Metrics
This table contains hourly MSU utilization forecasts for each product.

 [Logical View].
[PROD_MSU_FORECAST]

Product MSU Optimization Metrics
This table contains products after being optimized across a CPC.

[Logical View].
[PROD_MSU_OPTIMIZATION]

Product MSU Utilization Metrics
This view shows the MSU utilization metrics for a product. The view contains usage information for sub-capacity products that generate SMF89 records. The view is used to determine the LPARs where sub-capacity products are being used and the amount of CPU capacity consumed by each program within the LPAR. This view joins content from Tivoli Decision Support for z/OS tables MVSPM_PROD_T and MVSPM_LPAR_MSU_T. It calculates MSUs consumed by the product and determines the SCRT reporting hour.

[Logical View].
[MVSPM_PROD_VIEW]

Prompts/CPC Prompt View
This is a united view of CPCs from Observed, Forecasted & Optimized data for the Software Cost Analysis workspace.

[Logical View].
[MVSPM_CPC_PROMPT_VIEW]

Prompts/LPAR MSU Prompt View
This is a united view of CPCs and LPARs from Observed, Forecasted & Optimized data for the LPAR MSU report.

[Logical View].
[MVSPM_LPAR_MSU
_PROMPT_VIEW]

Prompts/Product Billable MSU Prompt View
This is a united view of products from Observed, Forecasted & Optimized data for the Product MSU report.

[Logical View].
[MVSPM_PROD_BILLABLE
_PROMPT_VIEW]

Prompts/Product Prompt for LPAR MSU Report View
This is a united view of products from Observed, Forecasted & Optimized data for the LPAR MSU report.

[Logical View].
[MVSPM_PROD_MSU
_PROMPT_VIEW]

Product MSU Utilization Metrics Without Z/OS
This view includes the same columns as the Product MSU Utilization Metrics view, but using different filters.

[Logical View].
[MVSPM_PROD_VIEW
_WITHOUT_ZOS]

qry_User_Info
Get user name using session parameter $account.defaultName.

[Logical View].
[qry_User_Info]

Raw Device Activity Metrics
This table provides statistics on the I/O activity of MVS devices. It contains data on all devices that were active during an RMF™ monitor 1 session. The data comes from SMF type 74, subtype 1 records.

[Logical View].
[DRL_MVSPM_DEVICE]

Raw LPAR MSU Utilization Metrics
This table contains the 4HRA information for each LPAR, irrespective of the individual programs and their pricing structures, running in the LPAR.

[Logical View].
[MVSPM_LPAR_MSU_T]

Raw LPAR Product MSU Utilization Metrics
This view shows the subset of MSU utilization metrics for a product, the actual consumed MSU.

[Logical View].
[MVSPM_PROD_RAW_MSU
_UTILIZATION_VIEW]

Raw Product MSU Utilization Metrics
This table contains usage information for sub-capacity products that generate SMF89 records. The table is used to determine the LPARs where the sub-capacity products are being used and the amount of CPU used.

[Logical View].
[MVSPM_PROD_T]

Sub-Capacity Program Pricing Structures
The table contains sub-capacity programs with pricing structure information. The table includes Program Pricing Structure information for IBM programs only. It is used to determine the type of MSU capacity calculation that should be done for the program.

[Logical View].
[SUBCAP_PROGRAMS]

TCP Server Port Connection Metrics
This table provides hourly TCP/IP statistics about the ports connection activity. Data is based on SMF record type 119 (subtype 7).

[Logical View].
[TCP_TCPSERV_PORT_H]

Timezone Metrics
This table contains a listing of time zones.

[Logical View].
[TIMEZONES]

WLM Service Class Period Performance Metrics
This table provides statistics on MVS response time goals and actuals by workload type and service class. It contains data from SMF type 72, subtype 3 records. For more information, see MVSPM_GOAL_ACT_H in the Tivoli Decision Support for z/OSdocumentation (www.ibm.com/support/knowledgecenter/SSH53X_1.8.2/com.ibm.tivoli.dszos.doc.1.8.2/SysRef1/DRL5FT16.pdf).

[Logical View].
[MVSPM_GOAL_ACT_H]

WLM Service Class Period Usage/Delay Metrics
This table provides hourly statistics on MVS activity by workload type and service class, when the system is running in goal mode. It contains data from SMF type 72, subtype 3 records. For more information, see MVSPM_WORKLOAD2_H in the Tivoli Decision Support for z/OSdocumentation (www.ibm.com/support/knowledgecenter/SSH53X_1.8.2/com.ibm.tivoli.dszos.doc.1.8.2/SysRef1/DRL5FT16.pdf).

[Logical View].
[MVSPM_WORKLOAD2_H]

z/OS System Level Metrics
This table provides hourly statistics on address space and processor activity on MVS system. It contains data from SMF type 70 records processed through a record procedure (DRL2S070). For more information, see MVSPM_SYSTEM_H in the Tivoli Decision Support for z/OSdocumentation (www.ibm.com/support/knowledgecenter/SSH53X_1.8.2/com.ibm.tivoli.dszos.doc.1.8.2/SysRef1/DRL5FT16.pdf).

[Logical View].
[MVSPM_SYSTEM_H]

Z/OS Product MSU Utilization Metrics
This view includes the same columns as the Product MSU Utilization Metrics view, but using different filters.

[Logical View].
[MVSPM_PROD_VIEW_FOR_ZOS]

Channel Path Activity Metrics table
This table provides statistics on channel path activity on MVS systems. It contains data from SMF type 73 records. This table is updated by the Tivoli Decision Support for z/OS MVSPM_CHANNEL_H table

Customer Pricing Information table

Device Activity Metrics table
This table provides statistics on the I/O activity of MVS devices. The data comes from SMF type 74, subtype 1 records. This table is updated by Tivoli Decision Support for z/OS MVSPM_DEVICE_H table.

LPAR Level Forecast Metrics table
This table provides forecast results. All the forecast results come from three forecast streams: hourly, daily and monthly.

LPAR Level Metrics table
This table is a consolidated version of DRL.MVSPM_LPAR_H, which eliminates any duplicate entries from DRL.MVSPM_LPAR_H.

LPAR MSU Forecast Metrics table

LPAR MSU Optimization Metrics table

LPAR MSU Metrics table

MIPS Capacity Ratings table
This table provides the CPU_SERIAL_MIPS rating for your processors.

Movement Suggestion by Optimization Metrics table

NO89 Products table

Parents of Sub-Capacity Reference-based Programs table

Product Billable MSU Forecast Metrics table

Product Billable MSU Optimization Metrics table

Product MSU Forecast Metrics table

Product MSU Optimization Metrics table

Product MSU Utilization Metrics tables
The Product MSU Utilization Metrics table, z/OS Product MSU Utilization Metrics table, and Product MSU Utilization Metrics without z/OS table all use the same structure.

Prompts/CPC Prompt View table

Prompts/LPAR MSU Prompt View table

Prompts/Product Billable MSU Prompt View table

Prompts/Product Prompt for LPAR MSU Report View table

Raw LPAR Product MSU Utilization Metrics table

Sub-Capacity Program Pricing Structures table

Timezone Metrics table

Parent topic: Capacity Management Analytics Instance

Channel Path Activity Metrics tableThis table provides statistics on channel path activity on MVS systems. It contains data from SMF type 73 records. This table is updated by the Tivoli Decision Support for z/OS MVSPM_CHANNEL_H tableChannel Path Activity Metrics tableColumnDescriptionDATEDate of the mid measurement. From SMF73DAT, SMF73IST, and SMF73INT.TIMETime (rounded down as specified in the MVSPM_TIME_RES lookup table) of the mid measurement. From SMF73DAT, SMF73IST, and SMF73INT.PERIOD_NAMEName of the period. This is derived using fields SMF73DAT, SMF73IST, and SMF73INT from the record as parameters in the PERIOD function.MVS_SYSTEM_IDMVS system ID. This is the SMF system ID. From SMF73SID.CHANNEL_PATH_IDChannel path ID. From SMF73PID.CHANNEL_TYPEType of channel. From SMF73ACR.CHANNEL_FLAGSChannel flag bits. From SMF73FG3 and SMF73FG4. The following list shows bits and meaning when set. 0 - ES connection channel1 - ES connection director attached to the channel path2 - ES connection converter attached to this channel3 - Channel path modified4 - Channel path deleted5 - Channel path added6 - Valid channel path7 - Channel path is shared between logical partitions8 - CPMB entry is not valid9 - Channel path is channel-to-channel RECORD_LEVELSMF record level change number. This column enables processing of SMF record level changes in an existing release. From SMF73SRL.CHANNEL_BUSY_PCTAverage percentage of time that the channel path was busy. Calculated as the average of SMF73BSY*100/SMF73SMP. Please note this value may exceed 100% due to data from micro code.PART_CHAN_BUSY_PCTAverage percentage of time that the channel path was busy for the partition. Calculated as the average of SMF73PBY*100/SMF73PTI. Please note this value may exceed 100% due to data from micro code.CHANNEL_BUSY_MAXMaximum percentage of time that the channel path was busy during an RMF interval. Calculated as the maximum of SMF73BSY*100/SMF73SMP. Please note this value may exceed 100% due to data from micro code.PART_CHAN_BUSY_MAXMaximum percentage of time that the channel path was busy for the partition. Calculated as the maximum of SMF73PBY*100/SMF73PTI. Please note this value may exceed 100% due to data from micro code.TIME_RESOLUTIONTime resolution used for this table, in minutes. The default is 60 minutes. From TIME_RESOLUTION in the MVSPM_TIME_RES lookup table.MEASURED_SECMeasured time period, in seconds. This is the duration of the RMF measurement intervals. Calculated as the sum of SMF73INT/1000.SAMPLESNumber of store channel path status (STCP) samples taken by SRM. This is the sum of SMF73SMP.SAMPLES_BUSYNumber of store channel path status (STCP) samples taken by SRM in which the channel path related to this entry was found busy. This is the sum of SMF73BSY.CPMF_BUSY_SECLogical partitions channel path busy time as measured by the channel path measurement facility (CPMF), in seconds. Calculated as the sum of SMF73PBY*0.001024.CPMF_INTERVAL_SECSum of the interval times as measured by the channel path measurement facility (CPMF), in seconds. Calculated as the sum of SMF73PTI*0.001024.CPMF_GROUPCPMF channel measurement group. From SMF73CMGCHANNEL_BUSY_TOTTotal channel path busy time in seconds when the measurement group number is 1. Calculated as the sum of SMF73TUT/7.8125.PART_CHAN_BUSY_TOTLPAR channel path busy time in seconds when the measurement group number is 1. Calculated as the sum of SMF73PUT/7.8125.BUSCYCLE_MAX_SECMaximum bus cycles per second when the measurement group number is 2. Calculated as the maximum value of SMF73MBC.CHAN_UNIT_MAX_SECMaximum channel work units per second when the measurement group number is 2. Calculated as the maximum value of SMF73MCU.WRITE_MB_MAX_SECMaximum write data units per second when the measurement group number is 2. Calculated as the maximum value of SMF73MWU.READ_MB_MAX_SECMaximum read data units per second when the measurement group number is 2. Calculated as the maximum value of SMF73MRU.BUSCYCLEPercentage of bus cycles, the bus has been found busy for this channel in relation to the theoretical limit. Calculated as the average of (SMF73TBC*100.)/ (SMF73MBC*SMF73PTI*1024E-6). This value may exceed 100% due to data from micro code.CHAN_UNIT_TOTTotal channel work unit count when the measurement group Number is 2. Calculated as the sum of SMF73TUC.LPAR_CHAN_UNIT_TOTLPAR channel work unit count when the measurement group number is 2. Calculated as the sum of SMF73PUC.CHAN_WRITE_MB_TOTChannel write in MB when the measurement group number is 2. Calculated as the sum of SMF73TWU*(SMF73US/1024E3).LPAR_WRITE_MB_TOTLPAR write in MB when the measurement group number is 2. Calculated as the sum of SMF73PWU*(SMF73US/1024E6).CHAN_READ_MB_TOTChannel read in MB when the measurement group number is 2. Calculated as the sum of SMF73TRU*(SMF73US/1024E3).LPAR_READ_MB_TOTLPAR read in MB when the measurement group number is 2. Calculated as the sum of SMF73PRU*(SMF73US/1024E3).SHARED_INDChannel Path is shared between LPAR. This is Y (yes) if bit 8 of SMF73FG3 is on; otherwise, N (no).MULTI_SUBSYS_INDChannel Path is shared between LPAR. This is Y (yes) if bit 8 of SMF73FG3 is on; otherwise, N (no).CHANNEL_SUBSYS_IDChannel Subsystem ID. Valid only for hardware that supports Multiple Channel Subsystem. From SMF73CSS.DATA_TYPERank Flag id. A flag which distinguishes the Top N CHANNEL BUSY MAX record in this table.LPAR_NAMEName of the logical partition.CPU_SERIAL_NOLast 4 digits of the CPU serial number. LPAR_SYSTEM_IDSystem ID of the logical partition.Presentation ViewThe Presentation View includes shortcuts to the existing items in the Logical View.

Channel Path Activity Metrics table

This table provides statistics on channel path activity on MVS systems. It contains data from SMF type 73 records. This table is updated by the Tivoli® Decision Support for z/OS® MVSPM_CHANNEL_H table

Table 21. . Channel Path Activity Metrics table

Column
Description

DATE
Date of the mid measurement. From SMF73DAT, SMF73IST, and SMF73INT.

TIME
Time (rounded down as specified in the MVSPM_TIME_RES lookup table) of the mid measurement. From SMF73DAT, SMF73IST, and SMF73INT.

PERIOD_NAME
Name of the period. This is derived using fields SMF73DAT, SMF73IST, and SMF73INT from the record as parameters in the PERIOD function.

MVS_SYSTEM_ID
MVS system ID. This is the SMF system ID. From SMF73SID.

CHANNEL_PATH_ID
Channel path ID. From SMF73PID.

CHANNEL_TYPE
Type of channel. From SMF73ACR.

CHANNEL_FLAGS
Channel flag bits. From SMF73FG3 and SMF73FG4. The following list shows bits and meaning when set.

0 - ES connection channel

1 - ES connection director attached to the channel path

2 - ES connection converter attached to this channel

3 - Channel path modified

4 - Channel path deleted

5 - Channel path added

6 - Valid channel path

7 - Channel path is shared between logical partitions

8 - CPMB entry is not valid

9 - Channel path is channel-to-channel

RECORD_LEVEL
SMF record level change number. This column enables processing of SMF record level changes in an existing release. From SMF73SRL.

CHANNEL_BUSY_PCT
Average percentage of time that the channel path was busy. Calculated as the average of SMF73BSY*100/SMF73SMP. Please note this value may exceed 100% due to data from micro code.

PART_CHAN_BUSY_PCT
Average percentage of time that the channel path was busy for the partition. Calculated as the average of SMF73PBY*100/SMF73PTI. Please note this value may exceed 100% due to data from micro code.

CHANNEL_BUSY_MAX
Maximum percentage of time that the channel path was busy during an RMF interval. Calculated as the maximum of SMF73BSY*100/SMF73SMP. Please note this value may exceed 100% due to data from micro code.

PART_CHAN_BUSY_MAX
Maximum percentage of time that the channel path was busy for the partition. Calculated as the maximum of SMF73PBY*100/SMF73PTI. Please note this value may exceed 100% due to data from micro code.

TIME_RESOLUTION
Time resolution used for this table, in minutes. The default is 60 minutes. From TIME_RESOLUTION in the MVSPM_TIME_RES lookup table.

MEASURED_SEC
Measured time period, in seconds. This is the duration of the RMF measurement intervals. Calculated as the sum of SMF73INT/1000.

SAMPLES
Number of store channel path status (STCP) samples taken by SRM. This is the sum of SMF73SMP.

SAMPLES_BUSY
Number of store channel path status (STCP) samples taken by SRM in which the channel path related to this entry was found busy. This is the sum of SMF73BSY.

CPMF_BUSY_SEC
Logical partitions channel path busy time as measured by the channel path measurement facility (CPMF), in seconds. Calculated as the sum of SMF73PBY*0.001024.

CPMF_INTERVAL_SEC
Sum of the interval times as measured by the channel path measurement facility (CPMF), in seconds. Calculated as the sum of SMF73PTI*0.001024.

CPMF_GROUP
CPMF channel measurement group. From SMF73CMG

CHANNEL_BUSY_TOT
Total channel path busy time in seconds when the measurement group number is 1. Calculated as the sum of SMF73TUT/7.8125.

PART_CHAN_BUSY_TOT
LPAR channel path busy time in seconds when the measurement group number is 1. Calculated as the sum of SMF73PUT/7.8125.

BUSCYCLE_MAX_SEC
Maximum bus cycles per second when the measurement group number is 2. Calculated as the maximum value of SMF73MBC.

CHAN_UNIT_MAX_SEC
Maximum channel work units per second when the measurement group number is 2. Calculated as the maximum value of SMF73MCU.

WRITE_MB_MAX_SEC
Maximum write data units per second when the measurement group number is 2. Calculated as the maximum value of SMF73MWU.

READ_MB_MAX_SEC
Maximum read data units per second when the measurement group number is 2. Calculated as the maximum value of SMF73MRU.

BUSCYCLE
Percentage of bus cycles, the bus has been found busy for this channel in relation to the theoretical limit. Calculated as the average of (SMF73TBC*100.)/ (SMF73MBC*SMF73PTI*1024E-6). This value may exceed 100% due to data from micro code.

CHAN_UNIT_TOT
Total channel work unit count when the measurement group Number is 2. Calculated as the sum of SMF73TUC.

LPAR_CHAN_UNIT_TOT
LPAR channel work unit count when the measurement group number is 2. Calculated as the sum of SMF73PUC.

CHAN_WRITE_MB_TOT
Channel write in MB when the measurement group number is 2. Calculated as the sum of SMF73TWU*(SMF73US/1024E3).

LPAR_WRITE_MB_TOT
LPAR write in MB when the measurement group number is 2. Calculated as the sum of SMF73PWU*(SMF73US/1024E6).

CHAN_READ_MB_TOT
Channel read in MB when the measurement group number is 2. Calculated as the sum of SMF73TRU*(SMF73US/1024E3).

LPAR_READ_MB_TOT
LPAR read in MB when the measurement group number is 2. Calculated as the sum of SMF73PRU*(SMF73US/1024E3).

SHARED_IND
Channel Path is shared between LPAR. This is Y (yes) if bit 8 of SMF73FG3 is on; otherwise, N (no).

MULTI_SUBSYS_IND
Channel Path is shared between LPAR. This is Y (yes) if bit 8 of SMF73FG3 is on; otherwise, N (no).

CHANNEL_SUBSYS_ID
Channel Subsystem ID. Valid only for hardware that supports Multiple Channel Subsystem. From SMF73CSS.

DATA_TYPE
Rank Flag id. A flag which distinguishes the Top N CHANNEL BUSY MAX record in this table.

LPAR_NAME
Name of the logical partition.

CPU_SERIAL_NO
Last 4 digits of the CPU serial number.

LPAR_SYSTEM_ID
System ID of the logical partition.

Parent topic: Presentation View

Customer Pricing Information tableFramework Manager tablesCustomer Pricing InformationThe Customer Pricing Information table contains the following columns.STARTDATEStart date of the mid measurement. The default is 1900-01-01.ENDDATEEnd date of the mid measurement. The default is 2900-01-01ENTERPRISELogically named enterpise. Enterprise is defined as a grouping of mainframe systems that will all have the same currency.CPU_SERIAL_NOLast 4 digits of the CPU serial number. From SMF70SER.CURRENCYThe three character international symbol for the currency.LPAR_NAMEName of the logical partition. From SMF70LPM.MVS_SYSTEM_IDMVS system ID.PIDProduct ID number.CATEGORYSoftware Licensing category at IBM: MLC or IPLA.LICENSEMonthly License Charge metrics name of MLC products or Value Unit Exhibit name of IPLA products.TIERMonthly License Charge metrics tier of MLC products or Value Unit Exhibit tier of IPLA productsTIER_MIN_MSUMinimum MSU of this tier.TIER_MAX_MSUMaximum MSU of this tier.PRICE_PER_MSUPrice per MSU of MLC products. Decimal value that goes up to two decimal places.C_TIER_PRICEInterim price calculation result for MLC products for reporting usage.VU_PER_MSUVU per MSU.C_TIER_VUInterim VU calculation result for IPLA products for reporting usage.PRICE_PER_VUPrice per VU. Decimal value that goes up to two decimal places.VU_ENTITLED_MSUEntitled MSUs for this IPLA product on this CPU Serial No. It is not the entire enterprise entitlement. Adding up each entitlement totals the enterprise entitlement. SS_PIDSubscription and Support product ID. This value may be left blank if Subscription and Support was not purchased.SS_VU_ENTITLED_MSUEntitled MSUs for this IPLA product on this CPU Serial No. It is not the entire enterprise entitlement. This value may be left blank if Subscription and Support was not purchased.SS_PRICE_PER_MTHSubscription and Support product price per month. Decimal value that goes up to two decimal places.Presentation ViewThe Presentation View includes shortcuts to the existing items in the Logical View.

Customer Pricing Information table

The Customer Pricing Information table contains the following columns.

STARTDATE

Start date of the mid measurement. The default is 1900-01-01.

ENDDATE

End date of the mid measurement. The default is 2900-01-01

ENTERPRISE

Logically named enterpise. Enterprise is defined as a grouping of mainframe systems that will all have the same currency.

CPU_SERIAL_NO

Last 4 digits of the CPU serial number. From SMF70SER.

CURRENCY

The three character international symbol for the currency.

LPAR_NAME

Name of the logical partition. From SMF70LPM.

MVS_SYSTEM_ID

MVS™ system ID.

PID

Product ID number.

CATEGORY

Software Licensing category at IBM®: MLC or IPLA.

LICENSE

Monthly License Charge metrics name of MLC products or Value Unit Exhibit name of IPLA products.

TIER

Monthly License Charge metrics tier of MLC products or Value Unit Exhibit tier of IPLA products

TIER_MIN_MSU

Minimum MSU of this tier.

TIER_MAX_MSU

Maximum MSU of this tier.

PRICE_PER_MSU

Price per MSU of MLC products. Decimal value that goes up to two decimal places.

C_TIER_PRICE

Interim price calculation result for MLC products for reporting usage.

VU_PER_MSU

VU per MSU.

C_TIER_VU

Interim VU calculation result for IPLA products for reporting usage.

PRICE_PER_VU

Price per VU. Decimal value that goes up to two decimal places.

VU_ENTITLED_MSU

Entitled MSUs for this IPLA product on this CPU Serial No. It is not the entire enterprise entitlement. Adding up each entitlement totals the enterprise entitlement.

SS_PID

Subscription and Support product ID. This value may be left blank if Subscription and Support was not purchased.

SS_VU_ENTITLED_MSU

Entitled MSUs for this IPLA product on this CPU Serial No. It is not the entire enterprise entitlement. This value may be left blank if Subscription and Support was not purchased.

SS_PRICE_PER_MTH

Subscription and Support product price per month. Decimal value that goes up to two decimal places.

Parent topic: Presentation View

Device Activity Metrics tableThis table provides statistics on the I/O activity of MVS devices. The data comes from SMF type 74, subtype 1 records. This table is updated by Tivoli Decision Support for z/OS MVSPM_DEVICE_H table.Device Activity Metrics tableColumnDescriptionDATEDate of the mid measurement. From SMF74DAT, SMF74IST, and SMF74INT.TIMETime of the mid measurement. From SMF74DAT, SMF74IST, and SMF74INT.DATA_TYPERank Flag id. A flag which distinguishes DASD top record and DASD aggregate record in this table. If the flag equals 0, it represents DASD top record. If the flag equals -1, it represents DASD aggregate record.LPAR_NAMEName of the logical partition.CPU_SERIAL_NOLast 4 digits of the CPU serial number. PERIOD_NAMEName of the period. This is derived using fields SMF74DAT, SMF74IST, and SMF74INT from the record as parameters in the PERIOD function.MVS_SYSTEM_IDMVS system ID. From SMF74SID.LPAR_SYSTEM_IDSystem ID of the logical partition.IO_RATEIO Rate, MEASUREMENT_EVENTS/RMF_INTERVAL.IO_INTENSITYIO Intensity, IO_RATE/Response Time. Response time is sum of average Connection, disconnection, pending and IOSQ.QI_INTENSITYQueuing Intensity, IO_RATE/Queue Delay Time. Queue Delay Time is sum of average disconnection, pending and IOSQ.DEVICE_NUMBERDevice number. From MVS_ADDR in the MVSPM_DEVICE_ADDR lookup table, or from SMF74NUM if no match is found in the lookup table.CACHE_SUBSYSTEM_ID3990 control unit subsystem ID. From CACHE_SUBSYSTEM_ID in the MVSPM_DEVICE_ADDR lookup table using SMF74SID and SMF74LCU as lookup argument.DEVICE_CLASSDevice class code. This is derived using field SMF74TYP from the record as key.UNIT_TYPEDevice model name or unit type. From SMF74DEV or from UNIT_TYPE in the MVSPM_UNIT_TYPE lookup table if SMF74DEV is missing.LCU_NUMBERLogical control unit number. From SMF74LCU.VOLSERVolume serial of the volume mounted on the device (tape or direct access device only). From VOLSER in the MVSPM_DEVICE_ADDR lookup table, or from SMF74SER if VOLSER is missing or no match is found in the lookup table.STORAGE_GROUPStorage group name. From SMF74SGN.CU_NAMEControl unit name. This column is blank if control unit name cannot be determined. From SMF74CU.RECORD_LEVELSMF record level change number. This column enables processing of SMF record level changes in an existing release. From SMF74SRL.TIME_RESOLUTIONTime resolution used for this table, in minutes. The default is 60 minutes.RMF_INTERVALDuration of the RMF measurement interval, in minutes. Calculated as SUM(SMF74INT/60000).CYCLE_LENGTH_MSECRMF sampling cycle length, in milliseconds. From SMF74CYC.INPUT_RECORDSNumber of input records collected from the input log.SAMPLESNumber of RMF samples taken. This is calculated as the sum of SMF74SAM.SSCH_COUNTStart subchannel count. This is the number of SSCH instructions. This is the sum of SMF74SSC.MEASUREMENT_EVENTSMeasurement event count. This is the number of SSCH instructions for which connect, pending, and active times were stored. This is the sum of SMF74MEC.REQUESTS_QUEUEDNumber of requests queued in the IOS for the device. This is the sum of SMF74QUE.SAMPLES_UTLNumber of samples taken when the device was reserved but an SSCH instruction had not been issued to the device. This is the sum of SMF74UTL.SAMPLES_RSVNumber of samples taken when the device was reserved. This is the sum of SMF74RSV.ALLOCATIONSFor RMF 3.5.1 releases and later, this is the total number of allocations in effect for the device. For RMF releases earlier than 3.5.1, this is the total number of data sets open on the device. This is the sum of SMF74NDA and SMF74DSO.CONNECT_MSECDevice connect time, in milliseconds. This is the sum of SMF74CNN*0.128.PENDING_MSECDevice pending time, in milliseconds. This is the sum of SMF74PEN*0.128.ACTIVE_MSECDevice active time, in milliseconds. This is sum of SMF74ATV*0.128.DISCONN_MSECDevice disconnect time, in milliseconds. This is the sum of SMF74DIS*0.128.DEVICE_BUSY_DELAYDevice busy delay time, in milliseconds. This is from subchannel information block (SCHIB). This is the sum of SMF74DVB*0.128.CU_BUSY_DELAYControl unit busy delay time, in milliseconds. This is the sum of SMF74CUB*0.128. If you installed the RMF APAR OW54347, this field is no longer used.DPB_MSECAmount of time, in milliseconds, during the measurement interval that I/O requests to a device were delayed because a director port was busy. This is the sum of SMF74DPB*0.128. If you installed the RMF APAR OW54347, this field is no longer used.SAMPLES_ALLOCATEDNumber of samples that indicated that the device was allocated. This is the sum of SMF74ALC.SAMPLES_MOUNT_PENDNumber of samples that indicated a mount pending condition. This is the sum of SMF74MTP.SAMPLES_NOT_READYNumber of samples that indicated that the device was not ready. This is the sum of SMF74NRT.MOUNTS_DETECTEDNumber of mounts detected. This is the sum of SMF74MTC.ALLOCATED_TAPE_MAXMaximum number of mounts detected. This is the maximum of SMF74MTC.FLAGSFlags for the device class; 80 = number option active, 40 = SG option active, 20 = SG name changed during the interval, 10 = Open mount at interval start, 08 = Open mount at interval end. From SMF74CLF.SYSPLEX_NAMEThis is the name of the sysplex. From SMF74XNM.SYSTEM_NAMEMVS system name. From SMF74SNM.PAV_SUCC_SAMPLESNumber of unsuccessful PAV sample counts. From SMF74PCT.NUM_UCB_PAVAverage number of HyperPAV devices. Calculated as the sum of (SMF74NUX/SMF74PSM).INIT_CMD_RSP_MSECInitial command response time, in milliseconds. For a start or resume function of the subchannel, it is the time required until the device indicates that has accepted the command. Calculated as SUM of SMF74CMR*0.128.DEV_HPAV_ALIASNumber of configured HyperPAV aliases for the LSS of the device. Calculated as the sum of SMF74HPC.NUMBER_OF_CYLDASD volume capacity (specified by the number of available cylinders)IOSQ_MESCIOS Queue time, in milliseconds, REQUESTS_QUEUED * CYCLE_LENGTH_MSEC.Presentation ViewThe Presentation View includes shortcuts to the existing items in the Logical View.

Device Activity Metrics table

This table provides statistics on the I/O activity of MVS devices. The data comes from SMF type 74, subtype 1 records. This table is updated by Tivoli® Decision Support for z/OS® MVSPM_DEVICE_H table.

Table 22. . Device Activity Metrics table

Column | Description
DATE | Date of the mid measurement. From SMF74DAT, SMF74IST, and SMF74INT.
TIME | Time of the mid measurement. From SMF74DAT, SMF74IST, and SMF74INT.
DATA_TYPE | Rank Flag id. A flag which distinguishes DASD top record and DASD aggregate record in this table. If the flag equals 0, it represents DASD top record. If the flag equals -1, it represents DASD aggregate record.
LPAR_NAME | Name of the logical partition.
CPU_SERIAL_NO | Last 4 digits of the CPU serial number.
PERIOD_NAME | Name of the period. This is derived using fields SMF74DAT, SMF74IST, and SMF74INT from the record as parameters in the PERIOD function.
MVS_SYSTEM_ID | MVS system ID. From SMF74SID.
LPAR_SYSTEM_ID | System ID of the logical partition.
IO_RATE | IO Rate, MEASUREMENT_EVENTS/RMF_INTERVAL.
IO_INTENSITY | IO Intensity, IO_RATE/Response Time. Response time is sum of average Connection, disconnection, pending and IOSQ.
QI_INTENSITY | Queuing Intensity, IO_RATE/Queue Delay Time. Queue Delay Time is sum of average disconnection, pending and IOSQ.
DEVICE_NUMBER | Device number. From MVS_ADDR in the MVSPM_DEVICE_ADDR lookup table, or from SMF74NUM if no match is found in the lookup table.
CACHE_SUBSYSTEM_ID | 3990 control unit subsystem ID. From CACHE_SUBSYSTEM_ID in the MVSPM_DEVICE_ADDR lookup table using SMF74SID and SMF74LCU as lookup argument.
DEVICE_CLASS | Device class code. This is derived using field SMF74TYP from the record as key.
UNIT_TYPE | Device model name or unit type. From SMF74DEV or from UNIT_TYPE in the MVSPM_UNIT_TYPE lookup table if SMF74DEV is missing.
LCU_NUMBER | Logical control unit number. From SMF74LCU.
VOLSER | Volume serial of the volume mounted on the device (tape or direct access device only). From VOLSER in the MVSPM_DEVICE_ADDR lookup table, or from SMF74SER if VOLSER is missing or no match is found in the lookup table.
STORAGE_GROUP | Storage group name. From SMF74SGN.
CU_NAME | Control unit name. This column is blank if control unit name cannot be determined. From SMF74CU.
RECORD_LEVEL | SMF record level change number. This column enables processing of SMF record level changes in an existing release. From SMF74SRL.
TIME_RESOLUTION | Time resolution used for this table, in minutes. The default is 60 minutes.
RMF_INTERVAL | Duration of the RMF measurement interval, in minutes. Calculated as SUM(SMF74INT/60000).
CYCLE_LENGTH_MSEC | RMF sampling cycle length, in milliseconds. From SMF74CYC.
INPUT_RECORDS | Number of input records collected from the input log.
SAMPLES | Number of RMF samples taken. This is calculated as the sum of SMF74SAM.
SSCH_COUNT | Start subchannel count. This is the number of SSCH instructions. This is the sum of SMF74SSC.
MEASUREMENT_EVENTS | Measurement event count. This is the number of SSCH instructions for which connect, pending, and active times were stored. This is the sum of SMF74MEC.
REQUESTS_QUEUED | Number of requests queued in the IOS for the device. This is the sum of SMF74QUE.
SAMPLES_UTL | Number of samples taken when the device was reserved but an SSCH instruction had not been issued to the device. This is the sum of SMF74UTL.
SAMPLES_RSV | Number of samples taken when the device was reserved. This is the sum of SMF74RSV.
ALLOCATIONS | For RMF 3.5.1 releases and later, this is the total number of allocations in effect for the device. For RMF releases earlier than 3.5.1, this is the total number of data sets open on the device. This is the sum of SMF74NDA and SMF74DSO.
CONNECT_MSEC | Device connect time, in milliseconds. This is the sum of SMF74CNN*0.128.
PENDING_MSEC | Device pending time, in milliseconds. This is the sum of SMF74PEN*0.128.
ACTIVE_MSEC | Device active time, in milliseconds. This is sum of SMF74ATV*0.128.
DISCONN_MSEC | Device disconnect time, in milliseconds. This is the sum of SMF74DIS*0.128.
DEVICE_BUSY_DELAY | Device busy delay time, in milliseconds. This is from subchannel information block (SCHIB). This is the sum of SMF74DVB*0.128.
CU_BUSY_DELAY | Control unit busy delay time, in milliseconds. This is the sum of SMF74CUB*0.128. If you installed the RMF APAR OW54347, this field is no longer used.
DPB_MSEC | Amount of time, in milliseconds, during the measurement interval that I/O requests to a device were delayed because a director port was busy. This is the sum of SMF74DPB*0.128. If you installed the RMF APAR OW54347, this field is no longer used.
SAMPLES_ALLOCATED | Number of samples that indicated that the device was allocated. This is the sum of SMF74ALC.
SAMPLES_MOUNT_PEND | Number of samples that indicated a mount pending condition. This is the sum of SMF74MTP.
SAMPLES_NOT_READY | Number of samples that indicated that the device was not ready. This is the sum of SMF74NRT.
MOUNTS_DETECTED | Number of mounts detected. This is the sum of SMF74MTC.
ALLOCATED_TAPE_MAX | Maximum number of mounts detected. This is the maximum of SMF74MTC.
FLAGS | Flags for the device class; 80 = number option active, 40 = SG option active, 20 = SG name changed during the interval, 10 = Open mount at interval start, 08 = Open mount at interval end. From SMF74CLF.
SYSPLEX_NAME | This is the name of the sysplex. From SMF74XNM.
SYSTEM_NAME | MVS system name. From SMF74SNM.
PAV_SUCC_SAMPLES | Number of unsuccessful PAV sample counts. From SMF74PCT.
NUM_UCB_PAV | Average number of HyperPAV devices. Calculated as the sum of (SMF74NUX/SMF74PSM).
INIT_CMD_RSP_MSEC | Initial command response time, in milliseconds. For a start or resume function of the subchannel, it is the time required until the device indicates that has accepted the command. Calculated as SUM of SMF74CMR*0.128.
DEV_HPAV_ALIAS | Number of configured HyperPAV aliases for the LSS of the device. Calculated as the sum of SMF74HPC.
NUMBER_OF_CYL | DASD volume capacity (specified by the number of available cylinders)
IOSQ_MESC | IOS Queue time, in milliseconds, REQUESTS_QUEUED * CYCLE_LENGTH_MSEC.

Parent topic: Presentation View

LPAR Level Forecast Metrics tableThis table provides forecast results. All the forecast results come from three forecast streams: hourly, daily and monthly.The LPAR Level Forecast Metrics tableColumnDescriptionCPU_BUSY_MIPS_SUMSum of CPU_BUSY_MIPS in MVSPM_LPAR table by different time intervals (hourly, daily, monthly).CPU_BUSY_MIPS_SUM_PREDPredicted value of CPU_BUSY_MIPS_SUM based on different time intervals (hourly, daily, monthly).CPU_MODEL_NOCPU model number. From SMF70MOD.CPU_SERIAL_NOLast 4 digits of the CPU serial number. From SMF70SER.DATEDate of the mid measurement.FORECAST_TIME_LEVELAggregated time interval for the record (hourly, daily, monthly).LPAR_NAMEName of the logical partition. From SMF70LPM.MEASURED_SEC_DERIVEDFixed MEASURED_SEC_SUM. For hourly it is 3,600, for daily is 86,400, and for monthly is 2,592,000.MEASURED_SEC_SUMSum of MEASURED_SEC in MVSPM_LPAR table by different time intervals (hourly, daily, monthly).MIPS_TOT_CAPACITYTotal MIPS capacity for specified process type and LPAR.MVS_SYSTEM_IDMVS system ID. This is the SMF system ID. From SMF70SID.PROCESSOR_TYPEName of the logical processor type. Possible values are:NameDescriptionCPGeneral purpose processor ICF+ICF pool (includes IFA, IFL and ICF) IFAIntegrated Facility for Application processorIFLIntegrated Facility for Linux processor ICF Internal Coupling Facility processor IIP Integrated Information Processor TIMETime (rounded down as specified in the MVSPM_TIME_RES lookup table) of the mid measurement.GUARANTEED_MIPSPredicted guaranteed MIPS value of processors based on different time intervals (hourly, daily, monthly).LOGIC_PROC_MIPSLogical processor busy MIPS time.GMT_OFFSETOffset Greenwich Mean Time (GMT) to local time, in seconds, and calculated as SMF70LGO/4096E6.ADJUSTED_VALUEAdjusted flag for confidence interval.CPU_BUSY_MIPS_SUM_PRED_LPredicted lower bound value of CPU_BUSY_MIPS_SUM based on different time intervals (hourly, daily, monthly).CPU_BUSY_MIPS_SUM_PRED_UPredicted upper bound value of CPU_BUSY_MIPS_SUM based on different time intervals (hourly, daily, monthly).Presentation ViewThe Presentation View includes shortcuts to the existing items in the Logical View.

LPAR Level Forecast Metrics table

This table provides forecast results. All the forecast results come from three forecast streams: hourly, daily and monthly.

Table 23. The LPAR Level Forecast Metrics table.

Column
Description

CPU_BUSY_MIPS_SUM
Sum of CPU_BUSY_MIPS in MVSPM_LPAR table by different time intervals (hourly, daily, monthly).

CPU_BUSY_MIPS_SUM_PRED
Predicted value of CPU_BUSY_MIPS_SUM based on different time intervals (hourly, daily, monthly).

CPU_MODEL_NO
CPU model number. From SMF70MOD.

CPU_SERIAL_NO
Last 4 digits of the CPU serial number. From SMF70SER.

DATE
Date of the mid measurement.

FORECAST_TIME_LEVEL
Aggregated time interval for the record (hourly, daily, monthly).

LPAR_NAME
Name of the logical partition. From SMF70LPM.

MEASURED_SEC_DERIVED
Fixed MEASURED_SEC_SUM. For hourly it is 3,600, for daily is 86,400, and for monthly is 2,592,000.

MEASURED_SEC_SUM
Sum of MEASURED_SEC in MVSPM_LPAR table by different time intervals (hourly, daily, monthly).

MIPS_TOT_CAPACITY
Total MIPS capacity for specified process type and LPAR.

MVS_SYSTEM_ID
MVS system ID. This is the SMF system ID. From SMF70SID.

PROCESSOR_TYPE
Name of the logical processor type. Possible values are:

Name

Description

CP

General purpose processor

ICF+

ICF pool (includes IFA, IFL and ICF)

IFA

Integrated Facility for Application processor

IFL

Integrated Facility for Linux processor

ICF

Internal Coupling Facility processor

IIP

Integrated Information Processor

TIME
Time (rounded down as specified in the MVSPM_TIME_RES lookup table) of the mid measurement.

GUARANTEED_MIPS
Predicted guaranteed MIPS value of processors based on different time intervals (hourly, daily, monthly).

LOGIC_PROC_MIPS
Logical processor busy MIPS time.

GMT_OFFSET
Offset Greenwich Mean Time (GMT) to local time, in seconds, and calculated as SMF70LGO/4096E6.

ADJUSTED_VALUE
Adjusted flag for confidence interval.

CPU_BUSY_MIPS_SUM_PRED_L
Predicted lower bound value of CPU_BUSY_MIPS_SUM based on different time intervals (hourly, daily, monthly).

CPU_BUSY_MIPS_SUM_PRED_U
Predicted upper bound value of CPU_BUSY_MIPS_SUM based on different time intervals (hourly, daily, monthly).

Parent topic: Presentation View

LPAR Level Metrics tableThis table is a consolidated version of DRL.MVSPM_LPAR_H, which eliminates any duplicate entries from DRL.MVSPM_LPAR_H.LPAR Level Metrics tableColumnDescriptionCAPACITY_GRP_LMTGroup maximum licensing units which may be consumed per unit of time, on average. From SMF70GMU.CAPACITY_GRP_NMName of the capacity group this partition belongs to. From SMF70GNM.CAPACITY_LIMIT_MSUDefined capacity limit of the partition in MSUs per hour. A value of 0 indicates that the partition is not under control of the License Manager. Valid only for CP processors. From SMF70MSU.CENTRAL_STOR_FRAMECentral storage frames assigned to this partition. Calculated as MAX of SMF70CSF.COMPLEX_SECTotal processor complex time, in seconds. Each processor type is considered separately.COUNT_SHRNumber of occurrences of LPROC shared within the reported interval. Each processor type is considered separately.CPC_MODEL_IDCPC model identification. From the SMF70MDL.CPU_BUSY_MIPSVirtual processor busy MIPS time, in seconds. Calculated as the value of (SMF70PDT/1000000.) * (MIPS/TOT_PROC). MIPS is a column in lookup table MVS_MIPS_T. TOT_PROC is the physical processor total.CPU_DISPATCH_SECLogical processor dispatch time, in seconds. CPU_EFF_DISP_SECLogical processor effective dispatch time (excluding LPAR management time), in seconds. CPU_MODEL_NOCPU model number. From SMF70MOD.CPU_SERIAL_NOLast 4 digits of the CPU serial number. From SMF70SER.CPU_WAIT_SECProcessor wait time, in seconds. This is set to zero if the processor was run in LPAR mode and wait completion was not enabled. Calculated as the sum of SMF70WAT/4 096 000 000.DATEDate of the mid measurement.DEDICAT_PROC_LPARTotal number of dedicated processors for this LPAR. From LPDEDICF + LPDEDCP + LP_SPP_IFA_DED + LP_SPP_IFL_DED + LP_SPP_ICF_DED.DEDICAT_PROCESSORSNumber of dedicated processors for this LPAR. Each processor type is considered separately.DEDICATED_PROC_INDIndicates whether the partition has been assigned one or more dedicated processors: 1 = yes, 0 = no. From SMF70BPS.DISPATCH_INTERVALDispatch interval time, in seconds. A zero value indicates that the interval was dynamically determined. Calculated as the value of SMF70GTS/1000.EXPAND_STOR_FRAMEExpanded storage frames assigned to this partition. Calculated as maximum of SMF70CSF.FROM_LPAR_NONumber of the logical partition that wrote the record. From SMF70PTN.LOGICAL_PROC_TOTTotal number of logical processor assigned to the partition for all occurrences of LPROC shared. Each processor type is considered separately.LOGICAL_PROCESSORSNumber of logical processors assigned to the partition. From SMF70BDN.LP_ONLINE_SECTotal logical processor online seconds for this LPAR. From SMF70INT/1000. For z/OS system is SMF70ONT/1000000.LPAR_FLAGSLPAR flags. 80 = partition deactivated during interval, 40 = number of logical processors changed. From SMF70PFG.LPAR_NAMEName of the logical partition. From SMF70LPM.LPAR_NONumber of the logical partition. From SMF70LPN.LPAR_SYSTEM_IDSystem ID of the logical partition. LPAR_WEIGHTLogical partition processor resource weight factor. This column is set to zero if the weight factor is X'FFFF'. From SMF70BPS.LPAR_WEIGHT_TOTSum of all partition processor resource weight factors. Each processor type is considered separately.LPROC_SECAvailable logical processor time, in seconds. This is the time available for all nondedicated processors in the complex. Each processor type is considered separately.MEASURED_SECMeasured time period, in seconds. This is the duration of the RMF measurement intervals. From SMF70INT/1000.MIPS_PER_LOGTotal number of MIPS per logical processor type. The number of MIPS is taken from a specific Look Up table, MVS_MIPS_T.MIPS_PER_LOG_CPNumber of MIPS per single logical CPU.MIPS_TOT_CAPACITYTotal MIPS capacity for specified process type and LPAR.MVS_LEVELMVS software level. This is the acronym, version number, release number, and modification level. From SMF70MVS.MVS_SYSTEM_IDMVS system IDNONDED_PROCESSORSNumber of processors that were not assigned as dedicated in the processor complex. Each processor type is considered separately.NUM_CONSUMED_MSUActual number of consumed MSUs. Valid only for CP processors. Calculated as Sum(SMF70PDT)*3600*16/(SMF70CPA*SMF70INT*1000).PERIOD_NAMEName of the period.PHY_PROC_CNTThe number of physical processors currently recorded. PHY_PROC_SPP_ICFTotal number of ICF physical processors assigned for use by PR/SM. From TOT_SPP_ICF_PRO.PHY_PROC_SPP_IFATotal number of IFA physical processors assigned for use by PR/SM. From TOT_SPP_IFA_PRO.PHY_PROC_SPP_IFLTotal number of IFL physical processors assigned for use by PR/SM. From TOT_SPP_IFL_PRO.PHY_PROC_SPP_IIPTotal number of IIP physical processors assigned for use by PR/SM. From TOT_SPP_IIP_PRO.PHY_PROCESSORS_CPTotal number of CP physical processors assigned for use by PR/SM. From TOTCPPRO.PHYS_PROC_LPARTotal number of physical CPUs utilized in LPAR.PHYS_PROCESSORSTotal number of physical processors assigned for use by PR/SM. From TOTCPPRO+TOTICFPRO+TOT_SPP_IFA_PRO+ TOT_SPP_IFL_PRO+ TOT_SPP_ICF_PRO.PROCESSOR_FLAGSLogical processor flags. 80 = wait state assist enabled; 40 = wait state assist changed; 20 = relative share changed; 10 = partition capping is enabled; and 08 = partition capping status changed. From SMF70VPF.PROCESSOR_TYPEName of the logical processor type. Possible values are:NameDescriptionCPGeneral purpose processor ICF+ICF pool (includes IFA, IFL and ICF) IFAIntegrated Facility for Application processorIFLIntegrated Facility for Linux processor ICF Internal Coupling Facility processor IIP Integrated Information Processor PRSM_FLAGSPR/SM flags. 80 = diagnose X'204' failure; 40 = number of physical processors changed; 20 = global time slice value changed; and 10 = physical partition included. From SMF70INB.RECORD_LEVELSMF record level change number. This column enables processing of SMF record level changes in an existing release. From SMF70SRL.RMF_VERSIONRMF version number. From SMF72MFV.SYSPLEX_NAMEThe sysplex name to be associated with the MVS system ID or the MVS system name.SYSTEM_NAMEMVS system nameTIMETime (rounded down as specified in the MVSPM_TIME_RES lookup table) of the mid measurement.TIME_RESOLUTIONTime resolution for the set of tables, in minutes. This defines the time period for which data is to be recorded. Valid values are 1, 2, 3, 5, 6, 10, 12, 15, 20, and 30 or any multiple of 60.WEIGHT_ACTUALActual weighting of the shared processor resources. Valid only for z/OS system with WLM active. Calculated as the average of SMF70ACS / SMF70DSA.WLM_CAPPING_PCTPercentage of WLM capping of the partition. Valid only for CP processors. Calculated as the maximum of (SMF70NSW/SMF70DSA) * 100.GUARANTEED_MIPSGuaranteed busy MIPS time.LOGIC_PROC_MIPSLogical processor busy MIPS time.GMT_OFFSETOffset Greenwich Mean Time (GMT) to local time, in seconds, and calculated as SMF70LGO/4096E6.Presentation ViewThe Presentation View includes shortcuts to the existing items in the Logical View.

LPAR Level Metrics table

This table is a consolidated version of DRL.MVSPM_LPAR_H, which eliminates any duplicate entries from DRL.MVSPM_LPAR_H.

Table 24. LPAR Level Metrics table

Column
Description

CAPACITY_GRP_LMT
Group maximum licensing units which may be consumed per unit of time, on average. From SMF70GMU.

CAPACITY_GRP_NM
Name of the capacity group this partition belongs to. From SMF70GNM.

CAPACITY_LIMIT_MSU
Defined capacity limit of the partition in MSUs per hour. A value of 0 indicates that the partition is not under control of the License Manager. Valid only for CP processors. From SMF70MSU.

CENTRAL_STOR_FRAME
Central storage frames assigned to this partition. Calculated as MAX of SMF70CSF.

COMPLEX_SEC
Total processor complex time, in seconds. Each processor type is considered separately.

COUNT_SHR
Number of occurrences of LPROC shared within the reported interval. Each processor type is considered separately.

CPC_MODEL_ID
CPC model identification. From the SMF70MDL.

CPU_BUSY_MIPS
Virtual processor busy MIPS time, in seconds. Calculated as the value of (SMF70PDT/1000000.) * (MIPS/TOT_PROC). MIPS is a column in lookup table MVS_MIPS_T. TOT_PROC is the physical processor total.

CPU_DISPATCH_SEC
Logical processor dispatch time, in seconds.

CPU_EFF_DISP_SEC
Logical processor effective dispatch time (excluding LPAR management time), in seconds.

CPU_MODEL_NO
CPU model number. From SMF70MOD.

CPU_SERIAL_NO
Last 4 digits of the CPU serial number. From SMF70SER.

CPU_WAIT_SEC
Processor wait time, in seconds. This is set to zero if the processor was run in LPAR mode and wait completion was not enabled. Calculated as the sum of SMF70WAT/4 096 000 000.

DATE
Date of the mid measurement.

DEDICAT_PROC_LPAR
Total number of dedicated processors for this LPAR. From LPDEDICF + LPDEDCP + LP_SPP_IFA_DED + LP_SPP_IFL_DED + LP_SPP_ICF_DED.

DEDICAT_PROCESSORS
Number of dedicated processors for this LPAR. Each processor type is considered separately.

DEDICATED_PROC_IND
Indicates whether the partition has been assigned one or more dedicated processors: 1 = yes, 0 = no. From SMF70BPS.

DISPATCH_INTERVAL
Dispatch interval time, in seconds. A zero value indicates that the interval was dynamically determined. Calculated as the value of SMF70GTS/1000.

EXPAND_STOR_FRAME
Expanded storage frames assigned to this partition. Calculated as maximum of SMF70CSF.

FROM_LPAR_NO
Number of the logical partition that wrote the record. From SMF70PTN.

LOGICAL_PROC_TOT
Total number of logical processor assigned to the partition for all occurrences of LPROC shared. Each processor type is considered separately.

LOGICAL_PROCESSORS
Number of logical processors assigned to the partition. From SMF70BDN.

LP_ONLINE_SEC
Total logical processor online seconds for this LPAR. From SMF70INT/1000. For z/OS system is SMF70ONT/1000000.

LPAR_FLAGS
LPAR flags. 80 = partition deactivated during interval, 40 = number of logical processors changed. From SMF70PFG.

LPAR_NAME
Name of the logical partition. From SMF70LPM.

LPAR_NO
Number of the logical partition. From SMF70LPN.

LPAR_SYSTEM_ID
System ID of the logical partition.

LPAR_WEIGHT
Logical partition processor resource weight factor. This column is set to zero if the weight factor is X'FFFF'. From SMF70BPS.

LPAR_WEIGHT_TOT
Sum of all partition processor resource weight factors. Each processor type is considered separately.

LPROC_SEC
Available logical processor time, in seconds. This is the time available for all nondedicated processors in the complex. Each processor type is considered separately.

MEASURED_SEC
Measured time period, in seconds. This is the duration of the RMF measurement intervals. From SMF70INT/1000.

MIPS_PER_LOG
Total number of MIPS per logical processor type. The number of MIPS is taken from a specific Look Up table, MVS_MIPS_T.

MIPS_PER_LOG_CP
Number of MIPS per single logical CPU.

MIPS_TOT_CAPACITY
Total MIPS capacity for specified process type and LPAR.

MVS_LEVEL
MVS software level. This is the acronym, version number, release number, and modification level. From SMF70MVS.

MVS_SYSTEM_ID
MVS system ID

NONDED_PROCESSORS
Number of processors that were not assigned as dedicated in the processor complex. Each processor type is considered separately.

NUM_CONSUMED_MSU
Actual number of consumed MSUs. Valid only for CP processors. Calculated as Sum(SMF70PDT)*3600*16/(SMF70CPA*SMF70INT*1000).

PERIOD_NAME
Name of the period.

PHY_PROC_CNT
The number of physical processors currently recorded.

PHY_PROC_SPP_ICF
Total number of ICF physical processors assigned for use by PR/SM. From TOT_SPP_ICF_PRO.

PHY_PROC_SPP_IFA
Total number of IFA physical processors assigned for use by PR/SM. From TOT_SPP_IFA_PRO.

PHY_PROC_SPP_IFL
Total number of IFL physical processors assigned for use by PR/SM. From TOT_SPP_IFL_PRO.

PHY_PROC_SPP_IIP
Total number of IIP physical processors assigned for use by PR/SM. From TOT_SPP_IIP_PRO.

PHY_PROCESSORS_CP
Total number of CP physical processors assigned for use by PR/SM. From TOTCPPRO.

PHYS_PROC_LPAR
Total number of physical CPUs utilized in LPAR.

PHYS_PROCESSORS
Total number of physical processors assigned for use by PR/SM. From TOTCPPRO+TOTICFPRO+TOT_SPP_IFA_PRO+ TOT_SPP_IFL_PRO+ TOT_SPP_ICF_PRO.

PROCESSOR_FLAGS
Logical processor flags. 80 = wait state assist enabled; 40 = wait state assist changed; 20 = relative share changed; 10 = partition capping is enabled; and 08 = partition capping status changed. From SMF70VPF.

PROCESSOR_TYPE
Name of the logical processor type. Possible values are:

Name

Description

CP

General purpose processor

ICF+

ICF pool (includes IFA, IFL and ICF)

IFA

Integrated Facility for Application processor

IFL

Integrated Facility for Linux processor

ICF

Internal Coupling Facility processor

IIP

Integrated Information Processor

PRSM_FLAGS
PR/SM flags. 80 = diagnose X'204' failure; 40 = number of physical processors changed; 20 = global time slice value changed; and 10 = physical partition included. From SMF70INB.

RECORD_LEVEL
SMF record level change number. This column enables processing of SMF record level changes in an existing release. From SMF70SRL.

RMF_VERSION
RMF version number. From SMF72MFV.

SYSPLEX_NAME
The sysplex name to be associated with the MVS system ID or the MVS system name.

SYSTEM_NAME
MVS system name

TIME
Time (rounded down as specified in the MVSPM_TIME_RES lookup table) of the mid measurement.

TIME_RESOLUTION
Time resolution for the set of tables, in minutes. This defines the time period for which data is to be recorded. Valid values are 1, 2, 3, 5, 6, 10, 12, 15, 20, and 30 or any multiple of 60.

WEIGHT_ACTUAL
Actual weighting of the shared processor resources. Valid only for z/OS system with WLM active. Calculated as the average of SMF70ACS / SMF70DSA.

WLM_CAPPING_PCT
Percentage of WLM capping of the partition. Valid only for CP processors. Calculated as the maximum of (SMF70NSW/SMF70DSA) * 100.

GUARANTEED_MIPS
Guaranteed busy MIPS time.

LOGIC_PROC_MIPS
Logical processor busy MIPS time.

GMT_OFFSET
Offset Greenwich Mean Time (GMT) to local time, in seconds, and calculated as SMF70LGO/4096E6.

Parent topic: Presentation View

LPAR MSU Forecast Metrics tableFramework Manager tablesLPAR MSU Forecast MetricsThe LPAR MSU Forecast Metrics table contains the following columns.DATEDate of the mid measurement.TIMETime of the mid measurement.LPAR_NAMEName of the logical partition. From SMF70LPM.CPC_SERIAL_NOLast four digits of the CPC serial number. From SMF70SER.MVS_SYSTEM_IDMVS system ID.LPAR_MSULPAR consumed MSU hourlyLPAR_MSU_4HRALPAR four hour rolling average MSU.LPAR_MSU_4HRA_UTILRALPAR four hour rolling average MSU after capping.CPC_MSU_4HRA_UTILRA_FORECASTCPC four hour rolling average MSU after capping.LPAR_MSU_FORECASTForecasted LPAR consumed MSU hourly.LPAR_MSU_4HRA_FORECASTForecasted LPAR four hour rolling average MSU.LPAR_MSU_4HRA_UTILRA_FORECASTForecasted LPAR four hour rolling average MSU after capping.CAPACITY_LIMIT_MSUDefined capacity limit of the partition in MSUs per hour.CPU_MODEL_NOCPU model number. From SMF70MOD.CPC_MODEL_IDCPC model ID. From SMF70MDL.NOMINAL_CAPACITYNominal processor capacity available to the central processor complex, measured in MSU/h. Calculated as the average of SMF70NCR.CAPACITY_GRP_NMCAPACITY_GRP_NMacity group this partition belongs to. From SMF70GNM.CAPACITY_GRP_LMTGroup maximum licensing units which may be consumed per unit of time, on average. From SMF70GMU.CAPACITY_GRP_AVLLong-term average of CPU service in millions of service units which would be allowed by the limit of the capacity group, but is not used by its members. If the value is negative, this capacity group is subject to capping. From SMF70GAU.MACHINE_SERIAL_NOz/OS machine Serial No. HOUR_NOHour No.GMT_OFFSETOffset GMT to local time, in seconds. Calculated as SMF70LGO/4096E6.PROC_CAPACITY_MSUProcessor capacity available to MVS image, measured in MSUs. Calculated as the maximum of SMF70WLA. FORECAST_TIME_LEVELForecast time level. Possible values are hourly, monthly.Presentation ViewThe Presentation View includes shortcuts to the existing items in the Logical View.

LPAR MSU Forecast Metrics table

The LPAR MSU Forecast Metrics table contains the following columns.

DATE

Date of the mid measurement.

TIME

Time of the mid measurement.

LPAR_NAME

Name of the logical partition. From SMF70LPM.

CPC_SERIAL_NO

Last four digits of the CPC serial number. From SMF70SER.

MVS_SYSTEM_ID

MVS™ system ID.

LPAR_MSU

LPAR consumed MSU hourly

LPAR_MSU_4HRA

LPAR four hour rolling average MSU.

LPAR_MSU_4HRA_UTILRA

LPAR four hour rolling average MSU after capping.

CPC_MSU_4HRA_UTILRA_FORECAST

CPC four hour rolling average MSU after capping.

LPAR_MSU_FORECAST

Forecasted LPAR consumed MSU hourly.

LPAR_MSU_4HRA_FORECAST

Forecasted LPAR four hour rolling average MSU.

LPAR_MSU_4HRA_UTILRA_FORECAST

Forecasted LPAR four hour rolling average MSU after capping.

CAPACITY_LIMIT_MSU

Defined capacity limit of the partition in MSUs per hour.

CPU_MODEL_NO

CPU model number. From SMF70MOD.

CPC_MODEL_ID

CPC model ID. From SMF70MDL.

NOMINAL_CAPACITY

Nominal processor capacity available to the central processor complex, measured in MSU/h. Calculated as the average of SMF70NCR.

CAPACITY_GRP_NM

CAPACITY_GRP_NMacity group this partition belongs to. From SMF70GNM.

CAPACITY_GRP_LMT

Group maximum licensing units which may be consumed per unit of time, on average. From SMF70GMU.

CAPACITY_GRP_AVL

Long-term average of CPU service in millions of service units which would be allowed by the limit of the capacity group, but is not used by its members. If the value is negative, this capacity group is subject to capping. From SMF70GAU.

MACHINE_SERIAL_NO

z/OS® machine Serial No.

HOUR_NO

Hour No.

GMT_OFFSET

Offset GMT to local time, in seconds. Calculated as SMF70LGO/4096E6.

PROC_CAPACITY_MSU

Processor capacity available to MVS image, measured in MSUs. Calculated as the maximum of SMF70WLA.

FORECAST_TIME_LEVEL

Forecast time level. Possible values are hourly, monthly.

Parent topic: Presentation View

LPAR MSU Optimization Metrics tableFramework Manager tablesLPAR MSU Optimization MetricsThe LPAR MSU Optimization Metrics table contains the following columns.DATEDate of the mid measurement.TIMETime of the mid measurement.LPAR_NAMEName of the logical partition. From SMF70LPM.CPC_SERIAL_NOLast four digits of the CPC serial number. From SMF70SER.MVS_SYSTEM_IDMVS system ID.LPAR_MSU_FORECASTForecasted LPAR consumed MSU hourlyLPAR_MSU_OPTIMIZEDOptimized LPAR consumed MSU hourlyLPAR_MSU_4HRA_OPTIMIZEDOptimized LPAR four hour rolling average MSULPAR_MSU_4HRA_UTILRA_OPTIMIZEDOptimized LPAR four hour rolling average MSU after capping.CPC_MSU_4HRA_UTILRA_OPTIMIZEDOptimized CPC four hour rolling average MSU after capping.LPAR_MSU_4HRA_FORECASTForecasted LPAR four hour rolling average MSU.LPAR_MSU_4HRA_UTILRA_FORECASTForecasted LPAR four hour rolling average MSU after capping.CAPACITY_LIMIT_MSUDefined capacity limit of the partition in MSUs per hour.CPU_MODEL_NOCPU model number. From SMF70MOD.CPC_MODEL_IDCPC model ID. From SMF70MDL.NOMINAL_CAPACITYNominal processor capacity available to the central processor complex, measured in MSU/h. Calculated as the average of SMF70NCR.CAPACITY_GRP_NMCAPACITY_GRP_NMacity group this partition belongs to. From SMF70GNM.CAPACITY_GRP_LMTGroup maximum licensing units which may be consumed per unit of time, on average. From SMF70GMU.CAPACITY_GRP_AVLLong-term average of CPU service in millions of service units which would be allowed by the limit of the capacity group, but is not used by its members. If the value is negative, this capacity group is subject to capping. From SMF70GAU.MACHINE_SERIAL_NOz/OS machine Serial No. GMT_OFFSETOffset GMT to local time, in seconds. Calculated as SMF70LGO/4096E6.PROC_CAPACITY_MSUProcessor capacity available to MVS image, measured in MSUs. Calculated as the maximum of SMF70WLA. HOUR_NOHour No.Presentation ViewThe Presentation View includes shortcuts to the existing items in the Logical View.

LPAR MSU Optimization Metrics table

The LPAR MSU Optimization Metrics table contains the following columns.

DATE

Date of the mid measurement.

TIME

Time of the mid measurement.

LPAR_NAME

Name of the logical partition. From SMF70LPM.

CPC_SERIAL_NO

Last four digits of the CPC serial number. From SMF70SER.

MVS_SYSTEM_ID

MVS™ system ID.

LPAR_MSU_FORECAST

Forecasted LPAR consumed MSU hourly

LPAR_MSU_OPTIMIZED

Optimized LPAR consumed MSU hourly

LPAR_MSU_4HRA_OPTIMIZED

Optimized LPAR four hour rolling average MSU

LPAR_MSU_4HRA_UTILRA_OPTIMIZED

Optimized LPAR four hour rolling average MSU after capping.

CPC_MSU_4HRA_UTILRA_OPTIMIZED

Optimized CPC four hour rolling average MSU after capping.

LPAR_MSU_4HRA_FORECAST

Forecasted LPAR four hour rolling average MSU.

LPAR_MSU_4HRA_UTILRA_FORECAST

Forecasted LPAR four hour rolling average MSU after capping.

CAPACITY_LIMIT_MSU

Defined capacity limit of the partition in MSUs per hour.

CPU_MODEL_NO

CPU model number. From SMF70MOD.

CPC_MODEL_ID

CPC model ID. From SMF70MDL.

NOMINAL_CAPACITY

Nominal processor capacity available to the central processor complex, measured in MSU/h. Calculated as the average of SMF70NCR.

CAPACITY_GRP_NM

CAPACITY_GRP_NMacity group this partition belongs to. From SMF70GNM.

CAPACITY_GRP_LMT

Group maximum licensing units which may be consumed per unit of time, on average. From SMF70GMU.

CAPACITY_GRP_AVL

Long-term average of CPU service in millions of service units which would be allowed by the limit of the capacity group, but is not used by its members. If the value is negative, this capacity group is subject to capping. From SMF70GAU.

MACHINE_SERIAL_NO

z/OS® machine Serial No.

GMT_OFFSET

Offset GMT to local time, in seconds. Calculated as SMF70LGO/4096E6.

PROC_CAPACITY_MSU

Processor capacity available to MVS image, measured in MSUs. Calculated as the maximum of SMF70WLA.

HOUR_NO

Hour No.

Parent topic: Presentation View

LPAR MSU Metrics tableFramework Manager tablesLPAR MSU MetricsThe LPAR MSU Metrics table contains the following columns.DATEDate of the mid measurement.TIMETime of the mid measurement.CPC_SERIAL_NOLast four digits of the CPC serial number. From SMF70SER.CPC_MODEL_NOCPU model number. From SMF70MOD.CPC_MODEL_IDCPC model ID. From SMF70MDL.LPAR_NAMEName of the logical partition. From SMF70LPM.MVS_SYSTEM_IDMVS system ID.PROC_CAPACITY_MSUProcessor capacity available to MVS image, measured in MSUs. Calculated as the maximum of SMF70WLA.MSU_4HRALong-term average of CPU service (millions of service units). From SMF70LAC.LPAR_MSU_4HRALPAR 4 hour rolling average MSULPAR_MSU_4HUTILRAFour hour rolling average LPAR MSU after capping.LPAR_CONSUMED_MSULPAR consumed MSUCAPACITY_LIMIT_MSUDefined capacity limit of the partition in MSUs per hour.LPAR_MSU_1HTOTLPAR consumed MSU hourly.HOUR_NOHour No.CAPACITY_GRP_NMCAPACITY_GRP_NMacity group this partition belongs to. From SMF70GNM.CAPACITY_GRP_LMTGroup maximum licensing units which may be consumed per unit of time, on average. From SMF70GMU.CAPACITY_GRP_AVLLong-term average of CPU service in millions of service units which would be allowed by the limit of the capacity group, but is not used by its members. If the value is negative, this capacity group is subject to capping. From SMF70GAU.CPC_MSU_4HUTILRACPC four hour rolling average MSU after capping.GMT_OFFSETOffset GMT to local time, in seconds. Calculated as SMF70LGO/4096E6.Presentation ViewThe Presentation View includes shortcuts to the existing items in the Logical View.

LPAR MSU Metrics table

The LPAR MSU Metrics table contains the following columns.

DATE

Date of the mid measurement.

TIME

Time of the mid measurement.

CPC_SERIAL_NO

Last four digits of the CPC serial number. From SMF70SER.

CPC_MODEL_NO

CPU model number. From SMF70MOD.

CPC_MODEL_ID

CPC model ID. From SMF70MDL.

LPAR_NAME

Name of the logical partition. From SMF70LPM.

MVS_SYSTEM_ID

MVS system ID.

PROC_CAPACITY_MSU

Processor capacity available to MVS image, measured in MSUs. Calculated as the maximum of SMF70WLA.

MSU_4HRA

Long-term average of CPU service (millions of service units). From SMF70LAC.

LPAR_MSU_4HRA

LPAR 4 hour rolling average MSU

LPAR_MSU_4HUTILRA

Four hour rolling average LPAR MSU after capping.

LPAR_CONSUMED_MSU

LPAR consumed MSU

CAPACITY_LIMIT_MSU

Defined capacity limit of the partition in MSUs per hour.

LPAR_MSU_1HTOT

LPAR consumed MSU hourly.

HOUR_NO

Hour No.

CAPACITY_GRP_NM

CAPACITY_GRP_NMacity group this partition belongs to. From SMF70GNM.

CAPACITY_GRP_LMT

Group maximum licensing units which may be consumed per unit of time, on average. From SMF70GMU.

CAPACITY_GRP_AVL

Long-term average of CPU service in millions of service units which would be allowed by the limit of the capacity group, but is not used by its members. If the value is negative, this capacity group is subject to capping. From SMF70GAU.

CPC_MSU_4HUTILRA

CPC four hour rolling average MSU after capping.

GMT_OFFSET

Offset GMT to local time, in seconds. Calculated as SMF70LGO/4096E6.

Parent topic: Presentation View

MIPS Capacity Ratings tableThis table provides the CPU_SERIAL_MIPS rating for your processors.MIPS Capacity Ratings tableColumnDescriptionCPC_MODEL_IDCPC model identification. From the field SMF70MDL.CPU_MODEL_NOCPU model number. From SMF70MOD.CPU_SERIAL_NOLast 4 digits of the CPU serial number. From SMF70SER.MIPS_TOT_CAPACITYTotal MIPS capacity for specified process type and LPAR.PHY_PROC_CNTThe number of physical processors. For example, when processor type is CP, phy_proc_cnt = phy_processors_cp.PROCESSOR_TYPEName of the logical processor type. Possible values are:NameDescriptionCPGeneral purpose processor ICF+ICF pool (includes IFA, IFL and ICF) IFAIntegrated Facility for Application processorIFLIntegrated Facility for Linux processor ICF Internal Coupling Facility processor IIP Integrated Information Processor Presentation ViewThe Presentation View includes shortcuts to the existing items in the Logical View.

MIPS Capacity Ratings table

This table provides the CPU_SERIAL_MIPS rating for your processors.

Table 25. MIPS Capacity Ratings table

Column
Description

CPC_MODEL_ID
CPC model identification. From the field SMF70MDL.

CPU_MODEL_NO
CPU model number. From SMF70MOD.

CPU_SERIAL_NO
Last 4 digits of the CPU serial number. From SMF70SER.

MIPS_TOT_CAPACITY
Total MIPS capacity for specified process type and LPAR.

PHY_PROC_CNT
The number of physical processors. For example, when processor type is CP, phy_proc_cnt = phy_processors_cp.

PROCESSOR_TYPE
Name of the logical processor type. Possible values are:

Name

Description

CP

General purpose processor

ICF+

ICF pool (includes IFA, IFL and ICF)

IFA

Integrated Facility for Application processor

IFL

Integrated Facility for Linux processor

ICF

Internal Coupling Facility processor

IIP

Integrated Information Processor

Parent topic: Presentation View

Movement Suggestion by Optimization Metrics tableFramework Manager tablesMovement Suggestion by Optimization MetricsThe Movement Suggestion by Optimization Metrics table contains the following columns.MOVE_STEP_IDThe sequence to do the movement.TIMESTAMPTimestamp of the mid measurement.FROM_LPAR_NAMEOptimization movement source LPAR name.FROM_CPC_SERIAL_NOSource CPC serial number.FROM_MVS_SYSTEM_IDSource MVS system ID.PROD_IDProduct ID number.PROD_NAMEProduct name. From SMF89UPN or SMF89T2ProdName. PROD_VERSIONProduct version. From SMF89UPV or SMF89T2ProdVers.PRICING_STRUCTUREProduct sub-capacity program.TO_LPAR_NAMEOptimization movement target LPAR name.TO_CPC_SERIAL_NOMovement target CPC serial number.TO_MVS_SYSTEM_IDMovement source MVS system ID.MOVING_AMOUNTThe amount of the workload (MSU) for this product that should be moved in this step. (-1 mean the remainder of the workload.)MOVE_ALL1 - move all of the remaining workload (MSU) of this product in this step. 0 - move the amount of the workload indicated by MOVING_AMOUNT.TIMEZONEThe time zone of the timestamp.Presentation ViewThe Presentation View includes shortcuts to the existing items in the Logical View.

Movement Suggestion by Optimization Metrics table

The Movement Suggestion by Optimization Metrics table contains the following columns.

MOVE_STEP_ID

The sequence to do the movement.

TIMESTAMP

Timestamp of the mid measurement.

FROM_LPAR_NAME

Optimization movement source LPAR name.

FROM_CPC_SERIAL_NO

Source CPC serial number.

FROM_MVS_SYSTEM_ID

Source MVS™ system ID.

PROD_ID

Product ID number.

PROD_NAME

Product name. From SMF89UPN or SMF89T2ProdName.

PROD_VERSION

Product version. From SMF89UPV or SMF89T2ProdVers.

PRICING_STRUCTURE

Product sub-capacity program.

TO_LPAR_NAME

Optimization movement target LPAR name.

TO_CPC_SERIAL_NO

Movement target CPC serial number.

TO_MVS_SYSTEM_ID

Movement source MVS system ID.

MOVING_AMOUNT

The amount of the workload (MSU) for this product that should be moved in this step. (-1 mean the remainder of the workload.)

MOVE_ALL

1 - move all of the remaining workload (MSU) of this product in this step. 0 - move the amount of the workload indicated by MOVING_AMOUNT.

TIMEZONE

The time zone of the timestamp.

Parent topic: Presentation View

NO89 Products tableFramework Manager tablesNO89 ProductsThe NO89 Products table contains the following columns.STARTDATEStart date of the mid measurement. The default is 1900-01-01.ENDDATEEnd date of the mid measurement. The default is 2900-01-01PRODUCT_IDProduct ID number.CPC_MODEL_NOCPC model number. From SMF70MOD.CPC_SERIAL_NOLast 4 digits of the CPC serial number. From SMF70SER.PRODUCT_NAMEProduct name.LPAR_NAMEName of the logical partition. From SMF70LPM.Presentation ViewThe Presentation View includes shortcuts to the existing items in the Logical View.

NO89 Products table

The NO89 Products table contains the following columns.

STARTDATE

Start date of the mid measurement. The default is 1900-01-01.

ENDDATE

End date of the mid measurement. The default is 2900-01-01

PRODUCT_ID

Product ID number.

CPC_MODEL_NO

CPC model number. From SMF70MOD.

CPC_SERIAL_NO

Last 4 digits of the CPC serial number. From SMF70SER.

PRODUCT_NAME

Product name.

LPAR_NAME

Name of the logical partition. From SMF70LPM.

Parent topic: Presentation View

Parents of Sub-Capacity Reference-based Programs tableFramework Manager tablesParents of Sub-Capacity Reference-based ProgramsThe Parents of Sub-Capacity Reference-based Programs table contains the following columns.PROD_IDProduct ID number.PARENT_NAMEProduct Parent name.DESCRIPTIONProduct standard name from IBM website.Presentation ViewThe Presentation View includes shortcuts to the existing items in the Logical View.

Parents of Sub-Capacity Reference-based Programs table

The Parents of Sub-Capacity Reference-based Programs table contains the following columns.

PROD_ID

Product ID number.

PARENT_NAME

Product Parent name.

DESCRIPTION

Product standard name from IBM® website.

Parent topic: Presentation View

Product Billable MSU Forecast Metrics tableFramework Manager tablesProduct Billable MSU Forecast MetricsThe Product Billable MSU Forecast Metrics table contains the following columns.DATEDate of the mid measurement.TIMETime of the mid measurement.TIMESTAMPTimestamp of the mid measurement.PROD_IDProduct ID number.PROD_VERSIONProduct version. From SMF89UPV or SMF89T2ProdVers.PROD_NAMEProduct name. From SMF89UPN or SMF89T2ProdName. PRODUCT_USE_VALUECombined product ID, product name and pricing structure. PRICING_STRUCTUREProduct sub-capacity program.LPAR_NAMEName of the logical partition. From SMF70LPM.CPC_SERIAL_NOLast four digits of the CPC serial number. From SMF70SER.MVS_SYSTEM_IDMVS system ID.IWPProduct IWP program. For IWP program, this is 1.GSSPProduct GSSP program. For GSSP product, this is 1.IWP_ADJUSTEDProduct IWP adjusted program. For IWP adjusted product, this is 1.ZOS_BASEDProduct z/OS based program. For z/OS based product, this is 1.REFERENCE_BASEDProduct reference based program. For reference based product, this is 1.EXECUTION_BASEDProduct IPLA execution based program. For execution based product, this is 1.ZNALC_LICENSEIndicates that LICENSE=zNALC was specified in EASYSxx. Possible values are Y or N. From Bit 1 of SMF89PFL. IWP_DEFININGProduct IWP defining program. For IWP defining product, this is 1. PROD_PARENTProduct Parent name.PROD_LPAR_BILLABLE_MSULPAR billable MSU.PROD_LPAR_BILLABLE_MSU_FForecasted LPAR billable MSU.PROD_BILLABLE_MSUProduct billable MSU.PROD_BILLABLE_MSU_FForecasted product billable MSU.CAPACITY_GRP_NMCAPACITY_GRP_NMacity group this partition belongs to. From SMF70GNM.CAPACITY_GRP_LMTGroup maximum licensing units which may be consumed per unit of time, on average. From SMF70GMU.GMT_OFFSETOffset GMT to local time, in seconds. Calculated as SMF70LGO/4096E6.HOUR_NOHour No.PROD_SHORT_VERSIONProduct short version. PROD_FEATURE_NMProduct qualifier. From SMF89UPQ or SMF89T2FeatureName.Presentation ViewThe Presentation View includes shortcuts to the existing items in the Logical View.

Product Billable MSU Forecast Metrics table

The Product Billable MSU Forecast Metrics table contains the following columns.

DATE

Date of the mid measurement.

TIME

Time of the mid measurement.

TIMESTAMP

Timestamp of the mid measurement.

PROD_ID

Product ID number.

PROD_VERSION

Product version. From SMF89UPV or SMF89T2ProdVers.

PROD_NAME

Product name. From SMF89UPN or SMF89T2ProdName.

PRODUCT_USE_VALUE

Combined product ID, product name and pricing structure.

PRICING_STRUCTURE

Product sub-capacity program.

LPAR_NAME

Name of the logical partition. From SMF70LPM.

CPC_SERIAL_NO

Last four digits of the CPC serial number. From SMF70SER.

MVS_SYSTEM_ID

MVS™ system ID.

IWP

Product IWP program. For IWP program, this is 1.

GSSP

Product GSSP program. For GSSP product, this is 1.

IWP_ADJUSTED

Product IWP adjusted program. For IWP adjusted product, this is 1.

ZOS_BASED

Product z/OS® based program. For z/OS based product, this is 1.

REFERENCE_BASED

Product reference based program. For reference based product, this is 1.

EXECUTION_BASED

Product IPLA execution based program. For execution based product, this is 1.

ZNALC_LICENSE

Indicates that LICENSE=zNALC was specified in EASYSxx. Possible values are Y or N. From Bit 1 of SMF89PFL.

IWP_DEFINING

Product IWP defining program. For IWP defining product, this is 1.

PROD_PARENT

Product Parent name.

PROD_LPAR_BILLABLE_MSU

LPAR billable MSU.

PROD_LPAR_BILLABLE_MSU_F

Forecasted LPAR billable MSU.

PROD_BILLABLE_MSU

Product billable MSU.

PROD_BILLABLE_MSU_F

Forecasted product billable MSU.

CAPACITY_GRP_NM

CAPACITY_GRP_NMacity group this partition belongs to. From SMF70GNM.

CAPACITY_GRP_LMT

Group maximum licensing units which may be consumed per unit of time, on average. From SMF70GMU.

GMT_OFFSET

Offset GMT to local time, in seconds. Calculated as SMF70LGO/4096E6.

HOUR_NO

Hour No.

PROD_SHORT_VERSION

Product short version.

PROD_FEATURE_NM

Product qualifier. From SMF89UPQ or SMF89T2FeatureName.

Parent topic: Presentation View

Product Billable MSU Optimization Metrics tableFramework Manager tablesProduct Billable MSU Optimization MetricsThe Product Billable MSU Optimization Metrics table contains the following columns.DATEDate of the mid measurement.TIMETime of the mid measurement.TIMESTAMPTimestamp of the mid measurement.PROD_IDProduct ID number.PROD_VERSIONProduct version. From SMF89UPV or SMF89T2ProdVers.PROD_NAMEProduct name. From SMF89UPN or SMF89T2ProdName. PRODUCT_USE_VALUECombined product ID, product name and pricing structure. PRICING_STRUCTUREProduct sub-capacity program.LPAR_NAMEName of the logical partition. From SMF70LPM.CPC_SERIAL_NOLast four digits of the CPC serial number. From SMF70SER.MVS_SYSTEM_IDMVS system ID.IWPProduct IWP program. For IWP program, this is 1.GSSPProduct GSSP program. For GSSP product, this is 1.IWP_ADJUSTEDProduct IWP adjusted program. For IWP adjusted product, this is 1.ZOS_BASEDProduct z/OS based program. For z/OS based product, this is 1.REFERENCE_BASEDProduct reference based program. For reference based product, this is 1.EXECUTION_BASEDProduct IPLA execution based program. For execution based product, this is 1.ZNALC_LICENSEIndicates that LICENSE=zNALC was specified in EASYSxx. Possible values are Y or N. From Bit 1 of SMF89PFL. IWP_DEFININGProduct IWP defining program. For IWP defining product, this is 1. PROD_PARENTProduct parent name.PROD_LPAR_BILLABLE_MSU_OOptimized LPAR billable MSU.PROD_LPAR_BILLABLE_MSU_OOptimized LPAR billable MSU.PROD_LPAR_BILLABLE_MSU_FForecasted LPAR billable MSU.PROD_BILLABLE_MSU_OOptimized product billable MSU.PROD_BILLABLE_MSU_FForecasted product billable MSU.CAPACITY_GRP_NMCAPACITY_GRP_NMacity group this partition belongs to. From SMF70GNM.CAPACITY_GRP_LMTGroup maximum licensing units which may be consumed per unit of time, on average. From SMF70GMU.GMT_OFFSETOffset GMT to local time, in seconds. Calculated as SMF70LGO/4096E6.HOUR_NOHour No.PROD_SHORT_VERSIONProduct short version. PROD_FEATURE_NMProduct qualifier. From SMF89UPQ or SMF89T2FeatureName.Presentation ViewThe Presentation View includes shortcuts to the existing items in the Logical View.

Product Billable MSU Optimization Metrics table

The Product Billable MSU Optimization Metrics table contains the following columns.

DATE

Date of the mid measurement.

TIME

Time of the mid measurement.

TIMESTAMP

Timestamp of the mid measurement.

PROD_ID

Product ID number.

PROD_VERSION

Product version. From SMF89UPV or SMF89T2ProdVers.

PROD_NAME

Product name. From SMF89UPN or SMF89T2ProdName.

PRODUCT_USE_VALUE

Combined product ID, product name and pricing structure.

PRICING_STRUCTURE

Product sub-capacity program.

LPAR_NAME

Name of the logical partition. From SMF70LPM.

CPC_SERIAL_NO

Last four digits of the CPC serial number. From SMF70SER.

MVS_SYSTEM_ID

MVS™ system ID.

IWP

Product IWP program. For IWP program, this is 1.

GSSP

Product GSSP program. For GSSP product, this is 1.

IWP_ADJUSTED

Product IWP adjusted program. For IWP adjusted product, this is 1.

ZOS_BASED

Product z/OS® based program. For z/OS based product, this is 1.

REFERENCE_BASED

Product reference based program. For reference based product, this is 1.

EXECUTION_BASED

Product IPLA execution based program. For execution based product, this is 1.

ZNALC_LICENSE

Indicates that LICENSE=zNALC was specified in EASYSxx. Possible values are Y or N. From Bit 1 of SMF89PFL.

IWP_DEFINING

Product IWP defining program. For IWP defining product, this is 1.

PROD_PARENT

Product parent name.

PROD_LPAR_BILLABLE_MSU_O

Optimized LPAR billable MSU.

PROD_LPAR_BILLABLE_MSU_O

Optimized LPAR billable MSU.

PROD_LPAR_BILLABLE_MSU_F

Forecasted LPAR billable MSU.

PROD_BILLABLE_MSU_O

Optimized product billable MSU.

PROD_BILLABLE_MSU_F

Forecasted product billable MSU.

CAPACITY_GRP_NM

CAPACITY_GRP_NMacity group this partition belongs to. From SMF70GNM.

CAPACITY_GRP_LMT

Group maximum licensing units which may be consumed per unit of time, on average. From SMF70GMU.

GMT_OFFSET

Offset GMT to local time, in seconds. Calculated as SMF70LGO/4096E6.

HOUR_NO

Hour No.

PROD_SHORT_VERSION

Product short version.

PROD_FEATURE_NM

Product qualifier. From SMF89UPQ or SMF89T2FeatureName.

Parent topic: Presentation View

Product MSU Forecast Metrics tableFramework Manager tablesProduct MSU Forecast MetricsThe Product MSU Forecast Metrics table contains the following columns.DATEDate of the mid measurement.TIMETime of the mid measurement.LPAR_NAMEName of the logical partition. From SMF70LPM.CPC_SERIAL_NOLast four digits of the CPC serial number. From SMF70SER.MVS_SYSTEM_IDMVS system ID.PROD_VERSIONProduct version. From SMF89UPV or SMF89T2ProdVers.PROD_IDProduct ID number.PROD_NAMEProduct name. From SMF89UPN or SMF89T2ProdName. PRODUCT_USE_VALUECombined product ID, product name and pricing structure. PRICING_STRUCTUREProduct sub-capacity program.ZOS_BASEDProduct z/OS based program. For z/OS based product, this is 1.REFERENCE_BASEDProduct reference based program. For reference based product, this is 1.GSSPProduct GSSP program. For GSSP product, this is 1.EXECUTION_BASEDProduct IPLA execution based program. For execution based product, this is 1.GSSP_DB2_UTIL_MSUGSSP DB2 MSU value.GSSP_DB2_UTIL_MSU_FForecasted GSSP DB2 MSU.PROD_MSUProduct consumed MSU hourly.PROD_MSU_4HRAFour hour rolling average product MSU.PROD_MSU_4HRA_UTILRAFour hour rolling average product MSU after capping.PROD_MSU_FORECASTForecasted product MSU.PROD_MSU_4HRA_FORECASTForecasted product four hour rolling average MSU.PROD_MSU_4HRA_UTILRA_FORECASTForecasted product four hour rolling average MSU after capping.CAPACITY_LIMIT_MSUDefined capacity limit of the partition in MSUs per hour.IWPProduct IWP program. For IWP program, this is 1.GSSPProduct GSSP program. For GSSP product, this is 1.IWP_ADJUSTEDProduct IWP adjusted program. For IWP adjusted product, this is 1.CPU_MODEL_NOCPU model number. From SMF70MOD.CPC_MODEL_IDCPC model ID. From SMF70MDL.NOMINAL_CAPACITYNominal processor capacity available to the central processor complex, measured in MSU/h. Calculated as the average of SMF70NCR.CAPACITY_GRP_NMCAPACITY_GRP_NMacity group this partition belongs to. From SMF70GNM.CAPACITY_GRP_LMTGroup maximum licensing units which may be consumed per unit of time, on average. From SMF70GMU.CAPACITY_GRP_AVLLong-term average of CPU service in millions of service units which would be allowed by the limit of the capacity group, but is not used by its members. If the value is negative, this capacity group is subject to capping. From SMF70GAU.ZNALC_LICENSEIndicates that LICENSE=zNALC was specified in EASYSxx. Possible values are Y or N. From Bit 1 of SMF89PFL. MACHINE_SERIAL_NOz/OS machine Serial No. HOUR_NOHour No.GMT_OFFSETOffset GMT to local time, in seconds. Calculated as SMF70LGO/4096E6.PROD_PARENTProduct Parent name.GSSP_SCALING_FACTORGSSP Product scaling factor. Default is 2.IWP_DEFININGProduct IWP defining program. For IWP defining product, this is 1.PROC_CAPACITY_MSUProcessor capacity available to MVS image, measured in MSUs. Calculated as the maximum of SMF70WLA. FORECAST_TIME_LEVELForecast time level. Possible values are hourly, monthly. PROD_SHORT_VERSIONProduct short version. PROD_FEATURE_NMProduct qualifier. From SMF89UPQ or SMF89T2FeatureName.Presentation ViewThe Presentation View includes shortcuts to the existing items in the Logical View.

Product MSU Forecast Metrics table

The Product MSU Forecast Metrics table contains the following columns.

DATE

Date of the mid measurement.

TIME

Time of the mid measurement.

LPAR_NAME

Name of the logical partition. From SMF70LPM.

CPC_SERIAL_NO

Last four digits of the CPC serial number. From SMF70SER.

MVS_SYSTEM_ID

MVS™ system ID.

PROD_VERSION

Product version. From SMF89UPV or SMF89T2ProdVers.

PROD_ID

Product ID number.

PROD_NAME

Product name. From SMF89UPN or SMF89T2ProdName.

PRODUCT_USE_VALUE

Combined product ID, product name and pricing structure.

PRICING_STRUCTURE

Product sub-capacity program.

ZOS_BASED

Product z/OS® based program. For z/OS based product, this is 1.

REFERENCE_BASED

Product reference based program. For reference based product, this is 1.

GSSP

Product GSSP program. For GSSP product, this is 1.

EXECUTION_BASED

Product IPLA execution based program. For execution based product, this is 1.

GSSP_DB2_UTIL_MSU

GSSP DB2® MSU value.

GSSP_DB2_UTIL_MSU_F

Forecasted GSSP DB2 MSU.

PROD_MSU

Product consumed MSU hourly.

PROD_MSU_4HRA

Four hour rolling average product MSU.

PROD_MSU_4HRA_UTILRA

Four hour rolling average product MSU after capping.

PROD_MSU_FORECAST

Forecasted product MSU.

PROD_MSU_4HRA_FORECAST

Forecasted product four hour rolling average MSU.

PROD_MSU_4HRA_UTILRA_FORECAST

Forecasted product four hour rolling average MSU after capping.

CAPACITY_LIMIT_MSU

Defined capacity limit of the partition in MSUs per hour.

IWP

Product IWP program. For IWP program, this is 1.

GSSP

Product GSSP program. For GSSP product, this is 1.

IWP_ADJUSTED

Product IWP adjusted program. For IWP adjusted product, this is 1.

CPU_MODEL_NO

CPU model number. From SMF70MOD.

CPC_MODEL_ID

CPC model ID. From SMF70MDL.

NOMINAL_CAPACITY

Nominal processor capacity available to the central processor complex, measured in MSU/h. Calculated as the average of SMF70NCR.

CAPACITY_GRP_NM

CAPACITY_GRP_NMacity group this partition belongs to. From SMF70GNM.

CAPACITY_GRP_LMT

Group maximum licensing units which may be consumed per unit of time, on average. From SMF70GMU.

CAPACITY_GRP_AVL

Long-term average of CPU service in millions of service units which would be allowed by the limit of the capacity group, but is not used by its members. If the value is negative, this capacity group is subject to capping. From SMF70GAU.

ZNALC_LICENSE

Indicates that LICENSE=zNALC was specified in EASYSxx. Possible values are Y or N. From Bit 1 of SMF89PFL.

MACHINE_SERIAL_NO

z/OS machine Serial No.

HOUR_NO

Hour No.

GMT_OFFSET

Offset GMT to local time, in seconds. Calculated as SMF70LGO/4096E6.

PROD_PARENT

Product Parent name.

GSSP_SCALING_FACTOR

GSSP Product scaling factor. Default is 2.

IWP_DEFINING

Product IWP defining program. For IWP defining product, this is 1.

PROC_CAPACITY_MSU

Processor capacity available to MVS image, measured in MSUs. Calculated as the maximum of SMF70WLA.

FORECAST_TIME_LEVEL

Forecast time level. Possible values are hourly, monthly.

PROD_SHORT_VERSION

Product short version.

PROD_FEATURE_NM

Product qualifier. From SMF89UPQ or SMF89T2FeatureName.

Parent topic: Presentation View

Product MSU Optimization Metrics tableFramework Manager tablesProduct MSU Optimization MetricsThe Product MSU Optimization Metrics table contains the following columns.DATEDate of the mid measurement.TIMETime of the mid measurement.LPAR_NAMEName of the logical partition. From SMF70LPM.CPC_SERIAL_NOLast four digits of the CPC serial number. From SMF70SER.MVS_SYSTEM_IDMVS system ID.PROD_VERSIONProduct version. From SMF89UPV or SMF89T2ProdVers.PROD_IDProduct ID number.PROD_NAMEProduct name. From SMF89UPN or SMF89T2ProdName. PRODUCT_USE_VALUECombined product ID, product name and pricing structure. PRICING_STRUCTUREProduct sub-capacity program.ZOS_BASEDProduct z/OS based program. For z/OS based product, this is 1.REFERENCE_BASEDProduct reference based program. For reference based product, this is 1.EXECUTION_BASEDProduct IPLA execution based program. For execution based product, this is 1.PROD_MSU_FORECASTForecasted product MSU.PROD_MSU_OPTIMIZEDOptimized product MSU.PROD_MSU_4HRA_OPTIMIZEDOptimized product four hour rolling average MSU.PROD_MSU_4HRA_UTILRA_OPTIMIZEDOptimized product four hour rolling average MSU after capping.PROD_MSU_4HRA_FORECASTForecasted product four hour rolling average MSU.PROD_MSU_4HRA_UTILRA_FORECASTForecasted product four hour rolling average MSU after capping.CAPACITY_LIMIT_MSUDefined capacity limit of the partition in MSUs per hour.IWPProduct IWP program. For IWP program, this is 1.GSSPProduct GSSP program. For GSSP product, this is 1.IWP_ADJUSTEDProduct IWP adjusted program. For IWP adjusted product, this is 1.CPU_MODEL_NOCPU model number. From SMF70MOD.CPC_MODEL_IDCPC model ID. From SMF70MDL.NOMINAL_CAPACITYNominal processor capacity available to the central processor complex, measured in MSU/h. Calculated as the average of SMF70NCR.CAPACITY_GRP_NMCAPACITY_GRP_NMacity group this partition belongs to. From SMF70GNM.CAPACITY_GRP_LMTGroup maximum licensing units which may be consumed per unit of time, on average. From SMF70GMU.CAPACITY_GRP_AVLLong-term average of CPU service in millions of service units which would be allowed by the limit of the capacity group, but is not used by its members. If the value is negative, this capacity group is subject to capping. From SMF70GAU.ZNALC_LICENSEIndicates that LICENSE=zNALC was specified in EASYSxx. Possible values are Y or N. From Bit 1 of SMF89PFL. MACHINE_SERIAL_NOz/OS machine Serial No. GMT_OFFSETOffset GMT to local time, in seconds. Calculated as SMF70LGO/4096E6.PROD_PARENTProduct Parent name.GSSP_SCALING_FACTORGSSP Product scaling factor. Default is 2.IWP_DEFININGProduct IWP defining program. For IWP defining product, this is 1.PROC_CAPACITY_MSUProcessor capacity available to MVS image, measured in MSUs. Calculated as the maximum of SMF70WLA. HOUR_NOHour No.PROD_SHORT_VERSIONProduct short version. PROD_FEATURE_NMProduct qualifier. From SMF89UPQ or SMF89T2FeatureName.Presentation ViewThe Presentation View includes shortcuts to the existing items in the Logical View.

Product MSU Optimization Metrics table

The Product MSU Optimization Metrics table contains the following columns.

DATE

Date of the mid measurement.

TIME

Time of the mid measurement.

LPAR_NAME

Name of the logical partition. From SMF70LPM.

CPC_SERIAL_NO

Last four digits of the CPC serial number. From SMF70SER.

MVS_SYSTEM_ID

MVS™ system ID.

PROD_VERSION

Product version. From SMF89UPV or SMF89T2ProdVers.

PROD_ID

Product ID number.

PROD_NAME

Product name. From SMF89UPN or SMF89T2ProdName.

PRODUCT_USE_VALUE

Combined product ID, product name and pricing structure.

PRICING_STRUCTURE

Product sub-capacity program.

ZOS_BASED

Product z/OS® based program. For z/OS based product, this is 1.

REFERENCE_BASED

Product reference based program. For reference based product, this is 1.

EXECUTION_BASED

Product IPLA execution based program. For execution based product, this is 1.

PROD_MSU_FORECAST

Forecasted product MSU.

PROD_MSU_OPTIMIZED

Optimized product MSU.

PROD_MSU_4HRA_OPTIMIZED

Optimized product four hour rolling average MSU.

PROD_MSU_4HRA_UTILRA_OPTIMIZED

Optimized product four hour rolling average MSU after capping.

PROD_MSU_4HRA_FORECAST

Forecasted product four hour rolling average MSU.

PROD_MSU_4HRA_UTILRA_FORECAST

Forecasted product four hour rolling average MSU after capping.

CAPACITY_LIMIT_MSU

Defined capacity limit of the partition in MSUs per hour.

IWP

Product IWP program. For IWP program, this is 1.

GSSP

Product GSSP program. For GSSP product, this is 1.

IWP_ADJUSTED

Product IWP adjusted program. For IWP adjusted product, this is 1.

CPU_MODEL_NO

CPU model number. From SMF70MOD.

CPC_MODEL_ID

CPC model ID. From SMF70MDL.

NOMINAL_CAPACITY

Nominal processor capacity available to the central processor complex, measured in MSU/h. Calculated as the average of SMF70NCR.

CAPACITY_GRP_NM

CAPACITY_GRP_NMacity group this partition belongs to. From SMF70GNM.

CAPACITY_GRP_LMT

Group maximum licensing units which may be consumed per unit of time, on average. From SMF70GMU.

CAPACITY_GRP_AVL

Long-term average of CPU service in millions of service units which would be allowed by the limit of the capacity group, but is not used by its members. If the value is negative, this capacity group is subject to capping. From SMF70GAU.

ZNALC_LICENSE

Indicates that LICENSE=zNALC was specified in EASYSxx. Possible values are Y or N. From Bit 1 of SMF89PFL.

MACHINE_SERIAL_NO

z/OS machine Serial No.

GMT_OFFSET

Offset GMT to local time, in seconds. Calculated as SMF70LGO/4096E6.

PROD_PARENT

Product Parent name.

GSSP_SCALING_FACTOR

GSSP Product scaling factor. Default is 2.

IWP_DEFINING

Product IWP defining program. For IWP defining product, this is 1.

PROC_CAPACITY_MSU

Processor capacity available to MVS image, measured in MSUs. Calculated as the maximum of SMF70WLA.

HOUR_NO

Hour No.

PROD_SHORT_VERSION

Product short version.

PROD_FEATURE_NM

Product qualifier. From SMF89UPQ or SMF89T2FeatureName.

Parent topic: Presentation View

Product MSU Utilization Metrics tablesThe Product MSU Utilization Metrics table, z/OS Product MSU Utilization Metrics table, and Product MSU Utilization Metrics without z/OS table all use the same structure.Framework Manager tablesProduct MSU Utilization MetricsFramework Manager tablesz/OS Product MSU Utilization MetricsFramework Manager tablesProduct MSU Utilization Metrics without z/OSThe Product MSU Utilization Metrics tables contains the following columns.DATEDate of the mid measurement.TIMETime of the mid measurement.CPC_MNFCTRV1-CPC manufacturer. From SMF89MNF. CPC_SERIAL_NOLast four digits of the CPC serial number. From SMF70SER.CPC_MODEL_NOCPU model number. From SMF70MOD.CPC_MODEL_IDCPC model ID. From SMF70MDL.MACHINE_SERIAL_NOzOS machine Serial No.LPAR_NAMEName of the logical partition. From SMF70LPM.MVS_SYSTEM_IDMVS system ID.PROD_NAMEProduct name. From SMF89UPN or SMF89T2ProdName. PROD_IDProduct ID number.PROD_PARENTProduct Parent name.SMF89_REGISTEREDProduct register in SMF89 records. For SMF89 registered, this is 1.ZOS_BASEDProduct zOS Based program. For zOS Based product, this is 1.REFERENCE_BASEDProduct reference based program. For reference Based product, this is 1.GSSPProduct GSSP program. For GSSP product, this is 1.GSSP_SCALING_FACTORGSSP Product scaling factor. Default is 2.GSSP_DB2_UTIL_MSUGSSP DB2 MSU.IWPProduct IWP program. For IWP program, this is 1.IWP_ADJUSTEDProduct IWP adjusted program. For IWP adjusted product, this is 1.IWP_DEFININGProduct IWP defining program. For IWP defining product, this is 1.EXECUTION_BASEDProduct IPLA execution based program. For execution based product, this is 1.PROD_VERSIONProduct version. From SMF89UPV or SMF89T2ProdVers.PROD_SHORT_VERSIONProduct short version.PROD_FEATURE_NMProduct qualifier. From SMF89UPQ or SMF89T2FeatureName.PRODUCT_DISPLAY_VALUECombined product ID, product name and pricing structure. PRICING_STRUCTUREProduct sub-capacity program.PRODUCT_USE_VALUECombined product ID, product name and pricing structure.PROD_CP_SECONDSProduct CPU time.PROD_MSU_CONSUMEDProduct consumed MSU.PROD_MSU_1HAVGProduct consumed MSU hourly.PROD_MSU_4HUTILRAProduct four hour rolling average MSU after capping.LPAR_MSU_4HUTILRALPAR four hour rolling average MSU after capping.HOUR_NOHour No.UNIQUE_HOUR_NOUnique hour No. from 1980-01-01 00:00:00PROC_CAPACITY_MSUProcessor capacity available to MVS image, measured in MSUs. Calculated as the maximum of SMF70WLA.LPAR_CONSUMED_MSULPAR consumed MSU.CAPACITY_LIMIT_MSUDefined capacity limit of the partition in MSUs per hour.LPAR_MSU_4HRALPAR four hour rolling average MSU.LPAR_MSU_1HTOTLPAR consumed MSU hourly.OTHER_PRODS_MSU_1HTOTOther products consumed MSU hourly.CPC_MSU_4HUTILRACPC four hour rolling average MSU after capping.CAPACITY_GRP_NMCAPACITY_GRP_NMacity group this partition belongs to. From SMF70GNM.CAPACITY_GRP_LMTGroup maximum licensing units which may be consumed per unit of time, on average. From SMF70GMU.CAPACITY_GRP_AVLLong term average of CPU service in millions of service units which would be allowed by the limit of the capacity group but is not used by its members. If the value is negative, this capacity group is subject to capping. From SMF70GAU.ZNALC_LICENSEIndicates that LICENSE=zNALC was specified in EASYSxx. Possible values are Y or N. From Bit 1 of SMF89PFL. NOMINAL_CAPACITYNominal processor capacity available to the central processor complex, measured in MSU/h. Calculated as the average of SMF70NCR.IWP_DEFINING_PROD_MSU_4HUTILRAIWP defining product four hour rolling average MSU after capping.IWP_ADJUSTED_PROD_MSU_4HRAIWP adjusted product four hour rolling average MSU.GSSP_PROD_MSU_4HRAGSSP product db2 four hour rolling average MSU after capping.GMT_OFFSETOffset GMT to local time, in seconds. Calculated as SMF70LGO/4096E6.IWP_DEFINED_MSUIWP defining product consumed MSU.CAPACITY_GRP_MSU_4HUTILRATotal LPAR MSU by group.DESCRIPTIONProduct standard name from IBM websie.MLCProduct MLC program. For MLC product, this is 1.IPLAProduct IPLA program. For IPLA program, this is 1.PRODUCT_CATEGORYProduct is MLC program or IPLA program, others are blank.Presentation ViewThe Presentation View includes shortcuts to the existing items in the Logical View.

Product MSU Utilization Metrics tables

The Product MSU Utilization Metrics table, z/OS® Product MSU Utilization Metrics table, and Product MSU Utilization Metrics without z/OS table all use the same structure.

The Product MSU Utilization Metrics tables contains the following columns.

DATE

Date of the mid measurement.

TIME

Time of the mid measurement.

CPC_MNFCTR

V1-CPC manufacturer. From SMF89MNF.

CPC_SERIAL_NO

Last four digits of the CPC serial number. From SMF70SER.

CPC_MODEL_NO

CPU model number. From SMF70MOD.

CPC_MODEL_ID

CPC model ID. From SMF70MDL.

MACHINE_SERIAL_NO

zOS machine Serial No.

LPAR_NAME

Name of the logical partition. From SMF70LPM.

MVS_SYSTEM_ID

MVS™ system ID.

PROD_NAME

Product name. From SMF89UPN or SMF89T2ProdName.

PROD_ID

Product ID number.

PROD_PARENT

Product Parent name.

SMF89_REGISTERED

Product register in SMF89 records. For SMF89 registered, this is 1.

ZOS_BASED

Product zOS Based program. For zOS Based product, this is 1.

REFERENCE_BASED

Product reference based program. For reference Based product, this is 1.

GSSP

Product GSSP program. For GSSP product, this is 1.

GSSP_SCALING_FACTOR

GSSP Product scaling factor. Default is 2.

GSSP_DB2_UTIL_MSU

GSSP DB2® MSU.

IWP

Product IWP program. For IWP program, this is 1.

IWP_ADJUSTED

Product IWP adjusted program. For IWP adjusted product, this is 1.

IWP_DEFINING

Product IWP defining program. For IWP defining product, this is 1.

EXECUTION_BASED

Product IPLA execution based program. For execution based product, this is 1.

PROD_VERSION

Product version. From SMF89UPV or SMF89T2ProdVers.

PROD_SHORT_VERSION

Product short version.

PROD_FEATURE_NM

Product qualifier. From SMF89UPQ or SMF89T2FeatureName.

PRODUCT_DISPLAY_VALUE

Combined product ID, product name and pricing structure.

PRICING_STRUCTURE

Product sub-capacity program.

PRODUCT_USE_VALUE

Combined product ID, product name and pricing structure.

PROD_CP_SECONDS

Product CPU time.

PROD_MSU_CONSUMED

Product consumed MSU.

PROD_MSU_1HAVG

Product consumed MSU hourly.

PROD_MSU_4HUTILRA

Product four hour rolling average MSU after capping.

LPAR_MSU_4HUTILRA

LPAR four hour rolling average MSU after capping.

HOUR_NO

Hour No.

UNIQUE_HOUR_NO

Unique hour No. from 1980-01-01 00:00:00

PROC_CAPACITY_MSU

Processor capacity available to MVS image, measured in MSUs. Calculated as the maximum of SMF70WLA.

LPAR_CONSUMED_MSU

LPAR consumed MSU.

CAPACITY_LIMIT_MSU

Defined capacity limit of the partition in MSUs per hour.

LPAR_MSU_4HRA

LPAR four hour rolling average MSU.

LPAR_MSU_1HTOT

LPAR consumed MSU hourly.

OTHER_PRODS_MSU_1HTOT

Other products consumed MSU hourly.

CPC_MSU_4HUTILRA

CPC four hour rolling average MSU after capping.

CAPACITY_GRP_NM

CAPACITY_GRP_NMacity group this partition belongs to. From SMF70GNM.

CAPACITY_GRP_LMT

Group maximum licensing units which may be consumed per unit of time, on average. From SMF70GMU.

CAPACITY_GRP_AVL

Long term average of CPU service in millions of service units which would be allowed by the limit of the capacity group but is not used by its members. If the value is negative, this capacity group is subject to capping. From SMF70GAU.

ZNALC_LICENSE

Indicates that LICENSE=zNALC was specified in EASYSxx. Possible values are Y or N. From Bit 1 of SMF89PFL.

NOMINAL_CAPACITY

Nominal processor capacity available to the central processor complex, measured in MSU/h. Calculated as the average of SMF70NCR.

IWP_DEFINING_PROD_MSU_4HUTILRA

IWP defining product four hour rolling average MSU after capping.

IWP_ADJUSTED_PROD_MSU_4HRA

IWP adjusted product four hour rolling average MSU.

GSSP_PROD_MSU_4HRA

GSSP product db2 four hour rolling average MSU after capping.

GMT_OFFSET

Offset GMT to local time, in seconds. Calculated as SMF70LGO/4096E6.

IWP_DEFINED_MSU

IWP defining product consumed MSU.

CAPACITY_GRP_MSU_4HUTILRA

Total LPAR MSU by group.

DESCRIPTION

Product standard name from IBM® websie.

MLC

Product MLC program. For MLC product, this is 1.

IPLA

Product IPLA program. For IPLA program, this is 1.

PRODUCT_CATEGORY

Product is MLC program or IPLA program, others are blank.

Parent topic: Presentation View

Prompts/CPC Prompt View tableFramework Manager tablesPrompts/CPC Prompt ViewThe Prompts/CPC Prompt View table contains the following columns.DATEDate of the mid measurement.TIMETime of the mid measurement.CPC_SERIAL_NOLast four digits of the CPC serial number. From SMF70SER.Presentation ViewThe Presentation View includes shortcuts to the existing items in the Logical View.

Prompts/CPC Prompt View table

The Prompts/CPC Prompt View table contains the following columns.

DATE

Date of the mid measurement.

TIME

Time of the mid measurement.

CPC_SERIAL_NO

Last four digits of the CPC serial number. From SMF70SER.

Parent topic: Presentation View

Prompts/LPAR MSU Prompt View tableFramework Manager tablesPrompts/LPAR MSU Prompt ViewThe Prompts/LPAR MSU Prompt View table contains the following columns.DATEDate of the mid measurement.TIMETime of the mid measurement.CPC_SERIAL_NOLast 4 digits of the CPC serial number. From SMF70SER.LPAR_NAMEName of the logical partition. From SMF70LPM.MVS_SYSTEM_IDMVS system ID.Presentation ViewThe Presentation View includes shortcuts to the existing items in the Logical View.

Prompts/LPAR MSU Prompt View table

The Prompts/LPAR MSU Prompt View table contains the following columns.

DATE

Date of the mid measurement.

TIME

Time of the mid measurement.

CPC_SERIAL_NO

Last 4 digits of the CPC serial number. From SMF70SER.

LPAR_NAME

Name of the logical partition. From SMF70LPM.

MVS_SYSTEM_ID

MVS™ system ID.

Parent topic: Presentation View

Prompts/Product Billable MSU Prompt View tableFramework Manager tablesPrompts/Product Billable MSU Prompt ViewThe Prompts/Product Billable MSU Prompt View table contains the following columns.DATEDate of the mid measurement.TIMETime of the mid measurement.CPC_SERIAL_NOLast four digits of the CPC serial number. From SMF70SER.LPAR_NAMEName of the logical partition. From SMF70LPM.MVS_SYSTEM_IDMVS system ID.PROD_NAMEProduct name. From SMF89UPN or SMF89T2ProdName.PROD_IDProduct ID number.PROD_PARENTProduct Parent name.PROD_VERSIONProduct version. From SMF89UPV or SMF89T2ProdVers.PROD_FEATURE_NMProduct qualifier. From SMF89UPQ or SMF89T2FeatureName.PRODUCT_USE_VALUECombined product ID, product name and pricing structure.Presentation ViewThe Presentation View includes shortcuts to the existing items in the Logical View.

Prompts/Product Billable MSU Prompt View table

The Prompts/Product Billable MSU Prompt View table contains the following columns.

DATE

Date of the mid measurement.

TIME

Time of the mid measurement.

CPC_SERIAL_NO

Last four digits of the CPC serial number. From SMF70SER.

LPAR_NAME

Name of the logical partition. From SMF70LPM.

MVS_SYSTEM_ID

MVS™ system ID.

PROD_NAME

Product name. From SMF89UPN or SMF89T2ProdName.

PROD_ID

Product ID number.

PROD_PARENT

Product Parent name.

PROD_VERSION

Product version. From SMF89UPV or SMF89T2ProdVers.

PROD_FEATURE_NM

Product qualifier. From SMF89UPQ or SMF89T2FeatureName.

PRODUCT_USE_VALUE

Combined product ID, product name and pricing structure.

Parent topic: Presentation View

Prompts/Product Prompt for LPAR MSU Report View tableFramework Manager tablesPrompts/Product Prompt for LPAR MSU Report ViewThe Prompts/Product Prompt for LPAR MSU Report View table contains the following columns.CPC_SERIAL_NOLast four digits of the CPC serial number. From SMF70SER.LPAR_NAMEName of the logical partition. From SMF70LPM.MVS_SYSTEM_IDMVS system ID.PRODUCT_USE_VALUECombined product ID, product name and pricing structure.Presentation ViewThe Presentation View includes shortcuts to the existing items in the Logical View.

Prompts/Product Prompt for LPAR MSU Report View table

The Prompts/Product Prompt for LPAR MSU Report View table contains the following columns.

CPC_SERIAL_NO

Last four digits of the CPC serial number. From SMF70SER.

LPAR_NAME

Name of the logical partition. From SMF70LPM.

MVS_SYSTEM_ID

MVS™ system ID.

PRODUCT_USE_VALUE

Combined product ID, product name and pricing structure.

Parent topic: Presentation View

Raw LPAR Product MSU Utilization Metrics tableFramework Manager tablesRaw LPAR Product MSU Utilization MetricsThe Raw LPAR Product MSU Utilization Metrics table contains the following columns.DATEDate of the mid measurement.TIMETime of the mid measurement.CPC_SERIAL_NOLast four digits of the CPC serial number. From SMF70SER.CPC_MODEL_NOCPU model number. From SMF70MOD.CPC_MODEL_IDCPC model ID. From SMF70MDL.LPAR_NAMEName of the logical partition. From SMF70LPM.MVS_SYSTEM_IDMVS system ID.PROD_NAMEProduct name. From SMF89UPN or SMF89T2ProdName. PROD_IDProduct ID number.PROD_VERSIONProduct version. From SMF89UPV or SMF89T2ProdVers.PRODUCT_DISPLAY_VALUECombined product ID, product name and pricing structure. PRODUCT_USE_VALUECombined product ID, product name and pricing structure.PROD_MSU_CONSUMEDProduct consumed MSU.HOUR_NOHour No.LPAR_CONSUMED_MSULPAR consumed MSU.LPAR_MSU_1HTOTLPAR consumed MSU hourly.GMT_OFFSETOffset GMT to local time, in seconds. Calculated as SMF70LGO/4096E6.PROD_OWNERProduct owner or vendor name. From SMF89UPO or SMF89T2ProdOwner.Presentation ViewThe Presentation View includes shortcuts to the existing items in the Logical View.

Raw LPAR Product MSU Utilization Metrics table

The Raw LPAR Product MSU Utilization Metrics table contains the following columns.

DATE

Date of the mid measurement.

TIME

Time of the mid measurement.

CPC_SERIAL_NO

Last four digits of the CPC serial number. From SMF70SER.

CPC_MODEL_NO

CPU model number. From SMF70MOD.

CPC_MODEL_ID

CPC model ID. From SMF70MDL.

LPAR_NAME

Name of the logical partition. From SMF70LPM.

MVS_SYSTEM_ID

MVS™ system ID.

PROD_NAME

Product name. From SMF89UPN or SMF89T2ProdName.

PROD_ID

Product ID number.

PROD_VERSION

Product version. From SMF89UPV or SMF89T2ProdVers.

PRODUCT_DISPLAY_VALUE

Combined product ID, product name and pricing structure.

PRODUCT_USE_VALUE

Combined product ID, product name and pricing structure.

PROD_MSU_CONSUMED

Product consumed MSU.

HOUR_NO

Hour No.

LPAR_CONSUMED_MSU

LPAR consumed MSU.

LPAR_MSU_1HTOT

LPAR consumed MSU hourly.

GMT_OFFSET

Offset GMT to local time, in seconds. Calculated as SMF70LGO/4096E6.

PROD_OWNER

Product owner or vendor name. From SMF89UPO or SMF89T2ProdOwner.

Parent topic: Presentation View

Sub-Capacity Program Pricing Structures tableFramework Manager tablesSub-Capacity Program Pricing StructuresThe Sub-Capacity Program Pricing Structures table contains the following columns.DATE Date of the mid measurement.PROD_ID Product ID number.PARENT_NAME Product Parent name.SMF89_REGTERED Product register in SMF89 records. For SMF89 registered, this is 1.MLC Product MLC program. For MLC product, this is 1.IPLA Product IPLA program. For IPLA program, this is 1.ZOS_BASED Product z/OS Based program. For z/OS based product, this is 1.REFERENCE_BASED Product reference based program. For reference based product, this is 1.GSSP Product GSSP program. For GSSP product, this is 1.GSSP_SCALING_FACTOR GSSP Product scaling factor. Default is 2.IWP_ADJUSTED Product IWP adjusted program. For IWP adjusted product, this is 1.IWP_DEFINING Product IWP defining program. For IWP defining product, this is 1.EXECUTION_BASED Product IPLA execution based program. For execution based product, this is 1.DESCRIPTION Product standard name from IBM website.IWP Product IWP program. For IWP program, this is 1.PRICING_STRUCTURE Product sub-capacity program.Presentation ViewThe Presentation View includes shortcuts to the existing items in the Logical View.

Sub-Capacity Program Pricing Structures table

The Sub-Capacity Program Pricing Structures table contains the following columns.

DATE

Date of the mid measurement.

PROD_ID

Product ID number.

PARENT_NAME

Product Parent name.

SMF89_REGTERED

Product register in SMF89 records. For SMF89 registered, this is 1.

MLC

Product MLC program. For MLC product, this is 1.

IPLA

Product IPLA program. For IPLA program, this is 1.

ZOS_BASED

Product z/OS® Based program. For z/OS based product, this is 1.

REFERENCE_BASED

Product reference based program. For reference based product, this is 1.

GSSP

Product GSSP program. For GSSP product, this is 1.

GSSP_SCALING_FACTOR

GSSP Product scaling factor. Default is 2.

IWP_ADJUSTED

Product IWP adjusted program. For IWP adjusted product, this is 1.

IWP_DEFINING

Product IWP defining program. For IWP defining product, this is 1.

EXECUTION_BASED

Product IPLA execution based program. For execution based product, this is 1.

DESCRIPTION

Product standard name from IBM® website.

IWP

Product IWP program. For IWP program, this is 1.

PRICING_STRUCTURE

Product sub-capacity program.

Parent topic: Presentation View

Timezone Metrics tableFramework Manager tablesTimezone MetricsThe Timezone Metrics table contains the following columns.TIMEZONE_NAMETimezone name.DescriptionTimezone description.GMT_OFFSETOffset GMT to local time, in seconds.FLAGFrequently-used timezone, default is 1.Presentation ViewThe Presentation View includes shortcuts to the existing items in the Logical View.

Timezone Metrics table

The Timezone Metrics table contains the following columns.

TIMEZONE_NAME

Timezone name.

Description

Timezone description.

GMT_OFFSET

Offset GMT to local time, in seconds.

FLAG

Frequently-used timezone, default is 1.

Parent topic: Presentation View

Logical ViewThe Logical View organizes elements into logical query subjects.The Logical View contains some Query Subjects.All relationships should be defined in the Logical View.Capacity Management Analytics InstanceThe IBM Capacity Management Analytics Instance is the high-level container for Capacity Management Analytics which holds the four model layers.

Logical View

The Logical View organizes elements into logical query subjects.

The Logical View contains some Query Subjects.

All relationships should be defined in the Logical View.

Parent topic: Capacity Management Analytics Instance

Physical ViewThe physical view, using SQL (“select * from TableName”) to retrieve table information, reflects the underlying tables and join relationships in the data source.CMA DWThe CMA DW namespace holds folders for the Tivoli Decision Support for z/OS (TDSz) schema and the IBM Capacity Management Analytics schema.DRLDRL is the folder for all the TDSz components. Within this folder all the TDSz component tables are grouped by their prefix. For example, all DB2_ tables are found in the DB2 folder.Contents of the DRL folderContents of the DRL folder, showing tables with the DB2 prefix.Tables within a folder are further grouped by their name. For example, the DB2_APPLICATIONS folder is within the DB2 folder, and it holds all DB2_APPLICATIONS tables.Refer to the TDSz documentation to find the table you need for your specific reporting needs. Especially, the SMF_113 folder that is provided with TDSz with the z/OS System (MVS) Component. This folder supports the z13 SMF type 113 records.DRLSYSThe DRLSYS folder contains the catalog tables specifically for TDSz. These tables are used to do reporting on TDSz itself.Refer to the TDSz documentation to find the table you need for your specific reporting needs.HCMThe HCM folder holds all the Capacity Management Analytics specific tables.FORECASTSHolds all forecast results. These results are used to do MIPS forecast reporting. All the forecast results come from the following forecast streams: hourly, daily and monthly.HCM.MVSPM_LPARA consolidated version of the DRL.MVSPM_LPAR_H table, which eliminates any duplicate entries from the DRL.MVSPM_LPAR_H table. HCM.MIPS_CAPACITYHolds the CPU_SERIAL_MIPS rating for your processorsHCM.DATE_BANDBased on interval band, this table returns the first day of every date period.HCM.TIME_BANDBased on interval band, this table returns the first minute of every time period.HCM.QUARTER_BANDReturns the first day of every season.HCM.MONTH_BANDReturns the first day of every month.HCM.YEAR_BANDReturns the first day of every year.HCM.GMT_TIME Returns the time , depending on the format of time expression, that results from adding integer expression seconds to time expression.HCM.GMT_DATE Returns the DATE, depending on the format of date and time expression, that results from adding integer expression seconds to date expression.HCM.GMT_TIMESTAMP Returns the TIMESTAMP, depending on the format of timestamp expression, that results from adding integer expression seconds to timestamp expression.HCM.ROUNDOFF Round off MSU value. For example, ROUNDOFF (5.6) = 5, ROUNDOFF (0.5)=1, ROUNDOFF (4.99999999) = 5.HCM.UTILMSU For LPAR capping, compare LPAR MSU with defined capacity or processor capacity, and return minimum value.HCM.UTIL_GRP_MSU For sysplex group capping, compare total LPAR MSU values with group capacity limit, and return minimum value.HCM.PRICE_PER_MSU_DETERMINE Returns pricing per MSU by CPC Serial No, Product ID, and Product MSU.HCM.TIER_DETERMINE Returns pricing tier by CPC Serial No, Product ID, and Product MSU.HCM.TIER_MAX_MSU Returns maximum MSU of this pricing tier by CPC Serial No, Product ID, and Product MSU.HCM.TIER_MIN_MSReturns minimum MSU of this pricing tier by CPC Serial No, Product ID, and Product MSU.HCM.TIER_PRICE Returns price of this pricing tier by CPC Serial No, Product ID, and Product MSUHCM.TIER_VU Returns Interim VU calculation result by CPC Serial No, Product ID, and Product MSU.HCM.TIER_VU_PER_MSU Returns entitled MSUs by CPC SERIAL NO, Product ID, and Product MSU.HCM.GSSP_MSU Using product 4 hours rolling average MSU value, and LPAR four hours rolling average MSU value, returns GSSP product billable MSU.HCM.IWP_ADJUSTED_MSU Returns IWP adjusted product billable of one LPAR.Capacity Management Analytics InstanceThe IBM Capacity Management Analytics Instance is the high-level container for Capacity Management Analytics which holds the four model layers.

Physical View

The physical view, using SQL (“select * from TableName”) to retrieve table information, reflects the underlying tables and join relationships in the data source.

CMA DW

The CMA DW namespace holds folders for the Tivoli® Decision Support for z/OS® (TDSz) schema and the IBM® Capacity Management Analytics schema.

DRL

DRL is the folder for all the TDSz components. Within this folder all the TDSz component tables are grouped by their prefix. For example, all DB2_ tables are found in the DB2® folder.

Figure 20. Contents of the DRL folder

Tables within a folder are further grouped by their name. For example, the DB2_APPLICATIONS folder is within the DB2 folder, and it holds all DB2_APPLICATIONS tables.

Refer to the TDSz documentation to find the table you need for your specific reporting needs. Especially, the SMF_113 folder that is provided with TDSz with the z/OS System (MVS) Component. This folder supports the z13 SMF type 113 records.

DRLSYS

The DRLSYS folder contains the catalog tables specifically for TDSz. These tables are used to do reporting on TDSz itself.

Refer to the TDSz documentation to find the table you need for your specific reporting needs.

HCM

The HCM folder holds all the Capacity Management Analytics specific tables.

FORECASTS

Holds all forecast results. These results are used to do MIPS forecast reporting. All the forecast results come from the following forecast streams: hourly, daily and monthly.

HCM.MVSPM_LPAR

A consolidated version of the DRL.MVSPM_LPAR_H table, which eliminates any duplicate entries from the DRL.MVSPM_LPAR_H table.

HCM.MIPS_CAPACITY

Holds the CPU_SERIAL_MIPS rating for your processors

HCM.DATE_BAND

Based on interval band, this table returns the first day of every date period.

HCM.TIME_BAND

Based on interval band, this table returns the first minute of every time period.

HCM.QUARTER_BAND

Returns the first day of every season.

HCM.MONTH_BAND

Returns the first day of every month.

HCM.YEAR_BAND

Returns the first day of every year.

HCM.GMT_TIME

Returns the time , depending on the format of time expression, that results from adding integer expression seconds to time expression.

HCM.GMT_DATE

Returns the DATE, depending on the format of date and time expression, that results from adding integer expression seconds to date expression.

HCM.GMT_TIMESTAMP

Returns the TIMESTAMP, depending on the format of timestamp expression, that results from adding integer expression seconds to timestamp expression.

HCM.ROUNDOFF

Round off MSU value. For example, ROUNDOFF (5.6) = 5, ROUNDOFF (0.5)=1, ROUNDOFF (4.99999999) = 5.

HCM.UTILMSU

For LPAR capping, compare LPAR MSU with defined capacity or processor capacity, and return minimum value.

HCM.UTIL_GRP_MSU

For sysplex group capping, compare total LPAR MSU values with group capacity limit, and return minimum value.

HCM.PRICE_PER_MSU_DETERMINE

Returns pricing per MSU by CPC Serial No, Product ID, and Product MSU.

HCM.TIER_DETERMINE

Returns pricing tier by CPC Serial No, Product ID, and Product MSU.

HCM.TIER_MAX_MSU

Returns maximum MSU of this pricing tier by CPC Serial No, Product ID, and Product MSU.

HCM.TIER_MIN_MS

Returns minimum MSU of this pricing tier by CPC Serial No, Product ID, and Product MSU.

HCM.TIER_PRICE

Returns price of this pricing tier by CPC Serial No, Product ID, and Product MSU

HCM.TIER_VU

Returns Interim VU calculation result by CPC Serial No, Product ID, and Product MSU.

HCM.TIER_VU_PER_MSU

Returns entitled MSUs by CPC SERIAL NO, Product ID, and Product MSU.

HCM.GSSP_MSU

Using product 4 hours rolling average MSU value, and LPAR four hours rolling average MSU value, returns GSSP product billable MSU.

HCM.IWP_ADJUSTED_MSU

Returns IWP adjusted product billable of one LPAR.

Parent topic: Capacity Management Analytics Instance

Opening the Capacity Management Analytics Model in Framework ManagerYou can view the IBM Capacity Management Analytics model in IBM Cognos Framework Manager. The Framework Manager project file (.cpf) for the Capacity Management Analytics model is included in the Capacity Management Analytics installation.Copy the entire contents of the C:\Program Files (x86)\IBM\Capacity Management Analytics 1.2\FM_model directory from the computer where you installed Capacity Management Analytics to the computer where you have IBM Cognos Framework Manager installed.Following deleted for CMA 1.1 FP1.CMA is a Compatible Query Mode model that is published in Dynamic Query Mode. Refer to your database driver manager instructions to catalog a database alias for the Compatible Query Mode connection in Framework Manager.Launch IBM Cognos Framework Manager.From the Welcome page, click Open a project.Navigate to the project folder and click the CMA.cpf file.Click Open.Framework Manager model referenceThe IBM Cognos Framework Manager models used in IBM Capacity Management Analytics consists of several namespace/folder hierarchies that group tables and objects into a logically identifiable way within IBM Cognos Framework Manager.

Opening the Capacity Management Analytics Model in Framework Manager

You can view the IBM® Capacity Management Analytics model in IBM Cognos® Framework Manager. The Framework Manager project file (.cpf) for the Capacity Management Analytics model is included in the Capacity Management Analytics installation.

Before you begin

Copy the entire contents of the C:\Program Files (x86)\IBM\Capacity Management Analytics 1.2\FM_model directory from the computer where you installed Capacity Management Analytics to the computer where you have IBM Cognos Framework Manager installed.

Procedure

Launch IBM Cognos Framework Manager.

From the Welcome page, click Open a project.

Navigate to the project folder and click the CMA.cpf file.

Click Open.

Parent topic: Framework Manager model reference

Enabling a Capacity Management Analytics package with Dynamic Query Mode To optimize performance, configure your IBM Capacity Management Analytics package to use Dynamic Query Mode (DQM). Framework Manager model referenceThe IBM Cognos Framework Manager models used in IBM Capacity Management Analytics consists of several namespace/folder hierarchies that group tables and objects into a logically identifiable way within IBM Cognos Framework Manager. Testing query subjects against Dynamic Query ModeTest your query subjects to confirm that they run correctly using Dynamic Query Mode (DQM). If you modify any reports in your model, you should confirm that all queries work correctly with DQM.Publishing a package with Dynamic Query Mode supportOnce a package has been modified in any way, you must explicitly publish the package to make changes available to users.Verifying the query mode used by a published packageAfter you publish a package, you should verify that the package uses Dynamic Query Mode.

Enabling a Capacity Management Analytics package with Dynamic Query Mode

To optimize performance, configure your IBM® Capacity Management Analytics package to use Dynamic Query Mode (DQM).

Testing query subjects against Dynamic Query Mode
Test your query subjects to confirm that they run correctly using Dynamic Query Mode (DQM). If you modify any reports in your model, you should confirm that all queries work correctly with DQM.

Publishing a package with Dynamic Query Mode support
Once a package has been modified in any way, you must explicitly publish the package to make changes available to users.

Verifying the query mode used by a published package
After you publish a package, you should verify that the package uses Dynamic Query Mode.

Parent topic: Framework Manager model reference

Testing query subjects against Dynamic Query ModeTest your query subjects to confirm that they run correctly using Dynamic Query Mode (DQM). If you modify any reports in your model, you should confirm that all queries work correctly with DQM.In the Project Viewer, expand the IBM Capacity Management Analytics model.Right-click any shortcut under Presentation View, then click Go to target.Right-click the target query subject, then click Test.Click Test Sample.Click Close.Enabling a Capacity Management Analytics package with Dynamic Query ModeTo optimize performance, configure your IBM Capacity Management Analytics package to use Dynamic Query Mode (DQM).

Testing query subjects against Dynamic Query Mode

Test your query subjects to confirm that they run correctly using Dynamic Query Mode (DQM). If you modify any reports in your model, you should confirm that all queries work correctly with DQM.

Procedure

In the Project Viewer, expand the IBM Capacity Management Analytics model.

Right-click any shortcut under Presentation View, then click Go to target.

Right-click the target query subject, then click Test.

Click Test Sample.

Click Close.

Parent topic: Enabling a Capacity Management Analytics package with Dynamic Query Mode

Publishing a package with Dynamic Query Mode supportOnce a package has been modified in any way, you must explicitly publish the package to make changes available to users.In the Project Viewer, right-click Packages, then click Create, Package.In the Create Package dialog box, enter a Name for the package you are publishing.Optionally, enter a Description and Screen Tip for the package.Click Next.Select the metadata you want to include in your published package.Click Finish.Click Yes when prompted to open the Publish Package wizard.In the Publish Wizard – Select Location Type window, click IBM Cognos 10 Content Store.Specify the folder to which you want to publish the package. For example Public Folders\Customization\Models.Click Next.Click Next again.Click Publish.Click Finish.Enabling a Capacity Management Analytics package with Dynamic Query ModeTo optimize performance, configure your IBM Capacity Management Analytics package to use Dynamic Query Mode (DQM).

Publishing a package with Dynamic Query Mode support

Once a package has been modified in any way, you must explicitly publish the package to make changes available to users.

Procedure

In the Project Viewer, right-click Packages, then click Create, Package.

In the Create Package dialog box, enter a Name for the package you are publishing.

Optionally, enter a Description and Screen Tip for the package.

Click Next.

Select the metadata you want to include in your published package.

Click Finish.

Click Yes when prompted to open the Publish Package wizard.

In the Publish Wizard – Select Location Type window, click IBM Cognos 10 Content Store.

Specify the folder to which you want to publish the package. For example Public Folders\Customization\Models.

Click Next.

Click Next again.

Click Publish.

Click Finish.

Parent topic: Enabling a Capacity Management Analytics package with Dynamic Query Mode

Verifying the query mode used by a published packageAfter you publish a package, you should verify that the package uses Dynamic Query Mode.In IBM Cognos Connection, open the folder where your published package resides.On the Actions column, click Set Properties.On the General properties tab, confirm that the Query Mode is set to Dynamic.Enabling a Capacity Management Analytics package with Dynamic Query ModeTo optimize performance, configure your IBM Capacity Management Analytics package to use Dynamic Query Mode (DQM).

Verifying the query mode used by a published package

After you publish a package, you should verify that the package uses Dynamic Query Mode.

Procedure

In IBM® Cognos® Connection, open the folder where your published package resides.

On the Actions column, click Set Properties.

On the General properties tab, confirm that the Query Mode is set to Dynamic.

Parent topic: Enabling a Capacity Management Analytics package with Dynamic Query Mode

Report and workspace referenceIBM Capacity Management Analytics provides several predefined reports and workspaces that let you monitor and analyze the capacity of your IT services and the IT infrastructure. Opening a report or workspaceYou can access all IBM Capacity Management Analytics reports and workspaces through IBM Cognos Connection.Common report and workspace featuresThe IBM Capacity Management Analytics reporting application provides consistent features across all reports and workspaces.Anomaly Detection Analytics: CICS Anomaly DetailsThis report shows the abnormal transactions that were detected by the CICS anomaly detection streams. This report shows a summary of the abnormal transaction counts and the detail transactions that are determined to be abnormal over a short period.Application Analytics: Distributed CPU Usage - Application Summary ReportThis report shows the 90% CPU busy line for applications. It can be displayed as hourly, daily, weekly, monthly, or yearly.Application Analytics: Distributed CPU Usage - Application ReportThis report shows the 90% CPU busy line for applications. It can be displayed as hourly, daily, weekly, monthly, or yearly.Application Analytics: MIPS Used - ApplicationThis report shows the average MIPS used by each application or line-of-business grouping across all CPCs and LPARs. The average can be displayed as hourly, daily, weekly, monthly, or yearly.Application Analytics: MIPS Used - Application by FunctionThis report shows the average or peak MIPS used by the selected application or line-of-business grouping across all CPCs and LPARs, according to function. The value can be displayed as hourly, daily, weekly, monthly, or yearly. This report also includes a forecast option.Application Analytics: MIPS Used - Application by LPARThis report shows the average MIPS used by all applications or line-of-business groupings on the selected LPAR. The average can be displayed as hourly, weekly, monthly, or yearly.Application Analytics: MIPS Used - Report Class by Job NameThis report shows the average MIPS used for a detailed list of the job names that were mapped with Workload Manager to a Report Class. The average can be displayed as hourly, daily, weekly, monthly, or yearly.CPU: AIX - CPU UsageThis report shows the percentage of CPU used from both a virtual and physical perspective.CPU: Latent DemandLatent demand represents the work that is waiting to be dispatched on a CP where all of the other CPs are currently busy. This report shows you which systems are overloaded and, as a result, could potentially slow down your high-priority applications. The report allows you to be proactive and reroute this work to less-utilized systems for faster processing.CPU: Linux for System x - CPU UsageThis report shows the percentage of CPU used from both a virtual and physical perspective.CPU: Linux for System z - CPU UsageThis report shows the percentage of CPU used from both a virtual and physical perspective. CPU: LPAR Weight Optimization Run ResultThis report shows the available LPAR Weight Optimization runs. It also shows the input settings and optimized weight results for a run selected for each CPC and processor type.CPU: MIPS Used - Service Class Period Level This report provides a comprehensive view of all of the applications that are running across the entire systems landscape. This helps you quickly rebalance system allocation and to focus on the most critical applications.CPU: MIPS Used - System Level (Captured vs Uncaptured)This report provides insights into potential MIPS overages. This can help you to determine the need to tune and adjust your system processes and avoid overcapacity by allocating MIPS back to business applications. Aside from the potential cost savings, a regular assessment, based on this report, can help you maintain your SLA by keeping up the performance and availability level.CPU: MIPS Used - zServer/LPAR LevelThis report shows the observed average or peak CPU MIPS used at the zServer/CPC level and the forecasted average or peak CPU MIPS used along with their confidence interval. The average CPU MIPS used (observed and forecasted) and peak CPU MIPS used (observed and forecasted) can be displayed for each processor type (CP, zIIP, zAAP, IFL, or ICF) at each interval band and is broken down to the LPAR level. The forecasted peak CPU MIPS used can be displayed for each processor type at each interval band on the CPC level.CPU: MIPS Used - LPAR Level by WLM ImportanceThis report shows the observed average or peak CPU MIPS used at the LPAR level and the forecasted average or peak with their confidence intervals. The CPU MIPS used (both observed and forecasted) can be displayed for each processor type at each interval band. The observed CPU MIPS used is also broken down by the WLM importance level. The optimized LPAR weight and the target workload, according to the settings in the selected optimization run, can be also displayed.CPU: Over/Under Share Weight - CPC by LPARThis report shows the stacked usage of each LPAR against the total number of engines of each type in the shared pool of a CPC. It also shows the usage percentage over or under its guaranteed share of each LPAR, according to both the observed and optimized weights, due to the competition among LPARs. The target workload, according to the selected optimization run, can also be displayed.CPU: Windows - CPU UsageThis report shows the percentage of CPU used from both a virtual and physical perspective.Memory: AIX - Memory UsageThis report shows the free physical memory, free virtual memory, and active virtual memory on the system. The report also displays metric showing the turnover of physical memory, such as Page-In/sec and Page-Out/sec (file system related pages), as well as PageSpace-In/sec and PageSpace-Out/sec.Memory: CSA/ECSA/SQA/ESQA Utilization This report provides information on detailed memory utilization to help you decide whether you need to adjust and rebalance memory allocations to prevent costly system outages.Memory: Linux for System x - Memory UsageThis report shows the installed (visible) memory, free physical memory, and free virtual memory on the server.Memory: Linux for System z - Memory UsageThis report shows the installed (visible) memory, free physical memory, and free virtual memory on the server.Memory: Windows - Memory UsageThis report shows how much memory is installed and the amount of free memory that is available for the server.SCA: LPAR MSU UtilizationThis report helps you understand and forecast your costs based on your MSU usage. You can compare your historical MSU usage with the forecasted usage to see upcoming trends and take proactive action to adjust your workload and capacity limits to help control your bill.SCA: NO89 Products MatrixThis report provides a single place to review all of the NO89 products that use sub-capacity pricing. It also shows where these products are designated to be run (both the CPCs and the LPARs). It shows all of the locations where a product is running along with all of the other NO89 products to provide a planning resource for LPAR and CPC consolidations. From this report, you can also drill to the NO89 Product MSU and Price report for any product.SCA: NO89 Product MSU and PriceThis report helps you understand and forecast your costs based on your MSU usage. It also shows you the monetary cost of your MSU for each NO89 product. You can compare the historical MSU usage with the forecasted usage, see upcoming trends, and take proactive measures to adjust your workload and capacity limits to help control your costs.SCA: Product MSU and PriceThis report shows a registered IBM product's billable MSUs and monetary cost from a single CPC perspective for each scenario: Observed, Forecasted, and Optimized.SCA: Registered Products MatrixThis report provides a listing of LPARs and CPCs where the SCA registered products are running.WLM: Delays by Importance LevelThis report shows you the effect of delays for each WLM importance level that is running on the system. Delaying high importance workloads can result in missed Service Level Agreements (SLAs) which has a higher cost than delaying less important workloads. It is important to monitor these delays so that you can take timely action to control your costs and maintain your SLAs.WLM: Delays by Service Class PeriodThis report shows where delays are having a negative impact on performance so that you can take corrective action. Monitoring delays is important because delays can cause you to miss SLAs and incur a penalty.WLM: Performance IndexesThis report helps you align goal achievement metrics with overall business performance measurements, such as the cost associated with system performance (for example, adequate response times). zIIP/zAAP What-ifs - LPAR Level workspaceThis workspace puts analytical power at your fingertips. Scenario modeling lets you decide how to allocate processing costs to the zIIP/zAAP engines where it is most efficient and cost effective. Operational improvements that are based on the best scenario can drive significant cost reductions and savings.Enterprise Dashboard workspaceThe Enterprise Dashboard workspace brings together information for all of your servers across the enterprise to provide an overview of your inventory and utilization.Software Cost Analysis Summary workspaceManaging System z capacity can be costly and complex. This workspace allows you to better manage your software costs on System z by providing graphs and tables that show both the details and the summary of the software costs.zServer Monitoring Dashboard workspaceThis workspace provides a 360-degree view of the zServer system environment. Through timely identification of capacity bottlenecks and potential performance issues, IT can take steps to eliminate deficiencies and optimize provisioning for critical business functions. The benefits of using this dashboard include growing revenue by meeting business goals and SLAs for constant availability and scalability. It also helps with cost savings and cost avoidance of unnecessary software licenses. There are also efficiency and productivity gains from the reduction of administrative efforts. Product VersionThe Product Version report displays the IBM Capacity Management Analytics copyright, version, and build information.

Report and workspace reference

IBM® Capacity Management Analytics provides several predefined reports and workspaces that let you monitor and analyze the capacity of your IT services and the IT infrastructure.

Opening a report or workspace
You can access all IBM Capacity Management Analytics reports and workspaces through IBM Cognos® Connection.

Common report and workspace features
The IBM Capacity Management Analytics reporting application provides consistent features across all reports and workspaces.

Anomaly Detection Analytics: CICS Anomaly Details
This report shows the abnormal transactions that were detected by the CICS anomaly detection streams. This report shows a summary of the abnormal transaction counts and the detail transactions that are determined to be abnormal over a short period.

Application Analytics: Distributed CPU Usage - Application Summary Report
This report shows the 90% CPU busy line for applications. It can be displayed as hourly, daily, weekly, monthly, or yearly.

Application Analytics: Distributed CPU Usage - Application Report
This report shows the 90% CPU busy line for applications. It can be displayed as hourly, daily, weekly, monthly, or yearly.

Application Analytics: MIPS Used - Application
This report shows the average MIPS used by each application or line-of-business grouping across all CPCs and LPARs. The average can be displayed as hourly, daily, weekly, monthly, or yearly.

Application Analytics: MIPS Used - Application by Function
This report shows the average or peak MIPS used by the selected application or line-of-business grouping across all CPCs and LPARs, according to function. The value can be displayed as hourly, daily, weekly, monthly, or yearly. This report also includes a forecast option.

Application Analytics: MIPS Used - Application by LPAR
This report shows the average MIPS used by all applications or line-of-business groupings on the selected LPAR. The average can be displayed as hourly, weekly, monthly, or yearly.

Application Analytics: MIPS Used - Report Class by Job Name
This report shows the average MIPS used for a detailed list of the job names that were mapped with Workload Manager to a Report Class. The average can be displayed as hourly, daily, weekly, monthly, or yearly.

CPU: AIX - CPU Usage
This report shows the percentage of CPU used from both a virtual and physical perspective.

CPU: Latent Demand
Latent demand represents the work that is waiting to be dispatched on a CP where all of the other CPs are currently busy. This report shows you which systems are overloaded and, as a result, could potentially slow down your high-priority applications. The report allows you to be proactive and reroute this work to less-utilized systems for faster processing.

CPU: Linux for System x - CPU Usage
This report shows the percentage of CPU used from both a virtual and physical perspective.

CPU: Linux for System z - CPU Usage
This report shows the percentage of CPU used from both a virtual and physical perspective.

CPU: LPAR Weight Optimization Run Result
This report shows the available LPAR Weight Optimization runs. It also shows the input settings and optimized weight results for a run selected for each CPC and processor type.

CPU: MIPS Used - Service Class Period Level
This report provides a comprehensive view of all of the applications that are running across the entire systems landscape. This helps you quickly rebalance system allocation and to focus on the most critical applications.

CPU: MIPS Used - System Level (Captured vs Uncaptured)
This report provides insights into potential MIPS overages. This can help you to determine the need to tune and adjust your system processes and avoid overcapacity by allocating MIPS back to business applications. Aside from the potential cost savings, a regular assessment, based on this report, can help you maintain your SLA by keeping up the performance and availability level.

CPU: MIPS Used - zServer/LPAR Level
This report shows the observed average or peak CPU MIPS used at the zServer/CPC level and the forecasted average or peak CPU MIPS used along with their confidence interval. The average CPU MIPS used (observed and forecasted) and peak CPU MIPS used (observed and forecasted) can be displayed for each processor type (CP, zIIP, zAAP, IFL, or ICF) at each interval band and is broken down to the LPAR level. The forecasted peak CPU MIPS used can be displayed for each processor type at each interval band on the CPC level.

CPU: MIPS Used - LPAR Level by WLM Importance
This report shows the observed average or peak CPU MIPS used at the LPAR level and the forecasted average or peak with their confidence intervals. The CPU MIPS used (both observed and forecasted) can be displayed for each processor type at each interval band. The observed CPU MIPS used is also broken down by the WLM importance level. The optimized LPAR weight and the target workload, according to the settings in the selected optimization run, can be also displayed.

CPU: Over/Under Share Weight - CPC by LPAR
This report shows the stacked usage of each LPAR against the total number of engines of each type in the shared pool of a CPC. It also shows the usage percentage over or under its guaranteed share of each LPAR, according to both the observed and optimized weights, due to the competition among LPARs. The target workload, according to the selected optimization run, can also be displayed.

CPU: Windows - CPU Usage
This report shows the percentage of CPU used from both a virtual and physical perspective.

Memory: AIX - Memory Usage
This report shows the free physical memory, free virtual memory, and active virtual memory on the system. The report also displays metric showing the turnover of physical memory, such as Page-In/sec and Page-Out/sec (file system related pages), as well as PageSpace-In/sec and PageSpace-Out/sec.

Memory: CSA/ECSA/SQA/ESQA Utilization
This report provides information on detailed memory utilization to help you decide whether you need to adjust and rebalance memory allocations to prevent costly system outages.

Memory: Linux for System x - Memory Usage
This report shows the installed (visible) memory, free physical memory, and free virtual memory on the server.

Memory: Linux for System z - Memory Usage
This report shows the installed (visible) memory, free physical memory, and free virtual memory on the server.

Memory: Windows - Memory Usage
This report shows how much memory is installed and the amount of free memory that is available for the server.

SCA: LPAR MSU Utilization
This report helps you understand and forecast your costs based on your MSU usage. You can compare your historical MSU usage with the forecasted usage to see upcoming trends and take proactive action to adjust your workload and capacity limits to help control your bill.

SCA: NO89 Products Matrix
This report provides a single place to review all of the NO89 products that use sub-capacity pricing. It also shows where these products are designated to be run (both the CPCs and the LPARs). It shows all of the locations where a product is running along with all of the other NO89 products to provide a planning resource for LPAR and CPC consolidations. From this report, you can also drill to the NO89 Product MSU and Price report for any product.

SCA: NO89 Product MSU and Price
This report helps you understand and forecast your costs based on your MSU usage. It also shows you the monetary cost of your MSU for each NO89 product. You can compare the historical MSU usage with the forecasted usage, see upcoming trends, and take proactive measures to adjust your workload and capacity limits to help control your costs.

SCA: Product MSU and Price
This report shows a registered IBM product's billable MSUs and monetary cost from a single CPC perspective for each scenario: Observed, Forecasted, and Optimized.

SCA: Registered Products Matrix
This report provides a listing of LPARs and CPCs where the SCA registered products are running.

WLM: Delays by Importance Level
This report shows you the effect of delays for each WLM importance level that is running on the system. Delaying high importance workloads can result in missed Service Level Agreements (SLAs) which has a higher cost than delaying less important workloads. It is important to monitor these delays so that you can take timely action to control your costs and maintain your SLAs.

WLM: Delays by Service Class Period
This report shows where delays are having a negative impact on performance so that you can take corrective action. Monitoring delays is important because delays can cause you to miss SLAs and incur a penalty.

WLM: Performance Indexes
This report helps you align goal achievement metrics with overall business performance measurements, such as the cost associated with system performance (for example, adequate response times).

zIIP/zAAP What-ifs - LPAR Level workspace
This workspace puts analytical power at your fingertips. Scenario modeling lets you decide how to allocate processing costs to the zIIP/zAAP engines where it is most efficient and cost effective. Operational improvements that are based on the best scenario can drive significant cost reductions and savings.

Enterprise Dashboard workspace
The Enterprise Dashboard workspace brings together information for all of your servers across the enterprise to provide an overview of your inventory and utilization.

Software Cost Analysis Summary workspace
Managing System z® capacity can be costly and complex. This workspace allows you to better manage your software costs on System z by providing graphs and tables that show both the details and the summary of the software costs.

zServer Monitoring Dashboard workspace
This workspace provides a 360-degree view of the zServer system environment. Through timely identification of capacity bottlenecks and potential performance issues, IT can take steps to eliminate deficiencies and optimize provisioning for critical business functions. The benefits of using this dashboard include growing revenue by meeting business goals and SLAs for constant availability and scalability. It also helps with cost savings and cost avoidance of unnecessary software licenses. There are also efficiency and productivity gains from the reduction of administrative efforts.

Product Version
The Product Version report displays the IBM Capacity Management Analytics copyright, version, and build information.

Opening a report or workspaceYou can access all IBM Capacity Management Analytics reports and workspaces through IBM Cognos Connection.reportsopeningworkspacesopeningOpen IBM Cognos Connection.Click the Public Folders tab.Click IBM Capacity Management Analytics Solution Kit.Click Reports.Click All to view links to all available reports, or Workspaces to view links to all available workspaces. Click Anomaly Detection Analytics, Application Analytics, CPU, Memory, Software Cost Analysis, or WLM to view links to the reports available in any of those categories. Click the title of the report or workspace that you want to view. The report or workspace opens in IBM Cognos Viewer. For details on using Cognos Viewer, see IBM Knowledge Center (www.ibm.com/support/knowledgecenter/SSEP7J/welcome). Report and workspace referenceIBM Capacity Management Analytics provides several predefined reports and workspaces that let you monitor and analyze the capacity of your IT services and the IT infrastructure.

Opening a report or workspace

You can access all IBM® Capacity Management Analytics reports and workspaces through IBM Cognos® Connection.

Procedure

Open IBM Cognos Connection.

Click the Public Folders tab.

Click IBM Capacity Management Analytics Solution Kit.

Click Reports.

Click All to view links to all available reports, or Workspaces to view links to all available workspaces.

Optional: Click Anomaly Detection Analytics, Application Analytics, CPU, Memory, Software Cost Analysis, or WLM to view links to the reports available in any of those categories.

Click the title of the report or workspace that you want to view.

The report or workspace opens in IBM Cognos Viewer. For details on using Cognos Viewer, see IBM Knowledge Center (www.ibm.com/support/knowledgecenter/SSEP7J/welcome).

Parent topic: Report and workspace reference

Common report and workspace featuresThe IBM Capacity Management Analytics reporting application provides consistent features across all reports and workspaces.common report featuresreportscommon elementscommon workspace featuresworkspacescommon elementsUse this sample report to become familiar with the elements that are common to all of the Capacity Management Analytics reports and workspaces.Sample Capacity Management Analytics reportA sample Capacity Management Analytics report that shows the common report features.Drill toContains a list of related reports that a user can drill through to for additional information. Drill to capabilities are also provided in some reports by clicking a section in the report body. Note: When you drill through to related reports, the prompt criteria is automatically populated.Standard prompts date rangeThe date range of the data to be analyzed.time rangeThe time range within each day of the selected date range to be analyzed.interval bandThe level to which you want to aggregate.By changing the interval band, a single report can aggregate to different levels, such as 30 minutes or 30 days. You must view the data at an interval that is a multiple of the recording interval. For example, if your recording interval is 10 minutes, you can view data at intervals of 10, 20, or 30 minutes, but not at an interval of 5 minutes.showThe report can be displayed in different formats.chartInformation is displayed in a graphical format. tableInformation is displayed in a table.bothInformation is displayed in both a chart and table. Report specific promptsPrompt criteria that is specific to the displayed report. In the sample report, the report specific prompts are cpu serial no, processor type, and lpar name.The values that are populated in the report-specific prompt boxes are generated dynamically by queries to the IBM Tivoli Decision Support for z/OS or Capacity Management Analytics data stores. For example, if a new CEC (cpu serial no), processor type, or LPAR is added to the environment, there are no additional tables or lists need to be updated by the user to reflect the new items.refresh viewThe refresh view buttons populate the next prompt box and generate the report. Workspace prompt and refresh buttons To avoid refreshing charts unnecessarily, next > buttons are available for each prompt value in the workspace prompt area. After you select a prompt value, you must click next > to refresh the next prompt. Other prompts or workspace charts and tables are not refreshed until the last prompt is selected. The next > buttons are only for the workspace, and not for the report. You must set each prompt for them to take effect. You must also set them in sequence and click next > to submit the setting. On the last prompt, click refresh workspace to refresh all of the charts and tables in the workspace. Each workspace can vary according to their business needs. Workspace prompt and refresh buttonsWorkspace prompt and refresh buttons Report and workspace referenceIBM Capacity Management Analytics provides several predefined reports and workspaces that let you monitor and analyze the capacity of your IT services and the IT infrastructure. Setting default values for report promptsYou can set default values for report prompts. When the report is run, the data is automatically filtered based on the prompt values that you specify. The user does not have to specify prompt values when the report is run.

Common report and workspace features

The IBM® Capacity Management Analytics reporting application provides consistent features across all reports and workspaces.

Use this sample report to become familiar with the elements that are common to all of the Capacity Management Analytics reports and workspaces.

Figure 21. Sample Capacity Management Analytics report

Drill to

Contains a list of related reports that a user can drill through to for additional information. Drill to capabilities are also provided in some reports by clicking a section in the report body.

Tip: Note: When you drill through to related reports, the prompt criteria is automatically populated.

Standard prompts

date range

The date range of the data to be analyzed.

time range

The time range within each day of the selected date range to be analyzed.

interval band

The level to which you want to aggregate.

By changing the interval band, a single report can aggregate to different levels, such as 30 minutes or 30 days. You must view the data at an interval that is a multiple of the recording interval. For example, if your recording interval is 10 minutes, you can view data at intervals of 10, 20, or 30 minutes, but not at an interval of 5 minutes.

show

The report can be displayed in different formats.

chart

Information is displayed in a graphical format.

table

Information is displayed in a table.

both

Information is displayed in both a chart and table.

Report specific prompts

Prompt criteria that is specific to the displayed report. In the sample report, the report specific prompts are cpu serial no, processor type, and lpar name.

The values that are populated in the report-specific prompt boxes are generated dynamically by queries to the IBM Tivoli® Decision Support for z/OS® or Capacity Management Analytics data stores. For example, if a new CEC (cpu serial no), processor type, or LPAR is added to the environment, there are no additional tables or lists need to be updated by the user to reflect the new items.

refresh view

The refresh view buttons populate the next prompt box and generate the report.

Workspace prompt and refresh buttons

To avoid refreshing charts unnecessarily, next > buttons are available for each prompt value in the workspace prompt area. After you select a prompt value, you must click next > to refresh the next prompt. Other prompts or workspace charts and tables are not refreshed until the last prompt is selected.

Important: The next > buttons are only for the workspace, and not for the report. You must set each prompt for them to take effect. You must also set them in sequence and click next > to submit the setting.

On the last prompt, click refresh workspace to refresh all of the charts and tables in the workspace.

Each workspace can vary according to their business needs.

Figure 22. Workspace prompt and refresh buttons

Setting default values for report prompts
You can set default values for report prompts. When the report is run, the data is automatically filtered based on the prompt values that you specify. The user does not have to specify prompt values when the report is run.

Parent topic: Report and workspace reference

Setting default values for report promptsYou can set default values for report prompts. When the report is run, the data is automatically filtered based on the prompt values that you specify. The user does not have to specify prompt values when the report is run. For detailed instruction on setting default prompt values, refer to the IBM Cognos Connection User Guide, available on the IBM Cognos Business Intelligence welcome page (http://www.ibm.com/support/knowledgecenter/SSEP7J/welcome) of IBM Knowledge Center. For most prompts, you can select and set the default value directly in the report. In some circumstances, such as when running a report with Run with options, you must manually input the value of some prompts. In these cases, refer to this table for the information you need to set prompt values. Report prompt valuesPrompt namePrompt internal namePrompt value for setting defaultPrompt display valueprocess typep_processor_type IIP IFA zIIP zAAP importance levelp_implvl 0 1 2 34 5 6 System Highest High Medium Low Lowest Discretionary timezonep_timezoneThe integer number of GMT offset seconds. For example, 0 for UTC/GMT, -25200 for PDT(UTC-7:00).The timezone name and the GMT offset hours. For example, UTC, GMT, PDT(UTC-7:00).showp_What_To_Show 0 1 2 chart table both interval band (for all reports except distributed reports)p_Interval_Type yy:01 qq:01 mo:01 dd:01 dd:07 dd:14 dd:30 mm:60 mm:120 mm:180 mm:240 mm:360 mm:480 mm:720 mm:01 mm:05 mm:10 mm:15 mm:30 -yearly- -quarterly- -monthly- 1 day 7 days 14 days 30 days 1 hour 2 hours 3 hours 4 hours 6 hours 8 hours 12 hours 1 minute 5 minutes 10 minutes 15 minutes 30 minutes interval band (for distributed reports)p_Interval_Type 1 2 3 4 5 -yearly- -monthly- -weekly- -daily- -hourly- show first SCRT cyclep_show_first_cycleSHOW_CYCLEN/ASMF collection intervalp_SMF_IntervaltrueN/A scenario p_scenarios observed forecast what-if Observed Forecasted Optimized Show Monetary Valuep_show_monetary1N/ACommon report and workspace featuresThe IBM Capacity Management Analytics reporting application provides consistent features across all reports and workspaces.

Setting default values for report prompts

You can set default values for report prompts. When the report is run, the data is automatically filtered based on the prompt values that you specify. The user does not have to specify prompt values when the report is run.

For detailed instruction on setting default prompt values, refer to the IBM Cognos Connection User Guide, available on the IBM Cognos Business Intelligence welcome page (http://www.ibm.com/support/knowledgecenter/SSEP7J/welcome) of IBM Knowledge Center. For most prompts, you can select and set the default value directly in the report.

In some circumstances, such as when running a report with Run with options, you must manually input the value of some prompts. In these cases, refer to this table for the information you need to set prompt values.

Table 26. Report prompt values

Prompt name
Prompt internal name
Prompt value for setting default
Prompt display value

process type
p_processor_type

IIP

IFA

zIIP

zAAP

importance level
p_implvl

0

1

2

3

4

5

6

System

Highest

High

Medium

Low

Lowest

Discretionary

timezone
p_timezone
The integer number of GMT offset seconds. For example, 0 for UTC/GMT, -25200 for PDT(UTC-7:00).
The timezone name and the GMT offset hours. For example, UTC, GMT, PDT(UTC-7:00).

show
p_What_To_Show

0

1

2

chart

table

both

interval band (for all reports except distributed reports)
p_Interval_Type

yy:01

qq:01

mo:01

dd:01

dd:07

dd:14

dd:30

mm:60

mm:120

mm:180

mm:240

mm:360

mm:480

mm:720

mm:01

mm:05

mm:10

mm:15

mm:30

-yearly-

-quarterly-

-monthly-

1 day

7 days

14 days

30 days

1 hour

2 hours

3 hours

4 hours

6 hours

8 hours

12 hours

1 minute

5 minutes

10 minutes

15 minutes

30 minutes

interval band (for distributed reports)
p_Interval_Type

1

2

3

4

5

-yearly-

-monthly-

-weekly-

-daily-

-hourly-

show first SCRT cycle
p_show_first_cycle
SHOW_CYCLE
N/A

SMF collection interval
p_SMF_Interval
true
N/A

scenario

p_scenarios

observed

forecast

what-if

Observed

Forecasted

Optimized

Show Monetary Value
p_show_monetary
1
N/A

Parent topic: Common report and workspace features

Anomaly Detection Analytics: CICS Anomaly DetailsThis report shows the abnormal transactions that were detected by the CICS anomaly detection streams. This report shows a summary of the abnormal transaction counts and the detail transactions that are determined to be abnormal over a short period.reportsAnomaly Detection Analytics: CICS Anomaly DetailsAnomaly Detection Analytics reportsCICS Anomaly DetailsCICS Anomaly DetailsCICS Anomaly DetailsSource tables[Presentation View].[CICS Anomaly Detection][Presentation View].[DIM MINUTE]Standard prompts:datetime rangeinterval band: hours and minutesshowReport-specific promptstransaction idmvs system idabnormality method You can select CLASSIFIED, CPU, RESPONSE, or IFOREST for the abnormality method.abnormality flag This prompt appears if you select CLASSIFIED in the abnormality method. You can select red or yellow for the flag.The Summary for Abnormality Method CLASSIFIED table contains the following information:TRANSACTION IDMVS SYSTEM IDCICS SYSTEM IDRED COUNTThe number of transactions that are classified as red.YELLOW COUNTThe number of transactions that are classified as yellow.GREEN COUNTThe number of transactions that are classified as green.RED %The percentage of transactions that are classified as red.YELLOW %The percentage of transactions that are classified as yellow.GREEN %The percentage of transactions that are classified as green.The Summary for Abnormality Method CPU, Summary for Abnormality Method RESPONSE, and Summary for Abnormality Method IFOREST tables contain the following information:TRANSACTION IDMVS SYSTEM IDCICS SYSTEM IDABNORMAL COUNTNumber of transactions that are marked as abnormal by the selected abnormality method.NORMAL COUNTNumber of transactions that are marked as normal by the selected abnormality method.ABNORMAL %Percentage of transactions that are marked as abnormal by the selected method.NORMAL %Percentage of transactions that are marked as normal by the selected method.The CICS Anomaly Details for Abnormality Method CLASSIFIED table contains the following information:TIME BANDThe time band is not shown if there are no abnormal transactions in that band.START TIMESTAMPMVS SYSTEM IDCICS SYSTEM IDTRANSACTION TYPESTOP TIMESTAMPCPU SEC (OBSERVED)The observed CPU seconds of the transaction.CPU SEC (PREDICTED)The predicted CPU seconds of the transaction.CPU SEC RATIOThe ratio between the observed CPU seconds and the predicted CPU seconds of the transaction.RESPONSE SEC (OBSERVED)The observed response seconds of the transaction.RESPONSE SEC (PREDICTED)The predicted response seconds of the transaction.RESPONSE SEC RATIOThe ratio between the observed response seconds and the predicted response seconds of the transaction.IFOREST SCOREThe score of the transaction using the IFOREST method.The CICS Anomaly Details for Abnormality Method CPU table contains the following information:TIME BANDThe time band is not shown if there are no abnormal transactions in that band.START TIMESTAMPMVS SYSTEM IDCICS SYSTEM IDTRANSACTION TYPESTOP TIMESTAMPCPU SEC (OBSERVED)The observed CPU seconds of the transaction.CPU SEC (PREDICTED)The predicted CPU seconds of the transaction.CPU SEC RATIOThe ratio between the observed CPU seconds and the predicted CPU seconds of the transaction.The CICS Anomaly Details for Abnormality Method RESPONSE table contains the following information:TIME BANDThe time band is not shown if there are no abnormal transactions in that band.START TIMESTAMPMVS SYSTEM IDCICS SYSTEM IDTRANSACTION TYPESTOP TIMESTAMPRESPONSE SEC (OBSERVED)The observed response seconds of the transaction.RESPONSE SEC (PREDICTED)The predicted response seconds of the transaction.RESPONSE SEC RATIOThe ratio between the observed response seconds and the predicted response seconds of the transaction.The CICS Anomaly Details for Abnormality Method IFOREST table contains the following information:TIME BANDThe time band is not shown if there are no abnormal transactions in that band.START TIMESTAMPMVS SYSTEM IDCICS SYSTEM IDTRANSACTION TYPESTOP TIMESTAMPIFOREST SCOREThe score of the transaction using the IFOREST method.Report and workspace referenceIBM Capacity Management Analytics provides several predefined reports and workspaces that let you monitor and analyze the capacity of your IT services and the IT infrastructure.

Anomaly Detection Analytics: CICS Anomaly Details

This report shows the abnormal transactions that were detected by the CICS anomaly detection streams. This report shows a summary of the abnormal transaction counts and the detail transactions that are determined to be abnormal over a short period.

Figure 23. CICS Anomaly Details

Source tables

[Presentation View].[CICS Anomaly Detection]

[Presentation View].[DIM MINUTE]

Standard prompts:

date

time range

interval band: hours and minutes

show

Report-specific prompts

transaction id

mvs system id

abnormality method

You can select CLASSIFIED, CPU, RESPONSE, or IFOREST for the abnormality method.

abnormality flag

This prompt appears if you select CLASSIFIED in the abnormality method. You can select red or yellow for the flag.

The Summary for Abnormality Method CLASSIFIED table contains the following information:

TRANSACTION ID

MVS SYSTEM ID

CICS SYSTEM ID

RED COUNT

The number of transactions that are classified as red.

YELLOW COUNT

The number of transactions that are classified as yellow.

GREEN COUNT

The number of transactions that are classified as green.

RED %

The percentage of transactions that are classified as red.

YELLOW %

The percentage of transactions that are classified as yellow.

GREEN %

The percentage of transactions that are classified as green.

The Summary for Abnormality Method CPU, Summary for Abnormality Method RESPONSE, and Summary for Abnormality Method IFOREST tables contain the following information:

TRANSACTION ID

MVS SYSTEM ID

CICS SYSTEM ID

ABNORMAL COUNT

Number of transactions that are marked as abnormal by the selected abnormality method.

NORMAL COUNT

Number of transactions that are marked as normal by the selected abnormality method.

ABNORMAL %

Percentage of transactions that are marked as abnormal by the selected method.

NORMAL %

Percentage of transactions that are marked as normal by the selected method.

The CICS Anomaly Details for Abnormality Method CLASSIFIED table contains the following information:

TIME BAND

The time band is not shown if there are no abnormal transactions in that band.

START TIMESTAMP

MVS SYSTEM ID

CICS SYSTEM ID

TRANSACTION TYPE

STOP TIMESTAMP

CPU SEC (OBSERVED)

The observed CPU seconds of the transaction.

CPU SEC (PREDICTED)

The predicted CPU seconds of the transaction.

CPU SEC RATIO

The ratio between the observed CPU seconds and the predicted CPU seconds of the transaction.

RESPONSE SEC (OBSERVED)

The observed response seconds of the transaction.

RESPONSE SEC (PREDICTED)

The predicted response seconds of the transaction.

RESPONSE SEC RATIO

The ratio between the observed response seconds and the predicted response seconds of the transaction.

IFOREST SCORE

The score of the transaction using the IFOREST method.

The CICS Anomaly Details for Abnormality Method CPU table contains the following information:

TIME BAND

The time band is not shown if there are no abnormal transactions in that band.

START TIMESTAMP

MVS SYSTEM ID

CICS SYSTEM ID

TRANSACTION TYPE

STOP TIMESTAMP

CPU SEC (OBSERVED)

The observed CPU seconds of the transaction.

CPU SEC (PREDICTED)

The predicted CPU seconds of the transaction.

CPU SEC RATIO

The ratio between the observed CPU seconds and the predicted CPU seconds of the transaction.

The CICS Anomaly Details for Abnormality Method RESPONSE table contains the following information:

TIME BAND

The time band is not shown if there are no abnormal transactions in that band.

START TIMESTAMP

MVS SYSTEM ID

CICS SYSTEM ID

TRANSACTION TYPE

STOP TIMESTAMP

RESPONSE SEC (OBSERVED)

The observed response seconds of the transaction.

RESPONSE SEC (PREDICTED)

The predicted response seconds of the transaction.

RESPONSE SEC RATIO

The ratio between the observed response seconds and the predicted response seconds of the transaction.

The CICS Anomaly Details for Abnormality Method IFOREST table contains the following information:

TIME BAND

The time band is not shown if there are no abnormal transactions in that band.

START TIMESTAMP

MVS SYSTEM ID

CICS SYSTEM ID

TRANSACTION TYPE

STOP TIMESTAMP

IFOREST SCORE

The score of the transaction using the IFOREST method.

Parent topic: Report and workspace reference

Application Analytics: Distributed CPU Usage - Application Summary ReportThis report shows the 90% CPU busy line for applications. It can be displayed as hourly, daily, weekly, monthly, or yearly.reportsDistributed CPU Usage - Application Summary ReportApplication Analytics reportsDistributed CPU Usage - Application Summary ReportThis report provides a summary of the CPU usage of all the servers associated with an application by showing the count of the servers that have usage within the different thresholds. The report is intended to help provide a quick view of all applications and their CPU usage of the servers in which they reside, by showing the overall results as well as day by day. This daily view can pinpoint when a change occurs and how many servers of an application are affected at the same time.Application Analytics: Distributed CPU Usage - Application SummaryApplication Analytics: Distributed CPU Usage - Application Summary example report.Source tables[Presentation View].[Distributed Application Mapping][Presentation View].[AIX CPU Raw Hour Metrics][Presentation View].[Linux for System X CPU Hour Metrics][Presentation View].[Linux for System Z CPU Hour Metrics][Presentation View].[WIN OPERATING CPU Hour Metrics]Drill through to the following reportsApplication Analytics: Distributed CPU Usage - Application ReportThis report shows the 90% CPU busy line for applications. It can be displayed as hourly, daily, weekly, monthly, or yearly.Standard prompts:date rangetime rangeinterval band: hourly, daily, weekly, monthly, yearlyReport-specific promptsApplication nameEnvironmentThe Analytics Application: Distributed CPU usage - Application report table contains the following information:Application NameEnvironmentServerDetail CPU Utilization by different interval bands (hourly, daily, weekly, monthly, yearly)Green is less than 70%, yellow is between 70% and less than 90%, and red is 90% or greater.Report and workspace referenceIBM Capacity Management Analytics provides several predefined reports and workspaces that let you monitor and analyze the capacity of your IT services and the IT infrastructure.

Application Analytics: Distributed CPU Usage - Application Summary Report

This report shows the 90% CPU busy line for applications. It can be displayed as hourly, daily, weekly, monthly, or yearly.

This report provides a summary of the CPU usage of all the servers associated with an application by showing the count of the servers that have usage within the different thresholds. The report is intended to help provide a quick view of all applications and their CPU usage of the servers in which they reside, by showing the overall results as well as day by day. This daily view can pinpoint when a change occurs and how many servers of an application are affected at the same time.

Figure 24. Application Analytics: Distributed CPU Usage - Application Summary

Source tables

[Presentation View].[Distributed Application Mapping]

[Presentation View].[AIX CPU Raw Hour Metrics]

[Presentation View].[Linux for System X CPU Hour Metrics]

[Presentation View].[Linux for System Z CPU Hour Metrics]

[Presentation View].[WIN OPERATING CPU Hour Metrics]

Drill through to the following reports

Application Analytics: Distributed CPU Usage - Application Report

Standard prompts:

date range

time range

interval band: hourly, daily, weekly, monthly, yearly

Report-specific prompts

Application name

Environment

The Analytics Application: Distributed CPU usage - Application report table contains the following information:

Application Name

Environment

Server

Detail CPU Utilization by different interval bands (hourly, daily, weekly, monthly, yearly)

Green is less than 70%, yellow is between 70% and less than 90%, and red is 90% or greater.

Parent topic: Report and workspace reference

Application Analytics: Distributed CPU Usage - Application ReportThis report shows the 90% CPU busy line for applications. It can be displayed as hourly, daily, weekly, monthly, or yearly.reportsDistributed CPU Usage - Application ReportApplication Analytics reportsDistributed CPU Usage - Application ReportThis report provides a view of all the servers associated with an application and shows the CPU usage for each of them with respect to thresholds of overall usage. The report is intended to help provide a quick view of which servers that support an application are running at higher utilizations than others. This report is normally used after viewing the Application Summary report.Application Analytics: Distributed CPU Usage - Application ReportApplication Analytics: Distributed CPU Usage - Application example report.Source tables[Presentation View].[Distributed Application Mapping][Presentation View].[AIX CPU Raw Hour Metrics][Presentation View].[Linux for System X CPU Hour Metrics][Presentation View].[Linux for System Z CPU Hour Metrics][Presentation View].[WIN OPERATING CPU Hour Metrics]Drill through to the following reportsApplication Analytics: Distributed CPU Usage - Application Summary ReportThis report shows the 90% CPU busy line for applications. It can be displayed as hourly, daily, weekly, monthly, or yearly.Standard prompts:date rangetime rangeinterval band: hourly, daily, weekly, monthly, yearlyReport-specific promptsApplication nameEnvironmentOnly show servers with available dataThe Analytics Application: Distributed CPU usage - Application report table contains the following information:Application NameEnvironmentFunctionServerDetail CPU Utilization by different interval bands (hourly, daily, weekly, monthly, yearly)Green is less than 70%, yellow is between 70% and less than 90%, and red is 90% or greater.Report and workspace referenceIBM Capacity Management Analytics provides several predefined reports and workspaces that let you monitor and analyze the capacity of your IT services and the IT infrastructure.

Application Analytics: Distributed CPU Usage - Application Report

This report shows the 90% CPU busy line for applications. It can be displayed as hourly, daily, weekly, monthly, or yearly.

This report provides a view of all the servers associated with an application and shows the CPU usage for each of them with respect to thresholds of overall usage. The report is intended to help provide a quick view of which servers that support an application are running at higher utilizations than others. This report is normally used after viewing the Application Summary report.

Figure 25. Application Analytics: Distributed CPU Usage - Application Report

Source tables

[Presentation View].[Distributed Application Mapping]

[Presentation View].[AIX CPU Raw Hour Metrics]

[Presentation View].[Linux for System X CPU Hour Metrics]

[Presentation View].[Linux for System Z CPU Hour Metrics]

[Presentation View].[WIN OPERATING CPU Hour Metrics]

Drill through to the following reports

Application Analytics: Distributed CPU Usage - Application Summary Report

Standard prompts:

date range

time range

interval band: hourly, daily, weekly, monthly, yearly

Report-specific prompts

Application name

Environment

Only show servers with available data

The Analytics Application: Distributed CPU usage - Application report table contains the following information:

Application Name

Environment

Function

Server

Detail CPU Utilization by different interval bands (hourly, daily, weekly, monthly, yearly)

Green is less than 70%, yellow is between 70% and less than 90%, and red is 90% or greater.

Parent topic: Report and workspace reference

Application Analytics: MIPS Used - ApplicationThis report shows the average MIPS used by each application or line-of-business grouping across all CPCs and LPARs. The average can be displayed as hourly, daily, weekly, monthly, or yearly.reportsMIPS Used - ApplicationApplication Analytics reportsMIPS Used - ApplicationThe purpose of this report is to be able to compare the MIPS usage of all applications which are mapped in the application mapping table. This also shows you where the MIPS are being consumed by each application so you can focus your attention on which applications use the most MIPS and where they use those MIPS within the environment. You can drill down to see the functions or select fewer applications to compare. You can also see if there is a particular CPC or LPAR that has a large amount of MIPS being consumed by one or more application.This report shows the CPU average busy MIPS for the z/OS application. You can define z/OS workload information in the mapping table.Application Analytics: MIPS Used - ApplicationApplication Analytics: MIPS Used - Application example report.If you change the mapping after the forecast stream is executed, there may be discrepancies between the application MIPS on which the forecast is based and the observed application MIPS.Source tables[Appl zOS CPU MIPS Usage][Application zOS Mapping][Application LPAR Prompt][Application Util View][Application KPMZ Address Space][Application Prompt View]Drill through to the following reportsApplication Analytics: MIPS Used - Application by FunctionThis report shows the average or peak MIPS used by the selected application or line-of-business grouping across all CPCs and LPARs, according to function. The value can be displayed as hourly, daily, weekly, monthly, or yearly. This report also includes a forecast option.Application Analytics: MIPS Used - Application by LPARThis report shows the average MIPS used by all applications or line-of-business groupings on the selected LPAR. The average can be displayed as hourly, weekly, monthly, or yearly.Standard prompts:date rangetime rangeinterval band: hourly, daily, weekly, monthly, yearlyshowReport-specific promptsapplication nameenvironmentprocessor typeThe Application Analytics: MIPS Used - Application table contains the following information:DATEDisplays on all DMR levelsTIMEDisplays on all DMR levelsAPPLICATION NAMEDisplays on application levelENV-FUNCTIONDisplays environment-function levelCPU_LPAR_SYSTEM IDDisplays CPU Serial No, LPAR Name and System IDDATA TYPEDisplay data type is the report class or job nameREPORT CLASSDisplays on the report class levelJOB NAMEDisplays on job name levelCP MIPS/IIP MIPS/AAP MIPSDisplays MIPS values by different processor type: CP/IIP/AAPCAPTURE RATIODisplays on the report class or job name levelPROGRAM TYPEDisplays on the report class/job name levelTRANSACTION RATEDisplays transaction rate of the report class or job name levelTRANSACTIONSDisplays on the report class or job name levelReport and workspace referenceIBM Capacity Management Analytics provides several predefined reports and workspaces that let you monitor and analyze the capacity of your IT services and the IT infrastructure.

Application Analytics: MIPS Used - Application

This report shows the average MIPS used by each application or line-of-business grouping across all CPCs and LPARs. The average can be displayed as hourly, daily, weekly, monthly, or yearly.

The purpose of this report is to be able to compare the MIPS usage of all applications which are mapped in the application mapping table. This also shows you where the MIPS are being consumed by each application so you can focus your attention on which applications use the most MIPS and where they use those MIPS within the environment. You can drill down to see the functions or select fewer applications to compare. You can also see if there is a particular CPC or LPAR that has a large amount of MIPS being consumed by one or more application.

This report shows the CPU average busy MIPS for the z/OS application. You can define z/OS workload information in the mapping table.

Figure 26. Application Analytics: MIPS Used - Application

Important: If you change the mapping after the forecast stream is executed, there may be discrepancies between the application MIPS on which the forecast is based and the observed application MIPS.

Source tables

[Appl zOS CPU MIPS Usage]

[Application zOS Mapping]

[Application LPAR Prompt]

[Application Util View]

[Application KPMZ Address Space]

[Application Prompt View]

Drill through to the following reports

Application Analytics: MIPS Used - Application by Function

Application Analytics: MIPS Used - Application by LPAR

Standard prompts:

date range

time range

interval band: hourly, daily, weekly, monthly, yearly

show

Report-specific prompts

application name

environment

processor type

The Application Analytics: MIPS Used - Application table contains the following information:

DATE

Displays on all DMR levels

TIME

Displays on all DMR levels

APPLICATION NAME

Displays on application level

ENV-FUNCTION

Displays environment-function level

CPU_LPAR_SYSTEM ID

Displays CPU Serial No, LPAR Name and System ID

DATA TYPE

Display data type is the report class or job name

REPORT CLASS

Displays on the report class level

JOB NAME

Displays on job name level

CP MIPS/IIP MIPS/AAP MIPS

Displays MIPS values by different processor type: CP/IIP/AAP

CAPTURE RATIO

Displays on the report class or job name level

PROGRAM TYPE

Displays on the report class/job name level

TRANSACTION RATE

Displays transaction rate of the report class or job name level

TRANSACTIONS

Displays on the report class or job name level

Parent topic: Report and workspace reference

Application Analytics: MIPS Used - Application by FunctionThis report shows the average or peak MIPS used by the selected application or line-of-business grouping across all CPCs and LPARs, according to function. The value can be displayed as hourly, daily, weekly, monthly, or yearly. This report also includes a forecast option.reportsMIPS Used - Application by FunctionApplication Analytics reportsMIPS Used - Application by FunctionIn the application mapping table, you have the flexibility of defining functions within an application. The functions that are defined can represent sub-entities of the application that have their own business growth. This report enables you to view the MIPS usage by application or specific functions within an application. Additionally, you can also see where the MIPS are being consumed. This report provides a forecast capability for the application and where you can include only the functions that make the most sense for your forecast.Application Analytics: MIPS Used - Application by FunctionApplication Analytics: MIPS Used - Application by Function example reportIf you change the mapping after the forecast stream is executed, there can be discrepancies between the application MIPS on which the forecast is based and the observed application MIPS.Source tables[Appl zOS CPU MIPS Usage][Application zOS Forecast][Application zOS Mapping][Application LPAR Prompt][Application Util View][Application KPMZ Address Space][Application Prompt View][Forecast Metadata]Drill through to the following reportsApplication Analytics: MIPS Used - ApplicationThis report shows the average MIPS used by each application or line-of-business grouping across all CPCs and LPARs. The average can be displayed as hourly, daily, weekly, monthly, or yearly.Application Analytics: MIPS Used - Application by LPARThis report shows the average MIPS used by all applications or line-of-business groupings on the selected LPAR. The average can be displayed as hourly, weekly, monthly, or yearly.Standard prompts:date rangetime rangeinterval band: hourly, daily, weekly, monthly, yearlyshowReport-specific promptsshow forecastapplication name advanced filtering, if selected, the following prompts are available: environmentfunctioncpu serial nolpar_system id processor type calculation method There are 3 calculation methods available: Average, Highest Hourly Average, and Percentile of Hourly Average. calculation value This prompt is shown only if you select Highest Hourly Average or Percentile of Hourly Average as the calculation method. If you select Highest Hourly Average, the values are: Highest Hourly Average2nd Highest Hourly Average3rd Highest Hourly Average4th Highest Hourly Average5th Highest Hourly Average If you select Percentile of Hourly Average, the values are: 100%ile of Hourly Average95%ile of Hourly Average90%ile of Hourly Average85%ile of Hourly Average80%ile of Hourly Average75%ile of Hourly Average70%ile of Hourly Average The Observed MIPS Used - Application by Function chart and table contains the following information. When the show forecast prompt is not selected, only observed values are displayed.DATE BANDThe start date of the time interval, display only when calculation method is average.TIME BANDThe start time of the time interval, display only when calculation method is average.PEAK DATE The date when the peak of the time interval happens. This value is displayed only when the calculation method is Highest Hourly Average or Percentile of Hourly Average. PEAK TIME The time when the peak of the time interval happens. This value is displayed only when the calculation method is Highest Hourly Average or Percentile of Hourly Average. MIPS USED - TOTAL (OBSERVED)The sum of the observed MIPS utilization of the application for the selected environment, function, cpu serial no, and lpar_system id, filtering for the selected processor type at each time interval or at the observed peak hour.ENVIRONMENTFUNCTIONCPU_SERIAL_NOThe zServer/CEC serial number.SYSPLEX_NAMEMIPS USED (OBSERVED)The detail value of the observed MIPS utilization of the application for each environment, function, cpu serial no, and lpar_system id, filtering for the selected processor type at each time interval or at the observed peak hour.The Forecasted MIPS Used – Application by Function chart and table contains the information that is shown in the Observed MIPS Used - Application by Function chart and table as well as the following information. When the show forecast prompt is selected, both forecasted and observed values are displayed.PEAK DATE (FORECASTED) The date when the forecasted peak of the time interval happens. This value is displayed only when the calculation method is Highest Hourly Average or Percentile of Hourly Average. PEAK TIME (FORECASTED) The time when the forecasted peak of the time interval happens. This value is displayed only when the calculation method is Highest Hourly Average or Percentile of Hourly Average. MIPS USED – TOTAL (FORECASTED)The sum of the forecasted MIPS utilization of the application for the selected environment, function, cpu serial no, and lpar_system id, filtering for the selected processor type at each time interval or at the forecasted peak hour.MIPS USED – TOTAL (FORECASTED LOWER CONFIDENCE BOUND)The lower confidence bound of forecasted MIPS.MIPS USED – TOTAL (FORECASTED UPPER CONFIDENCE BOUND)The upper confidence bound of forecasted MIPS.MIPS USED (FORECASTED) The detail value of the forecasted MIPS utilization of the application for each environment, function, cpu serial no, and lpar_system id, filtering for the selected processor type at each time interval or at the forecasted peak hour. If the application peak value forecast stream is used in the report, then this data item is not displayed. MIPS USED (FORECASTED LOWER CONFIDENCE BOUND)The lower confidence bound of forecasted MIPS. If the application peak value forecast stream is used in the report, then this data item is not displayed. MIPS USED (FORECASTED UPPER CONFIDENCE BOUND)The upper confidence bound of forecasted MIPS. If the application peak value forecast stream is used in the report, then this data item is not displayed. FORECAST LEVELThe aggregation level of the forecast stream that is used in the forecast MIPS calculation.The Forecast Metadata table contains the following information. This table is displayed when the show forecast prompt is selected.STREAM TYPEThe stream type that is used by the available forecast results set, for example appl_sec_avg_hourly. This means that the stream is for the application forecast, and it forecasts the CPU seconds at the hourly average aggregation level.AGGREGATIONThe aggregation level of the available forecast results set. The values are: Average Hourly, Average Daily, Highest Hourly Average, 2nd Highest Hourly Average, 3rd Highest Hourly Average, 4th Highest Hourly Average, and 5th Highest Hourly Average.HOUR STARTThe start hour of the forecasted hour range of the available forecast results set.HOUR ENDThe end hour of the forecasted hour range of the available forecast results set.HISTORY STARTThe start date of the based historical data range of the available forecast results set.HISTORY ENDThe end date of the based historical data range of the available forecast results set.FORECAST STARTThe start date of the available forecast results set.FORECAST ENDThe end date of the available forecast results set.EXECUTE TIMESTAMPThe forecast execution timestamp of the available forecast results set.MAPPING TIMESTAMPThe last updated timestamp of the mapping table on which the forecast is based.The rules to choose the forecast stream aggregation level when show forecast is selected are: If you select an hourly interval band: If you select the Average calculation method: If there is an hourly average stream available, then it is used.Otherwise, no data available is displayed.If you select the Highest Hourly Average or Percentile of Hourly Average calculation method: The daily interval band is used and the stream, the logic for displaying the report, and the interval band prompts are set to the daily interval band as well. If you select daily, weekly, monthly, or yearly: If you select the Average calculation method: If there is a suitable daily average stream available, then it is used. A suitable daily average stream means that the time range that you select is the same as the time range of the daily average stream.Otherwise, if there is a suitable hourly average stream available then it is used. A suitable hourly average stream means that the time range that you select is within the time range of the hourly average stream.Otherwise, no data available is displayed.If you select the Highest Hourly Average calculation method: If you select advanced filtering, then the hourly average stream is used. A suitable hourly average stream means that the time range that you select is within the time range of the hourly average stream. Otherwise, no data available is displayed.If you do not select advanced filtering and there is a suitable daily peak stream available, then the daily peak stream is used. Otherwise, if there is a suitable hourly average stream available, then it is used. A suitable daily peak stream means that the calculation value (highest, 2nd highest, etc.) that you select has the same aggregation number stream available, and that the time range that you select is the same as the time range of the daily peak stream.If you select the Percentile of Hourly Average calculation method: If there is a suitable hourly average stream available, then it is used.Otherwise, no data available is displayed.Report and workspace referenceIBM Capacity Management Analytics provides several predefined reports and workspaces that let you monitor and analyze the capacity of your IT services and the IT infrastructure.

Application Analytics: MIPS Used - Application by Function

This report shows the average or peak MIPS used by the selected application or line-of-business grouping across all CPCs and LPARs, according to function. The value can be displayed as hourly, daily, weekly, monthly, or yearly. This report also includes a forecast option.

In the application mapping table, you have the flexibility of defining functions within an application. The functions that are defined can represent sub-entities of the application that have their own business growth. This report enables you to view the MIPS usage by application or specific functions within an application. Additionally, you can also see where the MIPS are being consumed. This report provides a forecast capability for the application and where you can include only the functions that make the most sense for your forecast.

Figure 27. Application Analytics: MIPS Used - Application by Function

Important: If you change the mapping after the forecast stream is executed, there can be discrepancies between the application MIPS on which the forecast is based and the observed application MIPS.

Source tables

[Appl zOS CPU MIPS Usage]

[Application zOS Forecast]

[Application zOS Mapping]

[Application LPAR Prompt]

[Application Util View]

[Application KPMZ Address Space]

[Application Prompt View]

[Forecast Metadata]

Drill through to the following reports

Application Analytics: MIPS Used - Application

Application Analytics: MIPS Used - Application by LPAR

Standard prompts:

date range

time range

interval band: hourly, daily, weekly, monthly, yearly

show

Report-specific prompts

show forecast

application name

advanced filtering, if selected, the following prompts are available:

environment

function

cpu serial no

lpar_system id

processor type

calculation method

There are 3 calculation methods available: Average, Highest Hourly Average, and Percentile of Hourly Average.

calculation value

This prompt is shown only if you select Highest Hourly Average or Percentile of Hourly Average as the calculation method.

If you select Highest Hourly Average, the values are:

Highest Hourly Average

2nd Highest Hourly Average

3rd Highest Hourly Average

4th Highest Hourly Average

5th Highest Hourly Average

If you select Percentile of Hourly Average, the values are:

100%ile of Hourly Average

95%ile of Hourly Average

90%ile of Hourly Average

85%ile of Hourly Average

80%ile of Hourly Average

75%ile of Hourly Average

70%ile of Hourly Average

The Observed MIPS Used - Application by Function chart and table contains the following information. When the show forecast prompt is not selected, only observed values are displayed.

DATE BAND

The start date of the time interval, display only when calculation method is average.

TIME BAND

The start time of the time interval, display only when calculation method is average.

PEAK DATE

The date when the peak of the time interval happens.

This value is displayed only when the calculation method is Highest Hourly Average or Percentile of Hourly Average.

PEAK TIME

The time when the peak of the time interval happens.

This value is displayed only when the calculation method is Highest Hourly Average or Percentile of Hourly Average.

MIPS USED - TOTAL (OBSERVED)

The sum of the observed MIPS utilization of the application for the selected environment, function, cpu serial no, and lpar_system id, filtering for the selected processor type at each time interval or at the observed peak hour.

ENVIRONMENT

FUNCTION

CPU_SERIAL_NO

The zServer/CEC serial number.

SYSPLEX_NAME

MIPS USED (OBSERVED)

The detail value of the observed MIPS utilization of the application for each environment, function, cpu serial no, and lpar_system id, filtering for the selected processor type at each time interval or at the observed peak hour.

The Forecasted MIPS Used – Application by Function chart and table contains the information that is shown in the Observed MIPS Used - Application by Function chart and table as well as the following information. When the show forecast prompt is selected, both forecasted and observed values are displayed.

PEAK DATE (FORECASTED)

The date when the forecasted peak of the time interval happens.

This value is displayed only when the calculation method is Highest Hourly Average or Percentile of Hourly Average.

PEAK TIME (FORECASTED)

The time when the forecasted peak of the time interval happens.

This value is displayed only when the calculation method is Highest Hourly Average or Percentile of Hourly Average.

MIPS USED – TOTAL (FORECASTED)

The sum of the forecasted MIPS utilization of the application for the selected environment, function, cpu serial no, and lpar_system id, filtering for the selected processor type at each time interval or at the forecasted peak hour.

MIPS USED – TOTAL (FORECASTED LOWER CONFIDENCE BOUND)

The lower confidence bound of forecasted MIPS.

MIPS USED – TOTAL (FORECASTED UPPER CONFIDENCE BOUND)

The upper confidence bound of forecasted MIPS.

MIPS USED (FORECASTED)

The detail value of the forecasted MIPS utilization of the application for each environment, function, cpu serial no, and lpar_system id, filtering for the selected processor type at each time interval or at the forecasted peak hour.

If the application peak value forecast stream is used in the report, then this data item is not displayed.

MIPS USED (FORECASTED LOWER CONFIDENCE BOUND)

The lower confidence bound of forecasted MIPS. If the application peak value forecast stream is used in the report, then this data item is not displayed.

MIPS USED (FORECASTED UPPER CONFIDENCE BOUND)

The upper confidence bound of forecasted MIPS. If the application peak value forecast stream is used in the report, then this data item is not displayed.

FORECAST LEVEL

The aggregation level of the forecast stream that is used in the forecast MIPS calculation.

The Forecast Metadata table contains the following information. This table is displayed when the show forecast prompt is selected.

STREAM TYPE

The stream type that is used by the available forecast results set, for example appl_sec_avg_hourly. This means that the stream is for the application forecast, and it forecasts the CPU seconds at the hourly average aggregation level.

AGGREGATION

The aggregation level of the available forecast results set. The values are: Average Hourly, Average Daily, Highest Hourly Average, 2nd Highest Hourly Average, 3rd Highest Hourly Average, 4th Highest Hourly Average, and 5th Highest Hourly Average.

HOUR START

The start hour of the forecasted hour range of the available forecast results set.

HOUR END

The end hour of the forecasted hour range of the available forecast results set.

HISTORY START

The start date of the based historical data range of the available forecast results set.

HISTORY END

The end date of the based historical data range of the available forecast results set.

FORECAST START

The start date of the available forecast results set.

FORECAST END

The end date of the available forecast results set.

EXECUTE TIMESTAMP

The forecast execution timestamp of the available forecast results set.

MAPPING TIMESTAMP

The last updated timestamp of the mapping table on which the forecast is based.

The rules to choose the forecast stream aggregation level when show forecast is selected are:

If you select an hourly interval band:

If you select the Average calculation method:

If there is an hourly average stream available, then it is used.

Otherwise, no data available is displayed.

If you select the Highest Hourly Average or Percentile of Hourly Average calculation method:

The daily interval band is used and the stream, the logic for displaying the report, and the interval band prompts are set to the daily interval band as well.

If you select daily, weekly, monthly, or yearly:

If you select the Average calculation method:

If there is a suitable daily average stream available, then it is used. A suitable daily average stream means that the time range that you select is the same as the time range of the daily average stream.

Otherwise, if there is a suitable hourly average stream available then it is used. A suitable hourly average stream means that the time range that you select is within the time range of the hourly average stream.

Otherwise, no data available is displayed.

If you select the Highest Hourly Average calculation method:

If you select advanced filtering, then the hourly average stream is used. A suitable hourly average stream means that the time range that you select is within the time range of the hourly average stream. Otherwise, no data available is displayed.

If you do not select advanced filtering and there is a suitable daily peak stream available, then the daily peak stream is used. Otherwise, if there is a suitable hourly average stream available, then it is used.

A suitable daily peak stream means that the calculation value (highest, 2nd highest, etc.) that you select has the same aggregation number stream available, and that the time range that you select is the same as the time range of the daily peak stream.

If you select the Percentile of Hourly Average calculation method:

If there is a suitable hourly average stream available, then it is used.

Otherwise, no data available is displayed.

Parent topic: Report and workspace reference

Application Analytics: MIPS Used - Application by LPARThis report shows the average MIPS used by all applications or line-of-business groupings on the selected LPAR. The average can be displayed as hourly, weekly, monthly, or yearly.reportsMIPS Used - Application by LPARApplication Analytics reportsMIPS Used - Application by LPARThis report focuses on a LPAR and shows the MIPS usage of all applications in which it is running. If there is a planned outage change to a LPAR, then this report can help show the usage by application across time. You can plan ahead by finding out which applications may be impacted and what days and times show the least impact.If there is a performance issue or a capacity issue on a LPAR, this report will help identify the usage patterns of each application.This report shows the CPU average busy MIPS for the z/OS application. You can define z/OS workload information to the mapping table.Application Analytics: MIPS Used - Application by LPARApplication Analytics: MIPS used - Application by LPAR example report.Source tables[Appl zOS CPU MIPS Usage][Application zOS Mapping][Application LPAR Prompt][Application Util View][Application KPMZ Address Space][Application Prompt View]Drill through to the following reportsApplication Analytics: MIPS Used - ApplicationThis report shows the average MIPS used by each application or line-of-business grouping across all CPCs and LPARs. The average can be displayed as hourly, daily, weekly, monthly, or yearly.Application Analytics: MIPS Used - Application by LPARThis report shows the average MIPS used by all applications or line-of-business groupings on the selected LPAR. The average can be displayed as hourly, weekly, monthly, or yearly.CPU: MIPS Used - Service Class Period Level This report provides a comprehensive view of all of the applications that are running across the entire systems landscape. This helps you quickly rebalance system allocation and to focus on the most critical applications.CPU: MIPS Used - zServer/LPAR LevelThis report shows the observed average or peak CPU MIPS used at the zServer/CPC level and the forecasted average or peak CPU MIPS used along with their confidence interval. The average CPU MIPS used (observed and forecasted) and peak CPU MIPS used (observed and forecasted) can be displayed for each processor type (CP, zIIP, zAAP, IFL, or ICF) at each interval band and is broken down to the LPAR level. The forecasted peak CPU MIPS used can be displayed for each processor type at each interval band on the CPC level.SCA: LPAR MSU UtilizationThis report helps you understand and forecast your costs based on your MSU usage. You can compare your historical MSU usage with the forecasted usage to see upcoming trends and take proactive action to adjust your workload and capacity limits to help control your bill.Standard prompts:date rangetime rangeinterval band: hourly, daily, weekly, monthly, yearlyshowReport-specific promptscpu serial nolpar_system idapplication nameenvironmentprocessor type: CP is displayed in the processor type prompt as a default in some cases. Ensure that you click refresh view after you have completed all relevant fields.The Application Analytics: MIPS used - Application by LPAR table contains the following information:DATEDisplays on all DMR levelsTIMEDisplays on all DMR levelsAPPLICATIONDisplays on application levelENV-FUNCTIONDisplays environment-function levelDATA TYPEDisplay data type is report class or job nameREPORT CLASS OR JOB NAMEDisplays on report class or job name levelCP MIPS/IIP MIPS/AAP MIPSDisplays MIPS values by different processor type: CP/IIP/AAPCAPTURE RATIODisplay on report class or job name levelTRANSACTION COUNTSDisplays transaction counts on report class or job name levelTRANSACTION RATEDisplays transaction rate report class or job name levelReport and workspace referenceIBM Capacity Management Analytics provides several predefined reports and workspaces that let you monitor and analyze the capacity of your IT services and the IT infrastructure.

Application Analytics: MIPS Used - Application by LPAR

This report shows the average MIPS used by all applications or line-of-business groupings on the selected LPAR. The average can be displayed as hourly, weekly, monthly, or yearly.

This report focuses on a LPAR and shows the MIPS usage of all applications in which it is running. If there is a planned outage change to a LPAR, then this report can help show the usage by application across time. You can plan ahead by finding out which applications may be impacted and what days and times show the least impact.

If there is a performance issue or a capacity issue on a LPAR, this report will help identify the usage patterns of each application.

This report shows the CPU average busy MIPS for the z/OS application. You can define z/OS workload information to the mapping table.

Figure 28. Application Analytics: MIPS Used - Application by LPAR

Source tables

[Appl zOS CPU MIPS Usage]

[Application zOS Mapping]

[Application LPAR Prompt]

[Application Util View]

[Application KPMZ Address Space]

[Application Prompt View]

Drill through to the following reports

Application Analytics: MIPS Used - Application

Application Analytics: MIPS Used - Application by LPAR

CPU: MIPS Used - Service Class Period Level

CPU: MIPS Used - zServer/LPAR Level

SCA: LPAR MSU Utilization

Standard prompts:

date range

time range

interval band: hourly, daily, weekly, monthly, yearly

show

Report-specific prompts

cpu serial no

lpar_system id

application name

environment

processor type: CP is displayed in the processor type prompt as a default in some cases. Ensure that you click refresh view after you have completed all relevant fields.

The Application Analytics: MIPS used - Application by LPAR table contains the following information:

DATE

Displays on all DMR levels

TIME

Displays on all DMR levels

APPLICATION

Displays on application level

ENV-FUNCTION

Displays environment-function level

DATA TYPE

Display data type is report class or job name

REPORT CLASS OR JOB NAME

Displays on report class or job name level

CP MIPS/IIP MIPS/AAP MIPS

Displays MIPS values by different processor type: CP/IIP/AAP

CAPTURE RATIO

Display on report class or job name level

TRANSACTION COUNTS

Displays transaction counts on report class or job name level

TRANSACTION RATE

Displays transaction rate report class or job name level

Parent topic: Report and workspace reference

Application Analytics: MIPS Used - Report Class by Job NameThis report shows the average MIPS used for a detailed list of the job names that were mapped with Workload Manager to a Report Class. The average can be displayed as hourly, daily, weekly, monthly, or yearly.reportsMIPS Used - Report Class by Job NameApplication Analytics reportsMIPS Used - Report Class by Job NameReport classes are used to map to functions within an application. For instance, you can look at an application usage over several days, then select a single day to view the hourly usage and drill into it to determine if something has changed. Application Analytics: MIPS Used - Application by LPARApplication Analytics: MIPS used - Application by LPAR example report.Source tables[Appl zOS CPU MIPS Usage][Application zOS Mapping][Application LPAR Prompt][Application Util View][Application KPMZ Address Space][Application Prompt View]Drill through to the following reportsApplication Analytics: MIPS Used - ApplicationThis report shows the average MIPS used by each application or line-of-business grouping across all CPCs and LPARs. The average can be displayed as hourly, daily, weekly, monthly, or yearly.Application Analytics: MIPS Used - Application by FunctionThis report shows the average or peak MIPS used by the selected application or line-of-business grouping across all CPCs and LPARs, according to function. The value can be displayed as hourly, daily, weekly, monthly, or yearly. This report also includes a forecast option.Standard prompts:date rangetime rangeinterval band: hourly, daily, weekly, monthly, yearlyReport-specific promptsreport classcpu serial nolpar_system idprocessor typeThe Analytics Application: MIPS Used by Report Class by Job Name chart or table contains the following information:DATEDisplays on all DMR levelsTIMEDisplays on all DMR levelsREPORT CLASSDisplays report class the user selectedJOB NAMEDisplays job name of one report class the user selectedCPU SERIAL NOcpu serial noLPAR SYSTEM IDlpar name_system idCP MIPS/IIP MIPS/AAP MIPSDisplays MIPS values by different processor type: CP/IIP/AAPReport and workspace referenceIBM Capacity Management Analytics provides several predefined reports and workspaces that let you monitor and analyze the capacity of your IT services and the IT infrastructure.

Application Analytics: MIPS Used - Report Class by Job Name

This report shows the average MIPS used for a detailed list of the job names that were mapped with Workload Manager to a Report Class. The average can be displayed as hourly, daily, weekly, monthly, or yearly.

Report classes are used to map to functions within an application. For instance, you can look at an application usage over several days, then select a single day to view the hourly usage and drill into it to determine if something has changed.

Figure 29. Application Analytics: MIPS Used - Application by LPAR

Source tables

[Appl zOS CPU MIPS Usage]

[Application zOS Mapping]

[Application LPAR Prompt]

[Application Util View]

[Application KPMZ Address Space]

[Application Prompt View]

Drill through to the following reports

Application Analytics: MIPS Used - Application

Application Analytics: MIPS Used - Application by Function

Standard prompts:

date range

time range

interval band: hourly, daily, weekly, monthly, yearly

Report-specific prompts

report class

cpu serial no

lpar_system id

processor type

The Analytics Application: MIPS Used by Report Class by Job Name chart or table contains the following information:

DATE

Displays on all DMR levels

TIME

Displays on all DMR levels

REPORT CLASS

Displays report class the user selected

JOB NAME

Displays job name of one report class the user selected

CPU SERIAL NO

cpu serial no

LPAR SYSTEM ID

lpar name_system id

CP MIPS/IIP MIPS/AAP MIPS

Displays MIPS values by different processor type: CP/IIP/AAP

Parent topic: Report and workspace reference

CPU: AIX - CPU UsageThis report shows the percentage of CPU used from both a virtual and physical perspective.CPU reportsAIX - CPU UsagereportsAIX - CPU UsageThe server might be virtualized or it might be a dedicated physical server. Showing both virtual and physical servers lets you know what type of server it is and how it is using its resources.AIX - CPU Usage exampleAIX - CPU Usage exampleSource table[Presentation View].[AIX DIST_Date][Presentation View].[AIX DIST_Hour][Presentation View].[AIX DIST TIME][Presentation View].[AIX CPU Fact][Presentation View].[AIX IMAGE]Drill through to the following reportMemory: AIX - Memory UsageThis report shows the free physical memory, free virtual memory, and active virtual memory on the system. The report also displays metric showing the turnover of physical memory, such as Page-In/sec and Page-Out/sec (file system related pages), as well as PageSpace-In/sec and PageSpace-Out/sec.Standard prompts:date rangetime rangeinterval bandtime zoneshowReport-specific promptsServer NameThe AIX - CPU Usage report contains the following information:server nameThe name of the server against which the report is run.NUM PHYS CPUThe number of active virtual processors.NUM LOGICAL CPUThe logical number of CPUs that AIX can dispatch on. VIRT TOT CPU %BUSYTotal percent CPU busy of the Logical CPU count. This is the sum of Virt SYS CPU % Busy and Virt USR CPU % Busy.PERCENTAGE 10%10% CPU busy line.PERCENTAGE 90%90% CPU busy line. If the maximum CPU Usage percentage is less than 90%, then only 10% CPU busy line is displayed.Time Zone Summary tableThis table displays the time zone summary information for a selected server and contains the following elements:TIME ZONE (REPORT)The time zone used when displaying the report. The format is UTC -5:00.START DATE TIME (REPORT)The start date and time in reporting the time zone. The start date and time is the latest time between the start date and time in the prompt and the earliest date and time of available data in the database.END DATE TIME (REPORT)The end date and time in reporting the time zone. The start date and time is the earliest time between the end date and time in the prompt and the latest date and time of available data in the database.TIME ZONE (SERVER OS)The local time of the selected server. The format is GMT -5:00the server time zone is the time zone of the server OS and not the time of where the server is physically located.START DATE TIME (SERVER OS)The start date and time in the selected server local time zone. The start date and time is the latest time between the start date and the time in the prompt and the earliest date and time of available data in the database.END DATE TIME (SERVER OS)The end date and time in the selected server local time zone. The end date and time is the earliest time between the end date and the time in the prompt and the latest date and time of available data in the database.Report and workspace referenceIBM Capacity Management Analytics provides several predefined reports and workspaces that let you monitor and analyze the capacity of your IT services and the IT infrastructure.

CPU: AIX - CPU Usage

This report shows the percentage of CPU used from both a virtual and physical perspective.

The server might be virtualized or it might be a dedicated physical server. Showing both virtual and physical servers lets you know what type of server it is and how it is using its resources.

Figure 30. AIX - CPU Usage example

Source table

[Presentation View].[AIX® DIST_Date]

[Presentation View].[AIX DIST_Hour]

[Presentation View].[AIX DIST TIME]

[Presentation View].[AIX CPU Fact]

[Presentation View].[AIX IMAGE]

Drill through to the following report

Memory: AIX - Memory Usage

Standard prompts:

date range

time range

interval band

time zone

show

Report-specific prompts

Server Name

The AIX - CPU Usage report contains the following information:

server name

The name of the server against which the report is run.

NUM PHYS CPU

The number of active virtual processors.

NUM LOGICAL CPU

The logical number of CPUs that AIX can dispatch on.

VIRT TOT CPU %BUSY

Total percent CPU busy of the Logical CPU count. This is the sum of Virt SYS CPU % Busy and Virt USR CPU % Busy.

PERCENTAGE 10%

10% CPU busy line.

PERCENTAGE 90%

90% CPU busy line. If the maximum CPU Usage percentage is less than 90%, then only 10% CPU busy line is displayed.

Time Zone Summary table

This table displays the time zone summary information for a selected server and contains the following elements:

TIME ZONE (REPORT)

The time zone used when displaying the report. The format is UTC -5:00.

START DATE TIME (REPORT)

The start date and time in reporting the time zone. The start date and time is the latest time between the start date and time in the prompt and the earliest date and time of available data in the database.

END DATE TIME (REPORT)

The end date and time in reporting the time zone. The start date and time is the earliest time between the end date and time in the prompt and the latest date and time of available data in the database.

TIME ZONE (SERVER OS)

The local time of the selected server. The format is GMT -5:00

Important: the server time zone is the time zone of the server OS and not the time of where the server is physically located.

START DATE TIME (SERVER OS)

The start date and time in the selected server local time zone. The start date and time is the latest time between the start date and the time in the prompt and the earliest date and time of available data in the database.

END DATE TIME (SERVER OS)

The end date and time in the selected server local time zone. The end date and time is the earliest time between the end date and the time in the prompt and the latest date and time of available data in the database.

Parent topic: Report and workspace reference

CPU: Latent DemandLatent demand represents the work that is waiting to be dispatched on a CP where all of the other CPs are currently busy. This report shows you which systems are overloaded and, as a result, could potentially slow down your high-priority applications. The report allows you to be proactive and reroute this work to less-utilized systems for faster processing.CPU reportsLatent DemandreportsLatent DemandUsing this report, you can see the In-and-Ready Count and the Capture Ratio for a system. When the In-and-Ready Count exceeds the number of logical processors that are assigned to a system, then you have latent demand because work is waiting to be dispatched to a CP that is not already busy.Latent Demand exampleLatend Demand report exampleSource tables[Presentation View].[z/OS System Level Metrics][Presentation View].[WLM Service Class Period Usage/Delay Metrics]Drill through to the following reportsWLM: Delays by Importance LevelStandard prompts:date rangetime rangeinterval bandshowReport-specific promptssystemimportance levelThis report contains the following information:systemThe z/OS system id.importance levelThe WLM importance level that is assigned to a service class period.processor typeThe engine type: CP, zIIP, zAAP.AVG # OF ADDRESS SPACES WAITINGThe weighted average number of address spaces that are swapped in and ready to be dispatched.CPU DELAY COUNTSNumber of CPU delay samples. Calculated as the sum of R723CCDE.AVG of CP WORK UNITSAverage number of work units for general-purpose processors over the interval. Calculated as the average of SMF70CTT.AVG of zIIP WORK UNITSAverage number of work units for zIIP processors over the interval. Calculated as the average of SMF70ETT.AVG of zAAP WORK UNITSAverage number of work units for zAAP processors over the interval. Calculated as the average of SMF70DTT.MAX of CP WORK UNITSMaximum number of work units for general-purpose processors over the interval. Calculated as the minimum of SMF70CMM.MAX of zIIP WORK UNITSMaximum number of work units for zIIP processors over the interval. Calculated as the minimum of SMF70EMM.MAX of zAAP WORK UNITSMaximum number of work units for zAAP processors over the interval. Calculated as the minimum of SMF70DMM.AVG# of WORK UNITS WAITINGThe weighted average number of any work is queued and waiting to be processed.ONLINE PROCESSORSThe number of engines online to a system on which work can be dispatched.Report and workspace referenceIBM Capacity Management Analytics provides several predefined reports and workspaces that let you monitor and analyze the capacity of your IT services and the IT infrastructure.

CPU: Latent Demand

Latent demand represents the work that is waiting to be dispatched on a CP where all of the other CPs are currently busy. This report shows you which systems are overloaded and, as a result, could potentially slow down your high-priority applications. The report allows you to be proactive and reroute this work to less-utilized systems for faster processing.

Using this report, you can see the In-and-Ready Count and the Capture Ratio for a system. When the In-and-Ready Count exceeds the number of logical processors that are assigned to a system, then you have latent demand because work is waiting to be dispatched to a CP that is not already busy.

Figure 31. Latent Demand example

Source tables

[Presentation View].[z/OS System Level Metrics]

[Presentation View].[WLM Service Class Period Usage/Delay Metrics]

Drill through to the following reports

WLM: Delays by Importance Level

Standard prompts:

date range

time range

interval band

show

Report-specific prompts

system

importance level

This report contains the following information:

system

The z/OS system id.

importance level

The WLM importance level that is assigned to a service class period.

processor type

The engine type: CP, zIIP, zAAP.

AVG # OF ADDRESS SPACES WAITING

The weighted average number of address spaces that are swapped in and ready to be dispatched.

CPU DELAY COUNTS

Number of CPU delay samples. Calculated as the sum of R723CCDE.

AVG of CP WORK UNITS

Average number of work units for general-purpose processors over the interval. Calculated as the average of SMF70CTT.

AVG of zIIP WORK UNITS

Average number of work units for zIIP processors over the interval. Calculated as the average of SMF70ETT.

AVG of zAAP WORK UNITS

Average number of work units for zAAP processors over the interval. Calculated as the average of SMF70DTT.

MAX of CP WORK UNITS

Maximum number of work units for general-purpose processors over the interval. Calculated as the minimum of SMF70CMM.

MAX of zIIP WORK UNITS

Maximum number of work units for zIIP processors over the interval. Calculated as the minimum of SMF70EMM.

MAX of zAAP WORK UNITS

Maximum number of work units for zAAP processors over the interval. Calculated as the minimum of SMF70DMM.

AVG# of WORK UNITS WAITING

The weighted average number of any work is queued and waiting to be processed.

ONLINE PROCESSORS

The number of engines online to a system on which work can be dispatched.

Parent topic: Report and workspace reference

CPU: Linux for System x - CPU UsageThis report shows the percentage of CPU used from both a virtual and physical perspective.reportsLinux for System x - CPU UsageCPU reportsLinux for System x - CPU UsageThe server might be virtualized or it might be a dedicated physical server. Showing both virtual and physical servers lets you know what type of server it is and how it is using its resources.Linux for System X - CPU Usage exampleLinux for System X - CPU Usage exampleSource table[Presentation View].[Linux for System X DIST Date][Presentation View].[Linux for System X DIST Hour][Presentation View].[Linux for System X DIST Time][Presentation View].[Linux for System X CPU Fact][Presentation View].[Linux for System X Image]Drill through to the following reportMemory: Linux for System x - Memory UsageThis report shows the installed (visible) memory, free physical memory, and free virtual memory on the server.Standard prompts:date rangetime rangeinterval bandtime zoneshowReport-specific promptsServer NameThe Linux for System x - CPU Usage report contains the following information:server nameThe name of the server against which the report is run.VIRT CPU %BUSY The percentage of CPU resources that are used by this operating system in relation to the CPU capacity of the virtual server, without taking capping effects or competition with other partitions into account.PHYS CPU %BUSY The percentage of physical CPU resources that are used by this operating systemPERCENTAGE 10%10% CPU busy line.PERCENTAGE 90%90% CPU busy line. If the maximum CPU Usage percentage is less than 90%, then only 10% CPU busy line is displayed.Time Zone Summary tableThis table displays the time zone summary information for a selected server and contains the following elements:TIME ZONE (REPORT)The time zone used when displaying the report. The format is UTC -5:00.START DATE TIME (REPORT)The start date and time in reporting the time zone. The start date and time is the latest time between the start date and time in the prompt and the earliest date and time of available data in the database.END DATE TIME (REPORT)The end date and time in reporting the time zone. The start date and time is the earliest time between the end date and time in the prompt and the latest date and time of available data in the database.TIME ZONE (SERVER OS)The local time of the selected server. The format is GMT -5:00the server time zone is the time zone of the server OS and not the time of where the server is physically located.START DATE TIME (SERVER OS)The start date and time in the selected server local time zone. The start date and time is the latest time between the start date and the time in the prompt and the earliest date and time of available data in the database.END DATE TIME (SERVER OS)The end date and time in the selected server local time zone. The end date and time is the earliest time between the end date and the time in the prompt and the latest date and time of available data in the database.Report and workspace referenceIBM Capacity Management Analytics provides several predefined reports and workspaces that let you monitor and analyze the capacity of your IT services and the IT infrastructure.

CPU: Linux for System x - CPU Usage

This report shows the percentage of CPU used from both a virtual and physical perspective.

The server might be virtualized or it might be a dedicated physical server. Showing both virtual and physical servers lets you know what type of server it is and how it is using its resources.

Figure 32. Linux for System X - CPU Usage example

Source table

[Presentation View].[Linux for System X DIST Date]

[Presentation View].[Linux for System X DIST Hour]

[Presentation View].[Linux for System X DIST Time]

[Presentation View].[Linux for System X CPU Fact]

[Presentation View].[Linux for System X Image]

Drill through to the following report

Memory: Linux for System x - Memory Usage

Standard prompts:

date range

time range

interval band

time zone

show

Report-specific prompts

Server Name

The Linux for System x - CPU Usage report contains the following information:

server name

The name of the server against which the report is run.

VIRT CPU %BUSY

The percentage of CPU resources that are used by this operating system in relation to the CPU capacity of the virtual server, without taking capping effects or competition with other partitions into account.

PHYS CPU %BUSY

The percentage of physical CPU resources that are used by this operating system

PERCENTAGE 10%

10% CPU busy line.

PERCENTAGE 90%

90% CPU busy line. If the maximum CPU Usage percentage is less than 90%, then only 10% CPU busy line is displayed.

Time Zone Summary table

This table displays the time zone summary information for a selected server and contains the following elements:

TIME ZONE (REPORT)

The time zone used when displaying the report. The format is UTC -5:00.

START DATE TIME (REPORT)

The start date and time in reporting the time zone. The start date and time is the latest time between the start date and time in the prompt and the earliest date and time of available data in the database.

END DATE TIME (REPORT)

The end date and time in reporting the time zone. The start date and time is the earliest time between the end date and time in the prompt and the latest date and time of available data in the database.

TIME ZONE (SERVER OS)

The local time of the selected server. The format is GMT -5:00

Important: the server time zone is the time zone of the server OS and not the time of where the server is physically located.

START DATE TIME (SERVER OS)

The start date and time in the selected server local time zone. The start date and time is the latest time between the start date and the time in the prompt and the earliest date and time of available data in the database.

END DATE TIME (SERVER OS)

The end date and time in the selected server local time zone. The end date and time is the earliest time between the end date and the time in the prompt and the latest date and time of available data in the database.

Parent topic: Report and workspace reference

CPU: Linux for System z - CPU Usage This report shows the percentage of CPU used from both a virtual and physical perspective. reportsLinux for System z - CPU UsageCPU reportsLinux for System z - CPU UsageLinux for System z - CPU Usage exampleLinux for System z - CPU Usage example The server might be virtualized or it might be a dedicated physical server. The view that shows both virtual and physical lets you know what type of server it is and how its resources are being used.Source table[Presentation View].[Linux for System z DIST Date][Presentation View].[Linux for System z DIST Hour][Presentation View].[Linux for System z DIST Time][Presentation View].[Linux for System z CPU Fact][Presentation View].[Linux for System z Image]Drill through to the following reportMemory: Linux for System z - Memory UsageThis report shows the installed (visible) memory, free physical memory, and free virtual memory on the server.Standard prompts:date rangetime rangeinterval bandtime zoneshowReport-specific promptsServer NameThe Linux for System z - CPU Usage report contains the following information:server nameThe name of the server against which the report is run.VIRT CPU % BUSY The percentage of CPU resources that are used by this operating system in relation to the CPU capacity of the virtual server, without taking capping effects or competition with other partitions into account.PHYS CPU % BUSY The percentage of physical CPU resources that are used by this operating system.PERCENTAGE 10%10% CPU busy line.PERCENTAGE 90%90% CPU busy line. If the maximum CPU Usage percentage is less than 90%, then only 10% CPU busy line is displayed.Time Zone Summary tableThis table displays the time zone summary information for a selected server and contains the following elements:TIME ZONE (REPORT)The time zone used when displaying the report. The format is UTC -5:00.START DATE TIME (REPORT)The start date and time in reporting the time zone. The start date and time is the latest time between the start date and time in the prompt and the earliest date and time of available data in the database.END DATE TIME (REPORT)The end date and time in reporting the time zone. The start date and time is the earliest time between the end date and time in the prompt and the latest date and time of available data in the database.TIME ZONE (SERVER OS)The local time of the selected server. The format is GMT -5:00the server time zone is the time zone of the server OS and not the time of where the server is physically located.START DATE TIME (SERVER OS)The start date and time in the selected server local time zone. The start date and time is the latest time between the start date and the time in the prompt and the earliest date and time of available data in the database.END DATE TIME (SERVER OS)The end date and time in the selected server local time zone. The end date and time is the earliest time between the end date and the time in the prompt and the latest date and time of available data in the database.Report and workspace referenceIBM Capacity Management Analytics provides several predefined reports and workspaces that let you monitor and analyze the capacity of your IT services and the IT infrastructure.

CPU: Linux for System z - CPU Usage

This report shows the percentage of CPU used from both a virtual and physical perspective.

Figure 33. Linux for System z - CPU Usage example

The server might be virtualized or it might be a dedicated physical server. The view that shows both virtual and physical lets you know what type of server it is and how its resources are being used.

Source table

[Presentation View].[Linux for System z® DIST Date]

[Presentation View].[Linux for System z DIST Hour]

[Presentation View].[Linux for System z DIST Time]

[Presentation View].[Linux for System z CPU Fact]

[Presentation View].[Linux for System z Image]

Drill through to the following report

Memory: Linux for System z - Memory Usage

Standard prompts:

date range

time range

interval band

time zone

show

Report-specific prompts

Server Name

The Linux for System z - CPU Usage report contains the following information:

server name

The name of the server against which the report is run.

VIRT CPU % BUSY

The percentage of CPU resources that are used by this operating system in relation to the CPU capacity of the virtual server, without taking capping effects or competition with other partitions into account.

PHYS CPU % BUSY

The percentage of physical CPU resources that are used by this operating system.

PERCENTAGE 10%

10% CPU busy line.

PERCENTAGE 90%

90% CPU busy line. If the maximum CPU Usage percentage is less than 90%, then only 10% CPU busy line is displayed.

Time Zone Summary table

This table displays the time zone summary information for a selected server and contains the following elements:

TIME ZONE (REPORT)

The time zone used when displaying the report. The format is UTC -5:00.

START DATE TIME (REPORT)

The start date and time in reporting the time zone. The start date and time is the latest time between the start date and time in the prompt and the earliest date and time of available data in the database.

END DATE TIME (REPORT)

The end date and time in reporting the time zone. The start date and time is the earliest time between the end date and time in the prompt and the latest date and time of available data in the database.

TIME ZONE (SERVER OS)

The local time of the selected server. The format is GMT -5:00

Important: the server time zone is the time zone of the server OS and not the time of where the server is physically located.

START DATE TIME (SERVER OS)

The start date and time in the selected server local time zone. The start date and time is the latest time between the start date and the time in the prompt and the earliest date and time of available data in the database.

END DATE TIME (SERVER OS)

The end date and time in the selected server local time zone. The end date and time is the earliest time between the end date and the time in the prompt and the latest date and time of available data in the database.

Parent topic: Report and workspace reference

CPU: LPAR Weight Optimization Run ResultThis report shows the available LPAR Weight Optimization runs. It also shows the input settings and optimized weight results for a run selected for each CPC and processor type.reportsLPAR Weight Optimization Run ResultCPU reportsLPAR Weight Optimization Run ResultCPU: LPAR Weight Optimization Run Result exampleCPU: LPAR Weight Optimization Run Result exampleSource tables[Presentation View].[Optimization Metadata][Presentation View].[LPAR Weight Optimization Metrics][Presentation View].[LPAR Weight Optimization Parameters][Presentation View].[LPAR Weight Optimization Detail]Drill through to the following reportsCPU: MIPS Used - LPAR Level by WLM ImportanceThis report shows the observed average or peak CPU MIPS used at the LPAR level and the forecasted average or peak with their confidence intervals. The CPU MIPS used (both observed and forecasted) can be displayed for each processor type at each interval band. The observed CPU MIPS used is also broken down by the WLM importance level. The optimized LPAR weight and the target workload, according to the settings in the selected optimization run, can be also displayed.CPU: Over/Under Share Weight - CPC by LPARThis report shows the stacked usage of each LPAR against the total number of engines of each type in the shared pool of a CPC. It also shows the usage percentage over or under its guaranteed share of each LPAR, according to both the observed and optimized weights, due to the competition among LPARs. The target workload, according to the selected optimization run, can also be displayed.Report-specific promptscpu serial noprocessor typeoptimization runThe Optimization Metadata table contains the following information:OPTIMIZATION RUNThe name that is used for the execution of the LPAR weight optimization.MODEL VERSIONThe version of the optimization model.HOUR STARTThe start hour of the dates that are used in the LPAR weight optimization run.HOUR ENDThe end hour of the dates that are used in the LPAR weight optimization run.EXECUTION TIMESTAMPThe execution start time of this optimization run.EXECUTION DURATIONThe length of time that the optimization run took to run.OBJECTIVEThe total objective value of this optimization model.The LPAR Weight Optimization Run - LPAR Information table contains the following information:LPAR NAMEThe name of the LPAR.LPAR PRIORITYThe priority of the LPAR. Higher numbers mean that an LPAR has a higher priority.FIXED WEIGHTThe fixed weight value for the LPAR, if specified. If the value is N/A, it means that the weight is not fixed and it is optimized by the LPAR weight optimization run.TARGET WORKLOAD IMPORTANCE LEVELThe importance level of the target workload’s boundary of this LPAR. A NULL value means that the WLM importance level does not need to be considered. The target workload is considered to be the total workload multiplied by the value of the TARGET WORKLOAD IMPORTANCE LEVEL % column. A value of 0 to 6 means that the workloads under the importance level number are considered as the target workload for this LPAR. Also, it means that a certain percent (the value of the TARGET WORKLOAD IMPORTANCE LEVEL % column) of the importance level is considered as the target workload for this LPAR.TARGET WORKLOAD IMPORTANCE LEVEL %The percentage that is used in the target workload calculation. When TARGET WORKLOAD IMPORTANCE LEVEL is null, then it is the percentage of the total workload that should be considered as the target workload. When TARGET WORKLOAD IMPORTANCE LEVEL is a number from 0 to 6, then it is the percentage of the TARGET WORKLOAD IMPORTANCE LEVEL workload that should be considered as the target workload.OBSERVED WEIGHTThe average observed weight of the dates and hour range considered in the optimization run.OPTIMIZED WEIGHTThe optimized weight that is the result from the LPAR Weight Optimization run.MAX OVER TARGET WORKLOADThe maximum value that the target workload is over the optimized weight for this LPAR. The value is a percentage of the capacity of the shared pool of this processor type.MAX OVER TOTAL WORKLOADThe maximum value that the total workload is over the optimized weight for this LPAR. The value is a percentage of the capacity of the shared pool of this processor type.SUM OVER TARGET WORKLOADThe sum of the amount that the target workload is over the optimized weight for each time interval for this LPAR. The value is a percentage of the capacity of the shared pool of this processor type. Because it is a sum value for each time interval, it is possible that the value can be larger than 100%.SUM OVER TOTAL WORKLOADThe sum of the amount that the total workload is over the optimized weight for each time interval for this LPAR. The value is a percentage of the capacity of the shared pool of this processor type. Because it is a sum value for each time interval, it is possible that the value can be larger than 100%.The LPAR Weight Optimization Run – Dates Information table contains the following information.DATEThe dates used for the optimization run.The LPAR Weight Optimization Run – Sub Objectives Information table contains the following information.SUB OBJECTIVE NAMEThe name of each sub objective for the LPAR Weight Optimization model.SUB OBJECTIVE WEIGHTThe weight or punishment value for each sub objective for the optimization run.Report and workspace referenceIBM Capacity Management Analytics provides several predefined reports and workspaces that let you monitor and analyze the capacity of your IT services and the IT infrastructure.

CPU: LPAR Weight Optimization Run Result

This report shows the available LPAR Weight Optimization runs. It also shows the input settings and optimized weight results for a run selected for each CPC and processor type.

Figure 34. CPU: LPAR Weight Optimization Run Result example

Source tables

[Presentation View].[Optimization Metadata]

[Presentation View].[LPAR Weight Optimization Metrics]

[Presentation View].[LPAR Weight Optimization Parameters]

[Presentation View].[LPAR Weight Optimization Detail]

Drill through to the following reports

CPU: MIPS Used - LPAR Level by WLM Importance

CPU: Over/Under Share Weight - CPC by LPAR

Report-specific prompts

cpu serial no

processor type

optimization run

The Optimization Metadata table contains the following information:

OPTIMIZATION RUN

The name that is used for the execution of the LPAR weight optimization.

MODEL VERSION

The version of the optimization model.

HOUR START

The start hour of the dates that are used in the LPAR weight optimization run.

HOUR END

The end hour of the dates that are used in the LPAR weight optimization run.

EXECUTION TIMESTAMP

The execution start time of this optimization run.

EXECUTION DURATION

The length of time that the optimization run took to run.

OBJECTIVE

The total objective value of this optimization model.

The LPAR Weight Optimization Run - LPAR Information table contains the following information:

LPAR NAME

The name of the LPAR.

LPAR PRIORITY

The priority of the LPAR. Higher numbers mean that an LPAR has a higher priority.

FIXED WEIGHT

The fixed weight value for the LPAR, if specified. If the value is "N/A", it means that the weight is not fixed and it is optimized by the LPAR weight optimization run.

TARGET WORKLOAD IMPORTANCE LEVEL

The importance level of the target workload’s boundary of this LPAR.

A NULL value means that the WLM importance level does not need to be considered. The target workload is considered to be the total workload multiplied by the value of the TARGET WORKLOAD IMPORTANCE LEVEL % column.

A value of 0 to 6 means that the workloads under the importance level number are considered as the target workload for this LPAR. Also, it means that a certain percent (the value of the TARGET WORKLOAD IMPORTANCE LEVEL % column) of the importance level is considered as the target workload for this LPAR.

TARGET WORKLOAD IMPORTANCE LEVEL %

The percentage that is used in the target workload calculation.

When TARGET WORKLOAD IMPORTANCE LEVEL is null, then it is the percentage of the total workload that should be considered as the target workload.

When TARGET WORKLOAD IMPORTANCE LEVEL is a number from 0 to 6, then it is the percentage of the TARGET WORKLOAD IMPORTANCE LEVEL workload that should be considered as the target workload.

OBSERVED WEIGHT

The average observed weight of the dates and hour range considered in the optimization run.

OPTIMIZED WEIGHT

The optimized weight that is the result from the LPAR Weight Optimization run.

MAX OVER TARGET WORKLOAD

The maximum value that the target workload is over the optimized weight for this LPAR. The value is a percentage of the capacity of the shared pool of this processor type.

MAX OVER TOTAL WORKLOAD

The maximum value that the total workload is over the optimized weight for this LPAR. The value is a percentage of the capacity of the shared pool of this processor type.

SUM OVER TARGET WORKLOAD

The sum of the amount that the target workload is over the optimized weight for each time interval for this LPAR. The value is a percentage of the capacity of the shared pool of this processor type. Because it is a sum value for each time interval, it is possible that the value can be larger than 100%.

SUM OVER TOTAL WORKLOAD

The sum of the amount that the total workload is over the optimized weight for each time interval for this LPAR. The value is a percentage of the capacity of the shared pool of this processor type. Because it is a sum value for each time interval, it is possible that the value can be larger than 100%.

The LPAR Weight Optimization Run – Dates Information table contains the following information.

DATE

The dates used for the optimization run.

The LPAR Weight Optimization Run – Sub Objectives Information table contains the following information.

SUB OBJECTIVE NAME

The name of each sub objective for the LPAR Weight Optimization model.

SUB OBJECTIVE WEIGHT

The weight or punishment value for each sub objective for the optimization run.

Parent topic: Report and workspace reference

CPU: MIPS Used - Service Class Period Level This report provides a comprehensive view of all of the applications that are running across the entire systems landscape. This helps you quickly rebalance system allocation and to focus on the most critical applications.reportsMIPS Used - Service Class Period LevelCPU reportsMIPS Used - Service Class Period Level This report helps you assess whether you might need to purchase more capacity, and when that extra capacity is needed. However, what might appear as running out of capacity, might not necessarily mean that more capacity is needed—it does mean that you must review your usage and ensure that you are optimizing your usage effectively. The objective is to run 100% busy 24/7. The key to achieving that objective is ensuring that you can prioritize your workloads and move applications around efficiently. For example, scheduling lower priority jobs for off-peak hours. This ensures that the priority applications have the capacity they need when they need it.This report analyzes the workloads (service classes) that are driving CPU usage on a system by showing MIPS used at the Workload Manager service class period level. The total CPU time that is used by all WLM service class periods on a system is known as captured CPU time.For CPs, the zIIP / zAAP eligible MIPS are also shown. For zIIP / zAAP, the MIPS that were eligible are also shown.MIPS Used - Service Class Period Level example MIPS Used - Service Class Period Level chart exampleSource table[Presentation View].[LPAR Level Metrics][Presentation View].[WLM Service Class Period Usage/Delay Metrics]Drill through to the following reportsCPU: MIPS Used - zServer/LPAR LevelThis report shows the observed average or peak CPU MIPS used at the zServer/CPC level and the forecasted average or peak CPU MIPS used along with their confidence interval. The average CPU MIPS used (observed and forecasted) and peak CPU MIPS used (observed and forecasted) can be displayed for each processor type (CP, zIIP, zAAP, IFL, or ICF) at each interval band and is broken down to the LPAR level. The forecasted peak CPU MIPS used can be displayed for each processor type at each interval band on the CPC level.CPU: MIPS Used - System Level (Captured vs Uncaptured)This report provides insights into potential MIPS overages. This can help you to determine the need to tune and adjust your system processes and avoid overcapacity by allocating MIPS back to business applications. Aside from the potential cost savings, a regular assessment, based on this report, can help you maintain your SLA by keeping up the performance and availability level.CPU: MIPS Used - LPAR Level by WLM ImportanceThis report shows the observed average or peak CPU MIPS used at the LPAR level and the forecasted average or peak with their confidence intervals. The CPU MIPS used (both observed and forecasted) can be displayed for each processor type at each interval band. The observed CPU MIPS used is also broken down by the WLM importance level. The optimized LPAR weight and the target workload, according to the settings in the selected optimization run, can be also displayed.CPU: Over/Under Share Weight - CPC by LPARThis report shows the stacked usage of each LPAR against the total number of engines of each type in the shared pool of a CPC. It also shows the usage percentage over or under its guaranteed share of each LPAR, according to both the observed and optimized weights, due to the competition among LPARs. The target workload, according to the selected optimization run, can also be displayed.Standard prompts:date rangetime rangeinterval bandshowReport-specific promptscpu serial noprocessor typelpar nameserviceclass periodThis report contains the following information:CPU SERIAL NOThe zServer/CEC serial number.processor typeThe engine type: CP, zIIP, or zAAP. The list contains only installed processor types.lpar nameThe name of the logical partition to which the specified processor type is assigned.LOGIC PROC MIPSThe logical processors MIPS value is the MIPS capacity of the logical processors that are assigned to this LPAR If this value is double the maximum of the total MIPS usage, then this line does not appear in the chart. Showing the line would make the chart appear small.WEIGHT %The per cent of the shared pool that is guaranteed to the LPAR, if the LPAR uses shared processors. If the LPAR uses dedicated processors, then the value is shown as N/A.GUARANTEED MIPSThe guaranteed capacity in MIPS on this LPAR. The value is calculated according to the weight assigned to this LPAR. If dedicated processors are assigned to this LPAR, then this line does not appear in the chart and the value is shown as N/A in the table. If this value is double the maximum of the total MIPS usage, then this line does not appear in the chart. Showing the line would make the chart appear small.serviceclass periodThe WLM service class name and period. The concatenation of columns SERVICE_CLASS and SERV_CLASS_PERIOD.CAPTURED MIPS USEDThe CPU time that is consumed by serviceclass_period, converted to MIPS used.zIIP ON CP MIPSThe CPU time that is used for IIP on CP processing, converted to MIPS used.zAAP ON CP MIPSThe CPU time that is used for IFA on CP processing, converted to MIPS used.zIIP ELIGIBLE MIPSThe CPU time that is used for IIP on CP processing, converted to MIPS used.zAAP ELIGIBLE MIPSThe CPU time that is used for IFA on CP processing, converted to MIPS used.Report and workspace referenceIBM Capacity Management Analytics provides several predefined reports and workspaces that let you monitor and analyze the capacity of your IT services and the IT infrastructure.

CPU: MIPS Used - Service Class Period Level

This report provides a comprehensive view of all of the applications that are running across the entire systems landscape. This helps you quickly rebalance system allocation and to focus on the most critical applications.

This report helps you assess whether you might need to purchase more capacity, and when that extra capacity is needed. However, what might appear as running out of capacity, might not necessarily mean that more capacity is needed—it does mean that you must review your usage and ensure that you are optimizing your usage effectively. The objective is to run 100% busy 24/7. The key to achieving that objective is ensuring that you can prioritize your workloads and move applications around efficiently. For example, scheduling lower priority jobs for off-peak hours. This ensures that the priority applications have the capacity they need when they need it.

This report analyzes the workloads (service classes) that are driving CPU usage on a system by showing MIPS used at the Workload Manager service class period level. The total CPU time that is used by all WLM service class periods on a system is known as captured CPU time.

For CPs, the zIIP / zAAP eligible MIPS are also shown. For zIIP / zAAP, the MIPS that were eligible are also shown.

Figure 35. MIPS Used - Service Class Period Level example

Source table

[Presentation View].[LPAR Level Metrics]

[Presentation View].[WLM Service Class Period Usage/Delay Metrics]

Drill through to the following reports

CPU: MIPS Used - zServer/LPAR Level

CPU: MIPS Used - System Level (Captured vs Uncaptured)

CPU: MIPS Used - LPAR Level by WLM Importance

CPU: Over/Under Share Weight - CPC by LPAR

Standard prompts:

date range

time range

interval band

show

Report-specific prompts

cpu serial no

processor type

lpar name

serviceclass period

This report contains the following information:

CPU SERIAL NO

The zServer/CEC serial number.

processor type

The engine type: CP, zIIP, or zAAP. The list contains only installed processor types.

lpar name

The name of the logical partition to which the specified processor type is assigned.

LOGIC PROC MIPS

The logical processors MIPS value is the MIPS capacity of the logical processors that are assigned to this LPAR

If this value is double the maximum of the total MIPS usage, then this line does not appear in the chart. Showing the line would make the chart appear small.

WEIGHT %

The per cent of the shared pool that is guaranteed to the LPAR, if the LPAR uses shared processors. If the LPAR uses dedicated processors, then the value is shown as N/A.

GUARANTEED MIPS

The guaranteed capacity in MIPS on this LPAR. The value is calculated according to the weight assigned to this LPAR.

If dedicated processors are assigned to this LPAR, then this line does not appear in the chart and the value is shown as N/A in the table.

If this value is double the maximum of the total MIPS usage, then this line does not appear in the chart. Showing the line would make the chart appear small.

serviceclass period

The WLM service class name and period. The concatenation of columns SERVICE_CLASS and SERV_CLASS_PERIOD.

CAPTURED MIPS USED

The CPU time that is consumed by serviceclass_period, converted to MIPS used.

zIIP ON CP MIPS

The CPU time that is used for IIP on CP processing, converted to MIPS used.

zAAP ON CP MIPS

The CPU time that is used for IFA on CP processing, converted to MIPS used.

zIIP ELIGIBLE MIPS

The CPU time that is used for IIP on CP processing, converted to MIPS used.

zAAP ELIGIBLE MIPS

The CPU time that is used for IFA on CP processing, converted to MIPS used.

Parent topic: Report and workspace reference

CPU: MIPS Used - System Level (Captured vs Uncaptured)This report provides insights into potential MIPS overages. This can help you to determine the need to tune and adjust your system processes and avoid overcapacity by allocating MIPS back to business applications. Aside from the potential cost savings, a regular assessment, based on this report, can help you maintain your SLA by keeping up the performance and availability level.reportsMIPS Used - System Level (Captured vs Uncaptured)CPU reportsMIPS Used - System Level (Captured vs Uncaptured)This report analyzes a system’s capture ratio to determine whether the CPU time that is used by system-related processes (uncaptured CPU time) is too high. It shows both the captured and uncaptured CPU MIPS used at the system level. It also shows the system's capture ratio. A notable increase or decrease in a system's capture ratio can identify a system problem. It is recommended that you closely monitor a system's capture ratio after you apply any CPU hardware changes, major operating system upgrades, or software upgrades.MIPS Used - System Level (Captured vs Uncaptured) exampleMIPS Used - System Level (Captured vs Uncaptured) example report.Source tables[Presentation View].[LPAR Level Metrics][Presentation View].[WLM Service Class Period Usage/Delay Metrics]Drill through to the following reportsCPU: MIPS Used - zServer/LPAR LevelThis report shows the observed average or peak CPU MIPS used at the zServer/CPC level and the forecasted average or peak CPU MIPS used along with their confidence interval. The average CPU MIPS used (observed and forecasted) and peak CPU MIPS used (observed and forecasted) can be displayed for each processor type (CP, zIIP, zAAP, IFL, or ICF) at each interval band and is broken down to the LPAR level. The forecasted peak CPU MIPS used can be displayed for each processor type at each interval band on the CPC level.CPU: MIPS Used - LPAR Level by WLM ImportanceThis report shows the observed average or peak CPU MIPS used at the LPAR level and the forecasted average or peak with their confidence intervals. The CPU MIPS used (both observed and forecasted) can be displayed for each processor type at each interval band. The observed CPU MIPS used is also broken down by the WLM importance level. The optimized LPAR weight and the target workload, according to the settings in the selected optimization run, can be also displayed.CPU: MIPS Used - Service Class Period Level This report provides a comprehensive view of all of the applications that are running across the entire systems landscape. This helps you quickly rebalance system allocation and to focus on the most critical applications.CPU: Over/Under Share Weight - CPC by LPARThis report shows the stacked usage of each LPAR against the total number of engines of each type in the shared pool of a CPC. It also shows the usage percentage over or under its guaranteed share of each LPAR, according to both the observed and optimized weights, due to the competition among LPARs. The target workload, according to the selected optimization run, can also be displayed.Standard prompts:date rangetime rangeinterval bandshowReport-specific promptscpu serial noprocessor typelpar nameThe CPU: MIPS Used - System Level (Captured vs Uncaptured) report contains the following information:cpu serial noThe zServer/CEC serial number.processor typeThe engine type: CP, zIIP, or zAAP. The list contains only installed processor types.systemThe z/OS system id.LPAR MGMT MIPS USEDThe difference between LPAR Dispatch CPU Time and LPAR Effective Dispatch CPU Time, converted to MIPS used.CAPTURED MIPS USEDThe total CPU time that is captured in the Type72 workload data, converted to MIPS used.UNCAPTURED MIPS USEDThe difference between LPAR Effective Dispatch CPU Time and the total CPU time that is captured in the Type72 workload data, converted to MIPS used.CAPTURE RATIO (CR)The ratio of total CPU time that is captured in the Type72 workload data to LPAR Dispatch CPU Time.CR - WarningThe capture ratio warning threshold line = 0.90.CR- CriticalThe capture ratio critical threshold line = 0.85.LOGIC PROC MIPSThe logical processors MIPS value is the MIPS capacity of the logical processors that are assigned to this LPAR If this value is double the maximum of the total MIPS usage, then this line does not appear in the chart. Showing the line would make the chart appear small.WEIGHT %The per cent of the shared pool that is guaranteed to the LPAR, if the LPAR uses shared processors. If the LPAR uses dedicated processors, then the value is shown as N/A.GUARANTEED MIPSThe guaranteed capacity in MIPS on this LPAR. The value is calculated according to the weight assigned to this LPAR. If dedicated processors are assigned to this LPAR, then this line does not appear in the chart and the value is shown as N/A in the table. If this value is double the maximum of the total MIPS usage, then this line does not appear in the chart. Showing the line would make the chart appear small.Report and workspace referenceIBM Capacity Management Analytics provides several predefined reports and workspaces that let you monitor and analyze the capacity of your IT services and the IT infrastructure.

CPU: MIPS Used - System Level (Captured vs Uncaptured)

This report provides insights into potential MIPS overages. This can help you to determine the need to tune and adjust your system processes and avoid overcapacity by allocating MIPS back to business applications. Aside from the potential cost savings, a regular assessment, based on this report, can help you maintain your SLA by keeping up the performance and availability level.

This report analyzes a system’s capture ratio to determine whether the CPU time that is used by system-related processes (uncaptured CPU time) is too high. It shows both the captured and uncaptured CPU MIPS used at the system level. It also shows the system's capture ratio. A notable increase or decrease in a system's capture ratio can identify a system problem. It is recommended that you closely monitor a system's capture ratio after you apply any CPU hardware changes, major operating system upgrades, or software upgrades.

Figure 36. MIPS Used - System Level (Captured vs Uncaptured) example

Source tables

[Presentation View].[LPAR Level Metrics]

[Presentation View].[WLM Service Class Period Usage/Delay Metrics]

Drill through to the following reports

CPU: MIPS Used - zServer/LPAR Level

CPU: MIPS Used - LPAR Level by WLM Importance

CPU: MIPS Used - Service Class Period Level

CPU: Over/Under Share Weight - CPC by LPAR

Standard prompts:

date range

time range

interval band

show

Report-specific prompts

cpu serial no

processor type

lpar name

The CPU: MIPS Used - System Level (Captured vs Uncaptured) report contains the following information:

cpu serial no

The zServer/CEC serial number.

processor type

The engine type: CP, zIIP, or zAAP. The list contains only installed processor types.

system

The z/OS system id.

LPAR MGMT MIPS USED

The difference between LPAR Dispatch CPU Time and LPAR Effective Dispatch CPU Time, converted to MIPS used.

CAPTURED MIPS USED

The total CPU time that is captured in the Type72 workload data, converted to MIPS used.

UNCAPTURED MIPS USED

The difference between LPAR Effective Dispatch CPU Time and the total CPU time that is captured in the Type72 workload data, converted to MIPS used.

CAPTURE RATIO (CR)

The ratio of total CPU time that is captured in the Type72 workload data to LPAR Dispatch CPU Time.

CR - Warning

The capture ratio warning threshold line = 0.90.

CR- Critical

The capture ratio critical threshold line = 0.85.

LOGIC PROC MIPS

The logical processors MIPS value is the MIPS capacity of the logical processors that are assigned to this LPAR

If this value is double the maximum of the total MIPS usage, then this line does not appear in the chart. Showing the line would make the chart appear small.

WEIGHT %

The per cent of the shared pool that is guaranteed to the LPAR, if the LPAR uses shared processors. If the LPAR uses dedicated processors, then the value is shown as N/A.

GUARANTEED MIPS

The guaranteed capacity in MIPS on this LPAR. The value is calculated according to the weight assigned to this LPAR.

If dedicated processors are assigned to this LPAR, then this line does not appear in the chart and the value is shown as N/A in the table.

If this value is double the maximum of the total MIPS usage, then this line does not appear in the chart. Showing the line would make the chart appear small.

Parent topic: Report and workspace reference

CPU: MIPS Used - zServer/LPAR LevelThis report shows the observed average or peak CPU MIPS used at the zServer/CPC level and the forecasted average or peak CPU MIPS used along with their confidence interval. The average CPU MIPS used (observed and forecasted) and peak CPU MIPS used (observed and forecasted) can be displayed for each processor type (CP, zIIP, zAAP, IFL, or ICF) at each interval band and is broken down to the LPAR level. The forecasted peak CPU MIPS used can be displayed for each processor type at each interval band on the CPC level.reportsMIPS Used - zServer/LPAR LevelCPU reportsMIPS Used - zServer/LPAR LevelMIPS Used - zServer/LPAR Level exampleMIPS Used - zServer/LPAR Level example report.Source table[Presentation View].[LPAR Level Metrics][Presentation View].[LPAR Level Forecast Metrics][Presentation View].[CPC Level Forecast Metrics]Drill through to the following reportsCPU: MIPS Used - LPAR Level by WLM ImportanceThis report shows the observed average or peak CPU MIPS used at the LPAR level and the forecasted average or peak with their confidence intervals. The CPU MIPS used (both observed and forecasted) can be displayed for each processor type at each interval band. The observed CPU MIPS used is also broken down by the WLM importance level. The optimized LPAR weight and the target workload, according to the settings in the selected optimization run, can be also displayed.CPU: MIPS Used - System Level (Captured vs Uncaptured)This report provides insights into potential MIPS overages. This can help you to determine the need to tune and adjust your system processes and avoid overcapacity by allocating MIPS back to business applications. Aside from the potential cost savings, a regular assessment, based on this report, can help you maintain your SLA by keeping up the performance and availability level.CPU: MIPS Used - Service Class Period Level This report provides a comprehensive view of all of the applications that are running across the entire systems landscape. This helps you quickly rebalance system allocation and to focus on the most critical applications.CPU: Over/Under Share Weight - CPC by LPARThis report shows the stacked usage of each LPAR against the total number of engines of each type in the shared pool of a CPC. It also shows the usage percentage over or under its guaranteed share of each LPAR, according to both the observed and optimized weights, due to the competition among LPARs. The target workload, according to the selected optimization run, can also be displayed.Standard prompts:date rangetime rangeinterval band (If an interval band of less than one day is selected, and peak usage is shown, then the calculation uses an interval band of one day for the calculation.)showReport-specific promptsshow forecastcpu serial noprocessor typecalculation method There are three calculation methods available: Average, Highest Hourly Average, and Percentile of SMF Interval. If show forecast is selected, then Percentile of SMF Interval is not available.calculation value This prompt is shown only if you select Highest Hourly Average or Percentile of SMF Interval as the calculation method. If you select Highest Hourly Average, the values are: Highest Hourly Average2nd Highest Hourly Average3rd Highest Hourly Average4th Highest Hourly Average5th Highest Hourly Average If you select Percentile of SMF Interval, the values are: 100%ile of SMF Interval95%ile of SMF Interval90%ile of SMF Interval85%ile of SMF Interval80%ile of SMF Interval75%ile of SMF Interval70%ile of SMF Intervallpar name If Highest Hourly Average is selected as the calculation method, then lpar name is not shown.The Observed Average MIPS Used - zServer/LPAR Level and Observed Peak MIPS Used -zServer/LPAR Level charts or tables contain the following information. If show forecast is cleared, then only observed values are displayed.cpu serial noThe zServer/CEC serial number.processor typeThe engine type: CP, zIIP, zAAP, IFL, or ICF. The list contains only installed processor types.lpar nameThe name of the logical partition to which the specified processor type is assigned.DATE BANDThe start date of the time interval.TIME BANDThe start time of the time interval.This value displays only when the calculation method is Average.PEAK DATEThe date when the peak period occurs.This value is displayed only when the calculation method is Highest Hourly Average or Percentile of SMF Interval.PEAK TIMEThe start time of the peak period.This value is displayed only when the calculation method is Highest Hourly Average or Percentile of SMF Interval.MIPS Capacity The total MIPS capacity available for the selected processor type. If MIPS capacity has changed during the time band, then the maximum value is displayed. In this case, there is a "(ii)" marker behind the MIPS Capacity value and a footnote is displayed on the top of this table stating: "(ii) the value is the maximum value during the time band". If this value is double the maximum of the total MIPS usage, then this line does not appear in the chart. Showing the line would make the chart appear small. 90% MIPS Capacity The 90% MIPS capacity line (warning) for the selected processor type. 90% MIPS Capacity is displayed only if MIPS Capacity is also displayed. MIPS Used The amount of available relative processor capacity consumed for the selected processor type. If an LPAR is assigned with dedicated processors, and this LPAR does not collect SMF data, then CPU_DISPATCH_SEC is used which shows the dedicated engine's MIPS capacity but not the real utilization of this LPAR. In this case, there is a "(i)" marker behind the MIPS Used value and a footnote is displayed on the top of this table stating: (i) the value is calculated by CPU_DISPATCH_SEC collected from other LPAR because this LPAR uses dedicated processors and does not collect SMF data. FORECASTEDThe total forecasted MIPS used for the selected processor type. This metric is derived from the CPU forecast model (SPSS).The Forecasted Average MIPS Used - zServer/LPAR Level and the Observed and Forecasted Peak MIPS Used – zServer/LPAR Level charts and tables contain the information that is shown in the Observed Average MIPS Used - zServer/LPAR Level and Observed Peak MIPS Used - zServer/LPAR Level charts and tables as well as the following information. If show forecast is cleared, then only observed values are displayed.PEAK DATE (FORECASTED)The date when the forecasted peak of the time interval occurs.This value is displayed only when the calculation method is Highest Hourly Average or Percentile of Hourly Average.PEAK TIME (FORECASTED)The time when the forecasted peak of the time interval occurs.This value is displayed only when the calculation method is Highest Hourly Average or Percentile of Hourly Average.MIPS USED - TOTAL (OBSERVED)The total MIPS used for the selected processor type.If an LPAR is assigned with dedicated processors, and this LPAR does not collect SMF data, then CPU_DISPATCH_SEC is used which shows the dedicated engine's MIPS capacity but not the real utilization of this LPAR. In this case, there is a "(i)" marker by the MIPS Used value and a footnote is displayed on the top of this table stating: "(i) the value is calculated by CPU_DISPATCH_SEC collected from other LPAR because this LPAR uses dedicated processors and does not collect SMF data."MIPS USED (OBSERVED)The total MIPS used for the selected processor type.If an LPAR is assigned with dedicated processors, and this LPAR does not collect SMF data, then CPU_DISPATCH_SEC is used which shows the dedicated engine's MIPS capacity but not the real utilization of this LPAR. In this case, there is a "(i)" marker by the MIPS Used value and a footnote is displayed on the top of this table stating: "(i) the value is calculated by CPU_DISPATCH_SEC collected from other LPAR because this LPAR uses dedicated processors and does not collect SMF data."MIPS USED (FORECASTED)The total forecasted MIPS used for the selected processor type. This metric is derived from the CPU forecast model (SPSS).MIPS USED (FORECAST LOWER CONFIDENCE BOUND)The lower bound of the confidence interval for the MIPS USED forecasting.MIPS USED (FORECAST UPPER CONFIDENCE BOUND)The upper bound of the confidence interval for the MIPS USED forecasting.Values that are marked with an asterisk (iii) are adjusted values. The values were originally negative in the forecasting result and are adjusted to 0.Values that are marked with an asterisk (iv) are adjusted values. The values were originally extremes in the forecasted result and are adjusted to maximum forecasted value.The Forecast Metadata table contains the following information. This table is displayed when the show forecast prompt is selected and Highest Hourly Average is selected as calculation method.STREAM TYPEThe stream type that is used by the available forecast results set, for example lpar_mips_peak_daily. This means that the stream is for the LPAR MIPS forecast, and it forecasts the CPU seconds at the daily peak aggregation level.AGGREGATIONThe aggregation level of the available forecast results set. The values are: Highest Hourly Average of LPAR MIPS Daily, 2nd Highest Hourly Average of LPAR MIPS Daily, 3rd Highest Hourly Average of LPAR MIPS Daily, 4th Highest Hourly Average of LPAR MIPS Daily, and 5th Highest Hourly Average of LPAR MIPS Daily.HOUR STARTThe start hour of the forecasted hour range of the available forecast results set.HOUR ENDThe end hour of the forecasted hour range of the available forecast results set.HISTORY STARTThe start date of the based historical data range of the available forecast results set.HISTORY ENDThe end date of the based historical data range of the available forecast results set.FORECAST STARTThe start date of the available forecast results set.FORECAST ENDThe end date of the available forecast results set.EXECUTE TIMESTAMPThe forecast execution timestamp of the available forecast results set.MAPPING TIMESTAMPThe last updated timestamp of the mapping table on which the forecast is based.The rules to choose the forecast stream aggregation level when show forecast is selected are:If you select the Average calculation method: If you select daily, quarterly, or yearly, and a daily stream is available, then it is used. Otherwise, no data available is displayed.If you select monthly and a monthly stream is available, then it is used. Otherwise, no data available is displayed.If you select hourly and an hourly stream is available, then it is used. Otherwise, no data available is displayed. If you select the Highest Hourly Average calculation method: If you select Highest Hourly Average calculation value and a Highest Hourly Average of LPAR MIPS Daily stream is available, then it is used. Otherwise, no data available is displayed.If you select 2nd Highest Hourly Average calculation value and a 2nd Highest Hourly Average of LPAR MIPS Daily stream is available, then it is used. Otherwise, no data available is displayed.If you select 3rd Highest Hourly Average calculation value and a 3rd Highest Hourly Average of LPAR MIPS Daily stream is available, then it is used. Otherwise, no data available is displayed.If you select 4th Highest Hourly Average calculation value and a 4th Highest Hourly Average of LPAR MIPS Daily stream is available, then it is used. Otherwise, no data available is displayed.If you select 5th Highest Hourly Average calculation value and a 5th Highest Hourly Average of LPAR MIPS Daily stream is available, then it is used. Otherwise, no data available is displayed.Report and workspace referenceIBM Capacity Management Analytics provides several predefined reports and workspaces that let you monitor and analyze the capacity of your IT services and the IT infrastructure.

CPU: MIPS Used - zServer/LPAR Level

This report shows the observed average or peak CPU MIPS used at the zServer/CPC level and the forecasted average or peak CPU MIPS used along with their confidence interval. The average CPU MIPS used (observed and forecasted) and peak CPU MIPS used (observed and forecasted) can be displayed for each processor type (CP, zIIP, zAAP, IFL, or ICF) at each interval band and is broken down to the LPAR level. The forecasted peak CPU MIPS used can be displayed for each processor type at each interval band on the CPC level.

Figure 37. MIPS Used - zServer/LPAR Level example

Source table

[Presentation View].[LPAR Level Metrics]

[Presentation View].[LPAR Level Forecast Metrics]

[Presentation View].[CPC Level Forecast Metrics]

Drill through to the following reports

CPU: MIPS Used - LPAR Level by WLM Importance

CPU: MIPS Used - System Level (Captured vs Uncaptured)

CPU: MIPS Used - Service Class Period Level

CPU: Over/Under Share Weight - CPC by LPAR

Standard prompts:

date range

time range

interval band (If an interval band of less than one day is selected, and peak usage is shown, then the calculation uses an interval band of one day for the calculation.)

show

Report-specific prompts

show forecast

cpu serial no

processor type

calculation method

There are three calculation methods available: Average, Highest Hourly Average, and Percentile of SMF Interval.

If show forecast is selected, then Percentile of SMF Interval is not available.

calculation value

This prompt is shown only if you select Highest Hourly Average or Percentile of SMF Interval as the calculation method.

If you select Highest Hourly Average, the values are:

Highest Hourly Average

2nd Highest Hourly Average

3rd Highest Hourly Average

4th Highest Hourly Average

5th Highest Hourly Average

If you select Percentile of SMF Interval, the values are:

100%ile of SMF Interval

95%ile of SMF Interval

90%ile of SMF Interval

85%ile of SMF Interval

80%ile of SMF Interval

75%ile of SMF Interval

70%ile of SMF Interval

lpar name

If Highest Hourly Average is selected as the calculation method, then lpar name is not shown.

The Observed Average MIPS Used - zServer/LPAR Level and Observed Peak MIPS Used -zServer/LPAR Level charts or tables contain the following information. If show forecast is cleared, then only observed values are displayed.

cpu serial no

The zServer/CEC serial number.

processor type

The engine type: CP, zIIP, zAAP, IFL, or ICF. The list contains only installed processor types.

lpar name

The name of the logical partition to which the specified processor type is assigned.

DATE BAND

The start date of the time interval.

TIME BAND

The start time of the time interval.

This value displays only when the calculation method is Average.

PEAK DATE

The date when the peak period occurs.

This value is displayed only when the calculation method is Highest Hourly Average or Percentile of SMF Interval.

PEAK TIME

The start time of the peak period.

This value is displayed only when the calculation method is Highest Hourly Average or Percentile of SMF Interval.

MIPS Capacity

The total MIPS capacity available for the selected processor type.

If MIPS capacity has changed during the time band, then the maximum value is displayed. In this case, there is a "(ii)" marker behind the MIPS Capacity value and a footnote is displayed on the top of this table stating: "(ii) the value is the maximum value during the time band".

If this value is double the maximum of the total MIPS usage, then this line does not appear in the chart. Showing the line would make the chart appear small.

90% MIPS Capacity

The 90% MIPS capacity line (warning) for the selected processor type.

90% MIPS Capacity is displayed only if MIPS Capacity is also displayed.

MIPS Used

The amount of available relative processor capacity consumed for the selected processor type.

If an LPAR is assigned with dedicated processors, and this LPAR does not collect SMF data, then CPU_DISPATCH_SEC is used which shows the dedicated engine's MIPS capacity but not the real utilization of this LPAR. In this case, there is a "(i)" marker behind the MIPS Used value and a footnote is displayed on the top of this table stating: (i) the value is calculated by CPU_DISPATCH_SEC collected from other LPAR because this LPAR uses dedicated processors and does not collect SMF data.

FORECASTED

The total forecasted MIPS used for the selected processor type. This metric is derived from the CPU forecast model (SPSS).

The Forecasted Average MIPS Used - zServer/LPAR Level and the Observed and Forecasted Peak MIPS Used – zServer/LPAR Level charts and tables contain the information that is shown in the Observed Average MIPS Used - zServer/LPAR Level and Observed Peak MIPS Used - zServer/LPAR Level charts and tables as well as the following information. If show forecast is cleared, then only observed values are displayed.

PEAK DATE (FORECASTED)

The date when the forecasted peak of the time interval occurs.

This value is displayed only when the calculation method is Highest Hourly Average or Percentile of Hourly Average.

PEAK TIME (FORECASTED)

The time when the forecasted peak of the time interval occurs.

This value is displayed only when the calculation method is Highest Hourly Average or Percentile of Hourly Average.

MIPS USED - TOTAL (OBSERVED)

The total MIPS used for the selected processor type.

If an LPAR is assigned with dedicated processors, and this LPAR does not collect SMF data, then CPU_DISPATCH_SEC is used which shows the dedicated engine's MIPS capacity but not the real utilization of this LPAR. In this case, there is a "(i)" marker by the MIPS Used value and a footnote is displayed on the top of this table stating: "(i) the value is calculated by CPU_DISPATCH_SEC collected from other LPAR because this LPAR uses dedicated processors and does not collect SMF data."

MIPS USED (OBSERVED)

The total MIPS used for the selected processor type.

If an LPAR is assigned with dedicated processors, and this LPAR does not collect SMF data, then CPU_DISPATCH_SEC is used which shows the dedicated engine's MIPS capacity but not the real utilization of this LPAR. In this case, there is a "(i)" marker by the MIPS Used value and a footnote is displayed on the top of this table stating: "(i) the value is calculated by CPU_DISPATCH_SEC collected from other LPAR because this LPAR uses dedicated processors and does not collect SMF data."

MIPS USED (FORECASTED)

The total forecasted MIPS used for the selected processor type. This metric is derived from the CPU forecast model (SPSS).

MIPS USED (FORECAST LOWER CONFIDENCE BOUND)

The lower bound of the confidence interval for the MIPS USED forecasting.

MIPS USED (FORECAST UPPER CONFIDENCE BOUND)

The upper bound of the confidence interval for the MIPS USED forecasting.

Values that are marked with an asterisk (iii) are adjusted values. The values were originally negative in the forecasting result and are adjusted to 0.

Values that are marked with an asterisk (iv) are adjusted values. The values were originally extremes in the forecasted result and are adjusted to maximum forecasted value.

The Forecast Metadata table contains the following information. This table is displayed when the show forecast prompt is selected and Highest Hourly Average is selected as calculation method.

STREAM TYPE

The stream type that is used by the available forecast results set, for example lpar_mips_peak_daily. This means that the stream is for the LPAR MIPS forecast, and it forecasts the CPU seconds at the daily peak aggregation level.

AGGREGATION

The aggregation level of the available forecast results set. The values are: Highest Hourly Average of LPAR MIPS Daily, 2nd Highest Hourly Average of LPAR MIPS Daily, 3rd Highest Hourly Average of LPAR MIPS Daily, 4th Highest Hourly Average of LPAR MIPS Daily, and 5th Highest Hourly Average of LPAR MIPS Daily.

HOUR START

The start hour of the forecasted hour range of the available forecast results set.

HOUR END

The end hour of the forecasted hour range of the available forecast results set.

HISTORY START

The start date of the based historical data range of the available forecast results set.

HISTORY END

The end date of the based historical data range of the available forecast results set.

FORECAST START

The start date of the available forecast results set.

FORECAST END

The end date of the available forecast results set.

EXECUTE TIMESTAMP

The forecast execution timestamp of the available forecast results set.

MAPPING TIMESTAMP

The last updated timestamp of the mapping table on which the forecast is based.

The rules to choose the forecast stream aggregation level when show forecast is selected are:

If you select the Average calculation method:

If you select daily, quarterly, or yearly, and a daily stream is available, then it is used. Otherwise, no data available is displayed.

If you select monthly and a monthly stream is available, then it is used. Otherwise, no data available is displayed.

If you select hourly and an hourly stream is available, then it is used. Otherwise, no data available is displayed.

If you select the Highest Hourly Average calculation method:

If you select Highest Hourly Average calculation value and a Highest Hourly Average of LPAR MIPS Daily stream is available, then it is used. Otherwise, no data available is displayed.

If you select 2nd Highest Hourly Average calculation value and a 2nd Highest Hourly Average of LPAR MIPS Daily stream is available, then it is used. Otherwise, no data available is displayed.

If you select 3rd Highest Hourly Average calculation value and a 3rd Highest Hourly Average of LPAR MIPS Daily stream is available, then it is used. Otherwise, no data available is displayed.

If you select 4th Highest Hourly Average calculation value and a 4th Highest Hourly Average of LPAR MIPS Daily stream is available, then it is used. Otherwise, no data available is displayed.

If you select 5th Highest Hourly Average calculation value and a 5th Highest Hourly Average of LPAR MIPS Daily stream is available, then it is used. Otherwise, no data available is displayed.

Parent topic: Report and workspace reference

CPU: MIPS Used - LPAR Level by WLM ImportanceThis report shows the observed average or peak CPU MIPS used at the LPAR level and the forecasted average or peak with their confidence intervals. The CPU MIPS used (both observed and forecasted) can be displayed for each processor type at each interval band. The observed CPU MIPS used is also broken down by the WLM importance level. The optimized LPAR weight and the target workload, according to the settings in the selected optimization run, can be also displayed.reportsMIPS Used - LPAR Level by WLM ImportanceCPU reportsMIPS Used - LPAR Level by WLM ImportanceThe overall usage is shown in MIPS of the LPAR by stacking the usage that is attributed to the workloads that are running in WLM importance levels. The chart shows a reference line that represents the amount of MIPS that the hypervisor attempts to guarantee as available for this LPAR. The configured Weight value for the LPAR is used to determine the guarantee line. If other LPARs are using all of their guaranteed amount of capacity, then the LPAR being viewed must fit its workload demand within the guaranteed amount of capacity. WLM manages the prioritization of work based on the goals and priorities that are established by the customer. WLM defers the lower priority work and attempts to achieve the goals of the higher priority work with the capacity that is available.By stacking the workload by Importance level order and showing the guarantee line, the report shows you what priority work is at risk by running over the guarantee line. If it shows any importance level work running over the guarantee line, then that work is at risk of not having capacity available if other LPARs use more of their capacity.By setting up the WLM rules, you can define what work runs at what Importance level. You can use this chart to ensure that the Weight values for each LPAR are satisfying the needs of the important work as you have defined it. If your higher priority workload is consistently running above the guarantee line, then it is time to evaluate the LPAR’s weight value setting.The peak value for the selected LPAR is shown for each interval band, and the values of each WLM importance level are stacked. The peak time (by hour or SMF interval) is shown if the user chooses to show the peak usage.CPU: MIPS Used - LPAR Level by WLM ImportanceCPU: MIPS Used - LPAR Level by WLM Importance exampleSource table[Presentation View].[LPAR Level Metrics][Presentation View].[LPAR Level Forecast Metrics][Presentation View].[WLM Service Class Period Usage/Delay Metrics][Presentation View].[LPAR Weight Optimization Metrics]Drill through to the following reportsCPU: MIPS Used - zServer/LPAR LevelThis report shows the observed average or peak CPU MIPS used at the zServer/CPC level and the forecasted average or peak CPU MIPS used along with their confidence interval. The average CPU MIPS used (observed and forecasted) and peak CPU MIPS used (observed and forecasted) can be displayed for each processor type (CP, zIIP, zAAP, IFL, or ICF) at each interval band and is broken down to the LPAR level. The forecasted peak CPU MIPS used can be displayed for each processor type at each interval band on the CPC level.CPU: MIPS Used - System Level (Captured vs Uncaptured)This report provides insights into potential MIPS overages. This can help you to determine the need to tune and adjust your system processes and avoid overcapacity by allocating MIPS back to business applications. Aside from the potential cost savings, a regular assessment, based on this report, can help you maintain your SLA by keeping up the performance and availability level.CPU: MIPS Used - Service Class Period Level This report provides a comprehensive view of all of the applications that are running across the entire systems landscape. This helps you quickly rebalance system allocation and to focus on the most critical applications.CPU: Over/Under Share Weight - CPC by LPARThis report shows the stacked usage of each LPAR against the total number of engines of each type in the shared pool of a CPC. It also shows the usage percentage over or under its guaranteed share of each LPAR, according to both the observed and optimized weights, due to the competition among LPARs. The target workload, according to the selected optimization run, can also be displayed.CPU: LPAR Weight Optimization Run ResultThis report shows the available LPAR Weight Optimization runs. It also shows the input settings and optimized weight results for a run selected for each CPC and processor type.Standard prompts:date rangetime rangeinterval band (If an interval band of less than one day is selected, and peak usage is shown, then the calculation uses an interval band of one day for the calculation.)showReport-specific promptsshow forecastcpu serial noprocessor typelpar nameshow optimized weightoptimization runcalculation method Three calculation methods are available: Average, Highest Hourly Average, and Percentile of SMF Interval. Percentile of SMF Interval is not available when show forecast is selected.calculation value This prompt is shown only if you select Highest Hourly Average or Percentile of SMF Interval as the calculation method. If you select Highest Hourly Average, the values are: Highest Hourly Average2nd Highest Hourly Average3rd Highest Hourly Average4th Highest Hourly Average5th Highest Hourly Average If you select Percentile of SMF Interval, the values are: 100%ile of SMF Interval95%ile of SMF Interval90%ile of SMF Interval85%ile of SMF Interval80%ile of SMF Interval75%ile of SMF Interval70%ile of SMF IntervalThe Observed Average MIPS Used - LPAR Level by WLM Importance and Observed Peak MIPS Used - LPAR Level by WLM Importance charts or tables contains the following information:If the show forecast prompt is cleared, only observed values are displayed.cpu serial noThe zServer/CEC serial number.processor typeThe engine type: CP, zIIP, zAAP, IFL, or ICF. The list contains only installed processor types.lpar nameThe name of the logical partition to which the specified processor type is assigned.DATE BANDThe start date of the time interval.PEAK DATEThe date when the peak period occurs.This value is displayed only when the calculation method is Highest Hourly Average or Percentile of SMF Interval.PEAK TIMEThe start time of the peak period.This value is displayed only when the calculation method is Highest Hourly Average or Percentile of SMF Interval.TIME BANDThe start time of the time interval.This value displays only when the calculation method is Average.AVERAGE LPAR MIPS USEDThe average MIPS usage for the selected LPAR.This value displays only when the calculation method is Average.If the SMF72 records for this LPAR in the interval band are missing from the database, a "(i)" marker is shown beside the AVERAGE LPAR MIPS USED value and a footnote is displayed on the top of the table stating: "(i) there is missing SMF72 records for this LPAR in DB in the interval band, and the MIPS value is normalized to the whole interval."PEAK LPAR MIPS USEDThe peak MIPS usage for the selected LPAR.This value is displayed only when the calculation method is Highest Hourly Average or Percentile of SMF Interval.If the SMF72 records for this LPAR in the interval band are missing from the database, a "(i)" marker is shown beside the PEAK LPAR MIPS USED value and a footnote is displayed on the top of the table stating: "(i) there is missing SMF72 records for this LPAR in DB in the interval band, and the MIPS value is normalized to the whole interval."LOGIC PROC MIPSThe logical processors MIPS value is the MIPS capacity of the logical processors that are assigned to this LPAR If this value is double the maximum of the total MIPS usage, then this line does not appear in the chart. Showing the line would make the chart appear small.GUARANTEED MIPS (OBSERVED)The guaranteed capacity in MIPS on this LPAR. The value is calculated according to the weight assigned to this LPAR.If dedicated processors are assigned to this LPAR, then this line does not appear in the chart and the value is shown as N/A in the table.If this value is double the maximum of the total MIPS usage, then this line does not appear in the chart. Showing the line would make the chart appear small.GUARANTEED MIPS (OPTIMIZED)The guaranteed capacity in MIPS on this LPAR. The value is calculated according to the optimized weight assigned to this LPAR.This value is displayed only when show optimized weight is selected.AVERAGE LPAR TARGET MIPS USEDThe average LPAR target MIPS usage at each time interval. The target setting is in the setting of the selected optimization run.This value is displayed only when show optimized weight is selected and the calculation method is Average.If the SMF72 records for this LPAR in the interval band are missing from the database, a "(i)" marker is shown beside the AVERAGE LPAR MIPS USED value and a footnote is displayed on the top of the table stating: "(i) there is missing SMF72 records for this LPAR in DB in the interval band, and the MIPS value is normalized to the whole interval."PEAK LPAR TARGET MIPS USEDThe peak LPAR target MIPS usage at each time interval. The target setting is in the setting of the selected optimization run.This value is displayed only when show optimized weight is selected and the calculation method is Highest Hourly Average or Percentile of SMF Interval.If the SMF72 records for this LPAR in the interval band are missing from the database, a "(i)" marker is shown beside the PEAK LPAR MIPS USED value and a footnote is displayed on the top of the table stating: "(i) there is missing SMF72 records for this LPAR in DB in the interval band, and the MIPS value is normalized to the whole interval."IMPORTANCE LEVELThe WLM importance level assigned to a service class period.CAPTURED MIPS USEDThe CPU time that is consumed by serviceclass_period, converted to MIPS used.The Observed and Forecasted Average MIPS Used – LPAR Level by WLM Importance and the Observed and Forecasted Peak MIPS Used – LPAR Level by WLM Importance charts and tables contain the information that is shown in the Observed Average MIPS Used - LPAR Level by WLM Importance and Observed Peak MIPS Used - LPAR Level by WLM Importance charts and table, as well as the following information:PEAK DATE (FORECASTED)The date when the forecasted peak of the time interval occurs.This value is displayed only when the calculation method is Highest Hourly Average.PEAK TIME (FORECASTED)The time when the forecasted peak of the time interval occurs.This value is displayed only when the calculation method is Highest Hourly Average.AVERAGE LPAR MIPS USED (FORECASTED)The forecasted average MIPS usage for the selected LPAR.This value is displayed only when the calculation method is Average.AVERAGE LPAR MIPS USED (FORECASTED LOWER CONFIDENCE BOUND)The lower confidence bound of the forecasted MIPS.AVERAGE LPAR MIPS USED (FORECASTED UPPER CONFIDENCE BOUND)The upper confidence bound of the forecasted MIPS.Values that are marked with an asterisk (ii) are adjusted values. These values were originally negative in the forecasting result and are adjusted to 0. Values that are marked with an asterisk (iii) are adjusted values. These values were originally extremes in the forecasted result and are adjusted to the maximum forecasted value.LOGIC PROC MIPS (FORECASTED)The forecasted MIPS capacity of the logical processors that are assigned to this LPAR.If this value is double the maximum of the total MIPS usage, then this line does not appear in the chart. Showing the line would make the chart appear small.GUARANTEED MIPS (FORECASTED)The forecasted guaranteed capacity in MIPS on this LPAR.If this value is double the maximum of the total MIPS usage, then this line does not appear in the chart. Showing the line would make the chart appear small.The Forecast Metadata table contains the following information. This table is displayed when the show forecast prompt is selected and Highest Hourly Average is selected as calculation method.STREAM TYPEThe stream type that is used by the available forecast results set, for example lpar_mips_peak_daily. This means that the stream is for the lpar mips forecast, and it forecasts the CPU seconds at the daily peak aggregation level.AGGREGATIONThe aggregation level of the available forecast results set. The values are: Highest Hourly Average of LPAR MIPS Daily, 2nd Highest Hourly Average of LPAR MIPS Daily, 3rd Highest Hourly Average of LPAR MIPS Daily, 4th Highest Hourly Average of LPAR MIPS Daily, and 5th Highest Hourly Average of LPAR MIPS Daily.HOUR STARTThe start hour of the forecasted hour range of the available forecast results set.HOUR ENDThe end hour of the forecasted hour range of the available forecast results set.HISTORY STARTThe start date of the based historical data range of the available forecast results set.HISTORY ENDThe end date of the based historical data range of the available forecast results set.FORECAST STARTThe start date of the available forecast results set.FORECAST ENDThe end date of the available forecast results set.EXECUTE TIMESTAMPThe forecast execution timestamp of the available forecast results set.MAPPING TIMESTAMPThe last updated timestamp of the mapping table on which the forecast is based.The rules to choose the forecast stream aggregation level when show forecast is selected are:If you select the Average calculation method: If you select daily, quarterly, or yearly, and a daily stream is available, then it is used. Otherwise, no data available is displayed.If you select monthly and a monthly stream is available, then it is used. Otherwise, no data available is displayed.If you select hourly and an hourly stream is available, then it is used. Otherwise, no data available is displayed. If you select the Highest Hourly Average calculation method: If you select Highest Hourly Average calculation value and a Highest Hourly Average of LPAR MIPS Daily stream is available, then it is used. Otherwise, no data available is displayed.If you select 2nd Highest Hourly Average calculation value and a 2nd Highest Hourly Average of LPAR MIPS Daily stream is available, then it is used. Otherwise, no data available is displayed.If you select 3rd Highest Hourly Average calculation value and a 3rd Highest Hourly Average of LPAR MIPS Daily stream is available, then it is used. Otherwise, no data available is displayed.If you select 4th Highest Hourly Average calculation value and a 4th Highest Hourly Average of LPAR MIPS Daily stream is available, then it is used. Otherwise, no data available is displayed.If you select 5th Highest Hourly Average calculation value and a 5th Highest Hourly Average of LPAR MIPS Daily stream is available, then it is used. Otherwise, no data available is displayed.Report and workspace referenceIBM Capacity Management Analytics provides several predefined reports and workspaces that let you monitor and analyze the capacity of your IT services and the IT infrastructure.

CPU: MIPS Used - LPAR Level by WLM Importance

This report shows the observed average or peak CPU MIPS used at the LPAR level and the forecasted average or peak with their confidence intervals. The CPU MIPS used (both observed and forecasted) can be displayed for each processor type at each interval band. The observed CPU MIPS used is also broken down by the WLM importance level. The optimized LPAR weight and the target workload, according to the settings in the selected optimization run, can be also displayed.

The overall usage is shown in MIPS of the LPAR by stacking the usage that is attributed to the workloads that are running in WLM importance levels. The chart shows a reference line that represents the amount of MIPS that the hypervisor attempts to guarantee as available for this LPAR. The configured Weight value for the LPAR is used to determine the guarantee line. If other LPARs are using all of their guaranteed amount of capacity, then the LPAR being viewed must fit its workload demand within the guaranteed amount of capacity. WLM manages the prioritization of work based on the goals and priorities that are established by the customer. WLM defers the lower priority work and attempts to achieve the goals of the higher priority work with the capacity that is available.

By stacking the workload by Importance level order and showing the guarantee line, the report shows you what priority work is at risk by running over the guarantee line. If it shows any importance level work running over the guarantee line, then that work is at risk of not having capacity available if other LPARs use more of their capacity.

By setting up the WLM rules, you can define what work runs at what Importance level. You can use this chart to ensure that the Weight values for each LPAR are satisfying the needs of the important work as you have defined it. If your higher priority workload is consistently running above the guarantee line, then it is time to evaluate the LPAR’s weight value setting.

The peak value for the selected LPAR is shown for each interval band, and the values of each WLM importance level are stacked. The peak time (by hour or SMF interval) is shown if the user chooses to show the peak usage.

Figure 38. CPU: MIPS Used - LPAR Level by WLM Importance

Source table

[Presentation View].[LPAR Level Metrics]

[Presentation View].[LPAR Level Forecast Metrics]

[Presentation View].[WLM Service Class Period Usage/Delay Metrics]

[Presentation View].[LPAR Weight Optimization Metrics]

Drill through to the following reports

CPU: MIPS Used - zServer/LPAR Level

CPU: MIPS Used - System Level (Captured vs Uncaptured)

CPU: MIPS Used - Service Class Period Level

CPU: Over/Under Share Weight - CPC by LPAR

CPU: LPAR Weight Optimization Run Result

Standard prompts:

date range

time range

interval band (If an interval band of less than one day is selected, and peak usage is shown, then the calculation uses an interval band of one day for the calculation.)

show

Report-specific prompts

show forecast

cpu serial no

processor type

lpar name

show optimized weight

optimization run

calculation method

Three calculation methods are available: Average, Highest Hourly Average, and Percentile of SMF Interval. Percentile of SMF Interval is not available when show forecast is selected.

calculation value

This prompt is shown only if you select Highest Hourly Average or Percentile of SMF Interval as the calculation method.

If you select Highest Hourly Average, the values are:

Highest Hourly Average

2nd Highest Hourly Average

3rd Highest Hourly Average

4th Highest Hourly Average

5th Highest Hourly Average

If you select Percentile of SMF Interval, the values are:

100%ile of SMF Interval

95%ile of SMF Interval

90%ile of SMF Interval

85%ile of SMF Interval

80%ile of SMF Interval

75%ile of SMF Interval

70%ile of SMF Interval

The Observed Average MIPS Used - LPAR Level by WLM Importance and Observed Peak MIPS Used - LPAR Level by WLM Importance charts or tables contains the following information:

Note: If the show forecast prompt is cleared, only observed values are displayed.

cpu serial no

The zServer/CEC serial number.

processor type

The engine type: CP, zIIP, zAAP, IFL, or ICF. The list contains only installed processor types.

lpar name

The name of the logical partition to which the specified processor type is assigned.

DATE BAND

The start date of the time interval.

PEAK DATE

The date when the peak period occurs.

This value is displayed only when the calculation method is Highest Hourly Average or Percentile of SMF Interval.

PEAK TIME

The start time of the peak period.

This value is displayed only when the calculation method is Highest Hourly Average or Percentile of SMF Interval.

TIME BAND

The start time of the time interval.

This value displays only when the calculation method is Average.

AVERAGE LPAR MIPS USED

The average MIPS usage for the selected LPAR.

This value displays only when the calculation method is Average.

If the SMF72 records for this LPAR in the interval band are missing from the database, a "(i)" marker is shown beside the AVERAGE LPAR MIPS USED value and a footnote is displayed on the top of the table stating: "(i) there is missing SMF72 records for this LPAR in DB in the interval band, and the MIPS value is normalized to the whole interval."

PEAK LPAR MIPS USED

The peak MIPS usage for the selected LPAR.

This value is displayed only when the calculation method is Highest Hourly Average or Percentile of SMF Interval.

If the SMF72 records for this LPAR in the interval band are missing from the database, a "(i)" marker is shown beside the PEAK LPAR MIPS USED value and a footnote is displayed on the top of the table stating: "(i) there is missing SMF72 records for this LPAR in DB in the interval band, and the MIPS value is normalized to the whole interval."

LOGIC PROC MIPS

The logical processors MIPS value is the MIPS capacity of the logical processors that are assigned to this LPAR

If this value is double the maximum of the total MIPS usage, then this line does not appear in the chart. Showing the line would make the chart appear small.

GUARANTEED MIPS (OBSERVED)

The guaranteed capacity in MIPS on this LPAR. The value is calculated according to the weight assigned to this LPAR.

If dedicated processors are assigned to this LPAR, then this line does not appear in the chart and the value is shown as N/A in the table.

If this value is double the maximum of the total MIPS usage, then this line does not appear in the chart. Showing the line would make the chart appear small.

GUARANTEED MIPS (OPTIMIZED)

The guaranteed capacity in MIPS on this LPAR. The value is calculated according to the optimized weight assigned to this LPAR.

This value is displayed only when show optimized weight is selected.

AVERAGE LPAR TARGET MIPS USED

The average LPAR target MIPS usage at each time interval. The target setting is in the setting of the selected optimization run.

This value is displayed only when show optimized weight is selected and the calculation method is Average.

If the SMF72 records for this LPAR in the interval band are missing from the database, a "(i)" marker is shown beside the AVERAGE LPAR MIPS USED value and a footnote is displayed on the top of the table stating: "(i) there is missing SMF72 records for this LPAR in DB in the interval band, and the MIPS value is normalized to the whole interval."

PEAK LPAR TARGET MIPS USED

The peak LPAR target MIPS usage at each time interval. The target setting is in the setting of the selected optimization run.

This value is displayed only when show optimized weight is selected and the calculation method is Highest Hourly Average or Percentile of SMF Interval.

If the SMF72 records for this LPAR in the interval band are missing from the database, a "(i)" marker is shown beside the PEAK LPAR MIPS USED value and a footnote is displayed on the top of the table stating: "(i) there is missing SMF72 records for this LPAR in DB in the interval band, and the MIPS value is normalized to the whole interval."

IMPORTANCE LEVEL

The WLM importance level assigned to a service class period.

CAPTURED MIPS USED

The CPU time that is consumed by serviceclass_period, converted to MIPS used.

The Observed and Forecasted Average MIPS Used – LPAR Level by WLM Importance and the Observed and Forecasted Peak MIPS Used – LPAR Level by WLM Importance charts and tables contain the information that is shown in the Observed Average MIPS Used - LPAR Level by WLM Importance and Observed Peak MIPS Used - LPAR Level by WLM Importance charts and table, as well as the following information:

PEAK DATE (FORECASTED)

The date when the forecasted peak of the time interval occurs.

This value is displayed only when the calculation method is Highest Hourly Average.

PEAK TIME (FORECASTED)

The time when the forecasted peak of the time interval occurs.

This value is displayed only when the calculation method is Highest Hourly Average.

AVERAGE LPAR MIPS USED (FORECASTED)

The forecasted average MIPS usage for the selected LPAR.

This value is displayed only when the calculation method is Average.

AVERAGE LPAR MIPS USED (FORECASTED LOWER CONFIDENCE BOUND)

The lower confidence bound of the forecasted MIPS.

AVERAGE LPAR MIPS USED (FORECASTED UPPER CONFIDENCE BOUND)

The upper confidence bound of the forecasted MIPS.

Values that are marked with an asterisk (ii) are adjusted values. These values were originally negative in the forecasting result and are adjusted to 0.

Values that are marked with an asterisk (iii) are adjusted values. These values were originally extremes in the forecasted result and are adjusted to the maximum forecasted value.

LOGIC PROC MIPS (FORECASTED)

The forecasted MIPS capacity of the logical processors that are assigned to this LPAR.

If this value is double the maximum of the total MIPS usage, then this line does not appear in the chart. Showing the line would make the chart appear small.

GUARANTEED MIPS (FORECASTED)

The forecasted guaranteed capacity in MIPS on this LPAR.

If this value is double the maximum of the total MIPS usage, then this line does not appear in the chart. Showing the line would make the chart appear small.

The Forecast Metadata table contains the following information. This table is displayed when the show forecast prompt is selected and Highest Hourly Average is selected as calculation method.

STREAM TYPE

The stream type that is used by the available forecast results set, for example lpar_mips_peak_daily. This means that the stream is for the lpar mips forecast, and it forecasts the CPU seconds at the daily peak aggregation level.

AGGREGATION

The aggregation level of the available forecast results set. The values are: Highest Hourly Average of LPAR MIPS Daily, 2nd Highest Hourly Average of LPAR MIPS Daily, 3rd Highest Hourly Average of LPAR MIPS Daily, 4th Highest Hourly Average of LPAR MIPS Daily, and 5th Highest Hourly Average of LPAR MIPS Daily.

HOUR START

The start hour of the forecasted hour range of the available forecast results set.

HOUR END

The end hour of the forecasted hour range of the available forecast results set.

HISTORY START

The start date of the based historical data range of the available forecast results set.

HISTORY END

The end date of the based historical data range of the available forecast results set.

FORECAST START

The start date of the available forecast results set.

FORECAST END

The end date of the available forecast results set.

EXECUTE TIMESTAMP

The forecast execution timestamp of the available forecast results set.

MAPPING TIMESTAMP

The last updated timestamp of the mapping table on which the forecast is based.

The rules to choose the forecast stream aggregation level when show forecast is selected are:

If you select the Average calculation method:

If you select daily, quarterly, or yearly, and a daily stream is available, then it is used. Otherwise, no data available is displayed.

If you select monthly and a monthly stream is available, then it is used. Otherwise, no data available is displayed.

If you select hourly and an hourly stream is available, then it is used. Otherwise, no data available is displayed.

If you select the Highest Hourly Average calculation method:

If you select Highest Hourly Average calculation value and a Highest Hourly Average of LPAR MIPS Daily stream is available, then it is used. Otherwise, no data available is displayed.

If you select 2nd Highest Hourly Average calculation value and a 2nd Highest Hourly Average of LPAR MIPS Daily stream is available, then it is used. Otherwise, no data available is displayed.

If you select 3rd Highest Hourly Average calculation value and a 3rd Highest Hourly Average of LPAR MIPS Daily stream is available, then it is used. Otherwise, no data available is displayed.

If you select 4th Highest Hourly Average calculation value and a 4th Highest Hourly Average of LPAR MIPS Daily stream is available, then it is used. Otherwise, no data available is displayed.

If you select 5th Highest Hourly Average calculation value and a 5th Highest Hourly Average of LPAR MIPS Daily stream is available, then it is used. Otherwise, no data available is displayed.

Parent topic: Report and workspace reference

CPU: Over/Under Share Weight - CPC by LPARThis report shows the stacked usage of each LPAR against the total number of engines of each type in the shared pool of a CPC. It also shows the usage percentage over or under its guaranteed share of each LPAR, according to both the observed and optimized weights, due to the competition among LPARs. The target workload, according to the selected optimization run, can also be displayed.reportsOver/Under Share Weight - CPC by LPARCPU reportsOver/Under Share Weight - CPC by LPARCPU: Over/Under Share Weight - CPC by LPARCPU: Over/Under Share Weight - CPC by LPAROn a CPC, there are multiple LPARs that have a configuration of CPUs that they share among other LPARs that are defined on the same CPC. And each LPAR has a defined maximum number of CPUs that it can consume. It is also given a weight value that is calculated as a percentage of the entire set of CPUs that are shared on the CPC. The percentage of the physical CPUs is the target, or guaranteed, amount of CPU that the hypervisor ensures that this LPAR is able to use. If there is unused capacity on the CPC above this target and the LPAR wants more, then it is allowed to use it up to the maximum number of CPUs that are defined for that LPAR.There is a shared pool for each type of processor; General CP, zIIP, and zAAP. Each shared pool is managed separately and each LPAR has a target or guaranteed amount for each processor type that is based on weight. An LPAR participates in the sharing of a processor pool only if it has at least one logical processor of that processor type defined.When there are several LPARs on a CPC that are competing for more than their guaranteed amount during different portions of the day, it can be difficult to assess just what those guaranteed amounts should be to satisfy the workloads in the proper priority. When the CPC is running at 100% busy, then the hypervisor is trying to satisfy each LPAR's demand up to the guaranteed amount. This report uses two charts to show the guaranteed weight. One chart shows the weight from the CPC perspective, and the other shows it from the perspective of each individual LPAR against the shared pool for the CPC.From the CPC perspective, the chart shows the total number of CPUs in the shared pool and stacks the usage of each LPAR that is sharing the pool. This provides a view of how much of the shared pool is being utilized. This might not be the entire CPC capacity, as there can be other LPARs with dedicated CPs.The competition among LPARs is shown by the usage of each LPAR against it’s own guaranteed share as a percent of the CPC shared processor pool. For example, if an LPAR is guaranteed 30% of the CPC shared pool and it uses only 25%, then it shows as using 5% under its target. If there are 3 LPARs that each have a guarantee of 30% and each use 25%, then the chart shows a stacked bar of 3 LPARs each at 5% under, showing a total of 15% under target. The 15% of the CPC that is not being consumed is available for other LPARs. In the example, there is 15% capacity available above the planned target capacity to support the workload according to business priorities. However, at the same time interval there might be another LPAR with a guarantee of 10% and it might be using 25% of the CPC shared pool. This would be 15% over its guarantee and would be shown as 15% over the target usage. In this case, the CPC is actually running at 100% busy of the shared processor pool. The chart shows which LPARs are using more than their share. They are allowed to because the others are not using all of their guarantee.This report, with both of its charts, can be used to quickly assess if the time period of the CPC being 100% is good or bad. If this usage matches the business priority as expected, then it is good.The share weight reports should be viewed by using a fine interval band, preferably no higher than hourly. If all of the LPARs are using the same SMF interval duration, then you can go to that finest level. If a different SMF interval duration is used by different LPARs, then the selected interval duration is displayed.Source table[Presentation View].[LPAR Level Metrics][Presentation View].[LPAR Weight Optimization Metrics][Presentation View].[Optimization Metadata][Presentation View].[WLM Service Class Period Usage/Delay Metrics]Drill through to the following reportsCPU: MIPS Used - Service Class Period Level This report provides a comprehensive view of all of the applications that are running across the entire systems landscape. This helps you quickly rebalance system allocation and to focus on the most critical applications.CPU: MIPS Used - zServer/LPAR LevelThis report shows the observed average or peak CPU MIPS used at the zServer/CPC level and the forecasted average or peak CPU MIPS used along with their confidence interval. The average CPU MIPS used (observed and forecasted) and peak CPU MIPS used (observed and forecasted) can be displayed for each processor type (CP, zIIP, zAAP, IFL, or ICF) at each interval band and is broken down to the LPAR level. The forecasted peak CPU MIPS used can be displayed for each processor type at each interval band on the CPC level.CPU: MIPS Used - System Level (Captured vs Uncaptured)This report provides insights into potential MIPS overages. This can help you to determine the need to tune and adjust your system processes and avoid overcapacity by allocating MIPS back to business applications. Aside from the potential cost savings, a regular assessment, based on this report, can help you maintain your SLA by keeping up the performance and availability level.CPU: MIPS Used - LPAR Level by WLM ImportanceThis report shows the observed average or peak CPU MIPS used at the LPAR level and the forecasted average or peak with their confidence intervals. The CPU MIPS used (both observed and forecasted) can be displayed for each processor type at each interval band. The observed CPU MIPS used is also broken down by the WLM importance level. The optimized LPAR weight and the target workload, according to the settings in the selected optimization run, can be also displayed.CPU: LPAR Weight Optimization Run ResultThis report shows the available LPAR Weight Optimization runs. It also shows the input settings and optimized weight results for a run selected for each CPC and processor type.Standard prompts:date rangetime rangeinterval bandshowReport-specific promptscpu serial noprocessor typelpar nameshow optimized weightoptimization runworkload categoryThis report contains the following information:cpu serial noThe zServer/CEC serial number.processor typeThe engine type: CP, zIIP, zAAP, IFL, or ICF. The list contains only installed processor types.lpar nameThe name of the logical partition to which the specified processor type is assigned.Only LPARs with shared processors are shown.# SHARED CPC ENGINESThe number of shared engines on the CPC.LPAR WEIGHTThe weight that is assigned to this LPAR.LPAR WEIGHT TOTALThe sum of the weights for all active LPARs on the CPC for the selected processor type.% GUARANTEED OF SHARED POOL (OBSERVED)The percent of the shared pool that is guaranteed to the LPAR. The value is calculated according to the weight assigned to this LPAR.% GUARANTEED OF SHARED POOL (OPTIMIZED)The percent of the shared pool that is guaranteed to the LPAR. The value is calculated according to the optimized weight assigned to this LPAR.This value is displayed only when show optimized weight is selected. LPAR % BUSYThe percentage of time during which the logical engines for this LPAR are busy.This information is displayed when show optimized weight is cleared, or when show optimized weight is selected and total workload in workload category is also selected. LPAR % BUSY (TARGET WORKLOAD)The percentage of time during which the logical engines for this LPAR are busy for the target workload.This information is displayed only when show optimized weight and target workload in workload category are selected.# LOGICAL ENGINESThe number of logical engines for this LPAR.UTIL IN # ENGINESThe utilization in number of the logical engines on this LPAR.This information is displayed when show optimized weight is cleared, or when show optimized weight is selected and total workload in workload category is also selected. UTIL IN # ENGINES (TARGET WORKLOAD)The utilization of target workload in number of the logical engines on this LPAR.This information is displayed only when show optimized weight and target workload in workload category are selected.# ENGINES GUARANTEED (OBSERVED)The guaranteed capacity in number of engines on this LPAR. This value is calculated according to the weight assigned to the LPAR.# ENGINES GUARANTEED (OPTIMIZED)The guaranteed capacity in number of engines on this LPAR. This value is calculated according to the optimized weight assigned to the LPAR.This value is displayed only when show optimized weight is selected. % BUSY OF SHARED POOLThe utilization of this LPAR as a percentage of the shared pool of processors on the CPC.This information is displayed when show optimized weight is cleared, or when show optimized weight is selected and total workload in workload category is also selected. % BUSY OF SHARED POOL (TARGET WORKLOAD)The utilization of target workload of this LPAR as a percentage of the shared pool of processors on the CPC.This information is displayed only when show optimized weight and target workload in workload category are selected.% OVER GUARANTEED SHARE (OBSERVED)The usage percentage over the guaranteed share for each LPAR due to the competition among LPARs.% UNDER GUARANTEED SHARE (OBSERVED)The usage percentage under the guaranteed share for each LPAR due to the competition among LPARs.IS GUARANTEED (OBSERVED) > # LOGICAL ENGINESIf the guaranteed number of engines is larger than the number of logical engines, then the LPAR cannot get its guaranteed number of engines forever. In this case, the value is set to YES as a reminder of this configuration conflict on the LPAR.% OVER GUARANTEED SHARE (OPTIMIZED)The usage percentage for the total or target workload over the optimized guaranteed share for each LPAR due to the competition among LPARs.This information is displayed only when show optimized weight is selected.% UNDER GUARANTEED SHARE (OPTIMIZED)The usage percentage for the total or target workload under the optimized guaranteed share for each LPAR due to the competition among LPARs.This information is displayed only when show optimized weight is selected.LOGICAL TO PHYSICAL RATIOThe ratio that is presented is for all LPARs selected against the total physical shared pool.The Optimization Metadata table contains the following information. The table is shown when show optimized weight is selected.OPTIMIZATION RUNThe name that is used for the execution of the LPAR weight optimization.MODEL VERSIONThe version of the optimization model.EXECUTION TIMESTAMPThe execution start time of this optimization run.OBJECTIVEThe total objective value of this optimization model.Report and workspace referenceIBM Capacity Management Analytics provides several predefined reports and workspaces that let you monitor and analyze the capacity of your IT services and the IT infrastructure.

CPU: Over/Under Share Weight - CPC by LPAR

This report shows the stacked usage of each LPAR against the total number of engines of each type in the shared pool of a CPC. It also shows the usage percentage over or under its guaranteed share of each LPAR, according to both the observed and optimized weights, due to the competition among LPARs. The target workload, according to the selected optimization run, can also be displayed.

Figure 39. CPU: Over/Under Share Weight - CPC by LPAR

On a CPC, there are multiple LPARs that have a configuration of CPUs that they share among other LPARs that are defined on the same CPC. And each LPAR has a defined maximum number of CPUs that it can consume. It is also given a weight value that is calculated as a percentage of the entire set of CPUs that are shared on the CPC. The percentage of the physical CPUs is the target, or guaranteed, amount of CPU that the hypervisor ensures that this LPAR is able to use. If there is unused capacity on the CPC above this target and the LPAR wants more, then it is allowed to use it up to the maximum number of CPUs that are defined for that LPAR.

There is a shared pool for each type of processor; General CP, zIIP, and zAAP. Each shared pool is managed separately and each LPAR has a target or guaranteed amount for each processor type that is based on weight. An LPAR participates in the sharing of a processor pool only if it has at least one logical processor of that processor type defined.

Important: When there are several LPARs on a CPC that are competing for more than their guaranteed amount during different portions of the day, it can be difficult to assess just what those guaranteed amounts should be to satisfy the workloads in the proper priority. When the CPC is running at 100% busy, then the hypervisor is trying to satisfy each LPAR's demand up to the guaranteed amount.

This report uses two charts to show the guaranteed weight. One chart shows the weight from the CPC perspective, and the other shows it from the perspective of each individual LPAR against the shared pool for the CPC.

From the CPC perspective, the chart shows the total number of CPUs in the shared pool and stacks the usage of each LPAR that is sharing the pool. This provides a view of how much of the shared pool is being utilized. This might not be the entire CPC capacity, as there can be other LPARs with dedicated CPs.

The competition among LPARs is shown by the usage of each LPAR against it’s own guaranteed share as a percent of the CPC shared processor pool.

For example, if an LPAR is guaranteed 30% of the CPC shared pool and it uses only 25%, then it shows as using 5% under its target. If there are 3 LPARs that each have a guarantee of 30% and each use 25%, then the chart shows a stacked bar of 3 LPARs each at 5% under, showing a total of 15% under target. The 15% of the CPC that is not being consumed is available for other LPARs.

In the example, there is 15% capacity available above the planned target capacity to support the workload according to business priorities.

However, at the same time interval there might be another LPAR with a guarantee of 10% and it might be using 25% of the CPC shared pool. This would be 15% over its guarantee and would be shown as 15% over the target usage. In this case, the CPC is actually running at 100% busy of the shared processor pool.

The chart shows which LPARs are using more than their share. They are allowed to because the others are not using all of their guarantee.

This report, with both of its charts, can be used to quickly assess if the time period of the CPC being 100% is good or bad. If this usage matches the business priority as expected, then it is good.

The share weight reports should be viewed by using a fine interval band, preferably no higher than hourly. If all of the LPARs are using the same SMF interval duration, then you can go to that finest level. If a different SMF interval duration is used by different LPARs, then the selected interval duration is displayed.

Source table

[Presentation View].[LPAR Level Metrics]

[Presentation View].[LPAR Weight Optimization Metrics]

[Presentation View].[Optimization Metadata]

[Presentation View].[WLM Service Class Period Usage/Delay Metrics]

Drill through to the following reports

CPU: MIPS Used - Service Class Period Level

CPU: MIPS Used - zServer/LPAR Level

CPU: MIPS Used - System Level (Captured vs Uncaptured)

CPU: MIPS Used - LPAR Level by WLM Importance

CPU: LPAR Weight Optimization Run Result

Standard prompts:

date range

time range

interval band

show

Report-specific prompts

cpu serial no

processor type

lpar name

show optimized weight

optimization run

workload category

This report contains the following information:

cpu serial no

The zServer/CEC serial number.

processor type

The engine type: CP, zIIP, zAAP, IFL, or ICF. The list contains only installed processor types.

lpar name

The name of the logical partition to which the specified processor type is assigned.

Only LPARs with shared processors are shown.

# SHARED CPC ENGINES

The number of shared engines on the CPC.

LPAR WEIGHT

The weight that is assigned to this LPAR.

LPAR WEIGHT TOTAL

The sum of the weights for all active LPARs on the CPC for the selected processor type.

% GUARANTEED OF SHARED POOL (OBSERVED)

The percent of the shared pool that is guaranteed to the LPAR. The value is calculated according to the weight assigned to this LPAR.

% GUARANTEED OF SHARED POOL (OPTIMIZED)

The percent of the shared pool that is guaranteed to the LPAR. The value is calculated according to the optimized weight assigned to this LPAR.

This value is displayed only when show optimized weight is selected.

LPAR % BUSY

The percentage of time during which the logical engines for this LPAR are busy.

This information is displayed when show optimized weight is cleared, or when show optimized weight is selected and total workload in workload category is also selected.

LPAR % BUSY (TARGET WORKLOAD)

The percentage of time during which the logical engines for this LPAR are busy for the target workload.

This information is displayed only when show optimized weight and target workload in workload category are selected.

# LOGICAL ENGINES

The number of logical engines for this LPAR.

UTIL IN # ENGINES

The utilization in number of the logical engines on this LPAR.

This information is displayed when show optimized weight is cleared, or when show optimized weight is selected and total workload in workload category is also selected.

UTIL IN # ENGINES (TARGET WORKLOAD)

The utilization of target workload in number of the logical engines on this LPAR.

This information is displayed only when show optimized weight and target workload in workload category are selected.

# ENGINES GUARANTEED (OBSERVED)

The guaranteed capacity in number of engines on this LPAR. This value is calculated according to the weight assigned to the LPAR.

# ENGINES GUARANTEED (OPTIMIZED)

The guaranteed capacity in number of engines on this LPAR. This value is calculated according to the optimized weight assigned to the LPAR.

This value is displayed only when show optimized weight is selected.

% BUSY OF SHARED POOL

The utilization of this LPAR as a percentage of the shared pool of processors on the CPC.

This information is displayed when show optimized weight is cleared, or when show optimized weight is selected and total workload in workload category is also selected.

% BUSY OF SHARED POOL (TARGET WORKLOAD)

The utilization of target workload of this LPAR as a percentage of the shared pool of processors on the CPC.

This information is displayed only when show optimized weight and target workload in workload category are selected.

% OVER GUARANTEED SHARE (OBSERVED)

The usage percentage over the guaranteed share for each LPAR due to the competition among LPARs.

% UNDER GUARANTEED SHARE (OBSERVED)

The usage percentage under the guaranteed share for each LPAR due to the competition among LPARs.

IS GUARANTEED (OBSERVED) > # LOGICAL ENGINES

If the guaranteed number of engines is larger than the number of logical engines, then the LPAR cannot get its guaranteed number of engines forever. In this case, the value is set to YES as a reminder of this configuration conflict on the LPAR.

% OVER GUARANTEED SHARE (OPTIMIZED)

The usage percentage for the total or target workload over the optimized guaranteed share for each LPAR due to the competition among LPARs.

This information is displayed only when show optimized weight is selected.

% UNDER GUARANTEED SHARE (OPTIMIZED)

The usage percentage for the total or target workload under the optimized guaranteed share for each LPAR due to the competition among LPARs.

This information is displayed only when show optimized weight is selected.

LOGICAL TO PHYSICAL RATIO

The ratio that is presented is for all LPARs selected against the total physical shared pool.

The Optimization Metadata table contains the following information. The table is shown when show optimized weight is selected.

OPTIMIZATION RUN

The name that is used for the execution of the LPAR weight optimization.

MODEL VERSION

The version of the optimization model.

EXECUTION TIMESTAMP

The execution start time of this optimization run.

OBJECTIVE

The total objective value of this optimization model.

Parent topic: Report and workspace reference

CPU: Windows - CPU UsageThis report shows the percentage of CPU used from both a virtual and physical perspective.reportsWindows - CPU UsageCPU reportsWindows - CPU UsageThe server might be virtualized or it might be a dedicated physical server. Showing both virtual and physical servers lets you know what type of server it is and how it is using its resources.Windows - CPU Usage exampleWindows - CPU Usage exampleSource tables[Presentation View].[WIN DIST DATE][Presentation View].[WIN DIST HOUR][Presentation View].[WIN DIST TIME][Presentation View].[WIN CPU Fact][Presentation View].[WIN IMAGE]Drill through to the following reportMemory: Windows - Memory UsageThis report shows how much memory is installed and the amount of free memory that is available for the server.Standard prompts:date rangetime rangeinterval bandtime zoneshowReport-specific promptsServer NameThe Windows - CPU Usage report contains the following information:server nameThe name of the server against which the report is run.CPU % BUSY CPU utilization in percent. For systems with multiple cores this value represents the average utilization over all coresNUM CPU AVAILThe number of processors currently available on this system.NUM CPU INSTALLEDThe number of processors installed on this system. PERCENTAGE 10%10% CPU busy line.PERCENTAGE 90%90% CPU busy line. If the maximum CPU Usage percentage is less than 90%, then only 10% CPU busy line is displayed.Time Zone Summary tableThis table displays the time zone summary information for a selected server and contains the following elements:TIME ZONE (REPORT)The time zone used when displaying the report. The format is UTC -5:00.START DATE TIME (REPORT)The start date and time in reporting the time zone. The start date and time is the latest time between the start date and time in the prompt and the earliest date and time of available data in the database.END DATE TIME (REPORT)The end date and time in reporting the time zone. The start date and time is the earliest time between the end date and time in the prompt and the latest date and time of available data in the database.TIME ZONE (SERVER OS)The local time of the selected server. The format is GMT -5:00the LPAR time zone is the time zone of the server OS and not the time of where the server is physically located.START DATE TIME (SERVER OS)The start date and time in the selected server local time zone. The start date and time is the latest time between the start date and the time in the prompt and the earliest date and time of available data in the database.END DATE TIME (SERVER OS)The end date and time in the selected server local time zone. The end date and time is the earliest time between the end date and the time in the prompt and the latest date and time of available data in the database.Report and workspace referenceIBM Capacity Management Analytics provides several predefined reports and workspaces that let you monitor and analyze the capacity of your IT services and the IT infrastructure.

CPU: Windows - CPU Usage

This report shows the percentage of CPU used from both a virtual and physical perspective.

The server might be virtualized or it might be a dedicated physical server. Showing both virtual and physical servers lets you know what type of server it is and how it is using its resources.

Figure 40. Windows - CPU Usage example

Source tables

[Presentation View].[WIN DIST DATE]

[Presentation View].[WIN DIST HOUR]

[Presentation View].[WIN DIST TIME]

[Presentation View].[WIN CPU Fact]

[Presentation View].[WIN IMAGE]

Drill through to the following report

Memory: Windows - Memory Usage

Standard prompts:

date range

time range

interval band

time zone

show

Report-specific prompts

Server Name

The Windows - CPU Usage report contains the following information:

server name

The name of the server against which the report is run.

CPU % BUSY

CPU utilization in percent. For systems with multiple cores this value represents the average utilization over all cores

NUM CPU AVAIL

The number of processors currently available on this system.

NUM CPU INSTALLED

The number of processors installed on this system.

PERCENTAGE 10%

10% CPU busy line.

PERCENTAGE 90%

90% CPU busy line. If the maximum CPU Usage percentage is less than 90%, then only 10% CPU busy line is displayed.

Time Zone Summary table

This table displays the time zone summary information for a selected server and contains the following elements:

TIME ZONE (REPORT)

The time zone used when displaying the report. The format is UTC -5:00.

START DATE TIME (REPORT)

The start date and time in reporting the time zone. The start date and time is the latest time between the start date and time in the prompt and the earliest date and time of available data in the database.

END DATE TIME (REPORT)

The end date and time in reporting the time zone. The start date and time is the earliest time between the end date and time in the prompt and the latest date and time of available data in the database.

TIME ZONE (SERVER OS)

The local time of the selected server. The format is GMT -5:00

Important: the LPAR time zone is the time zone of the server OS and not the time of where the server is physically located.

START DATE TIME (SERVER OS)

The start date and time in the selected server local time zone. The start date and time is the latest time between the start date and the time in the prompt and the earliest date and time of available data in the database.

END DATE TIME (SERVER OS)

The end date and time in the selected server local time zone. The end date and time is the earliest time between the end date and the time in the prompt and the latest date and time of available data in the database.

Parent topic: Report and workspace reference

Memory: AIX - Memory UsageThis report shows the free physical memory, free virtual memory, and active virtual memory on the system. The report also displays metric showing the turnover of physical memory, such as Page-In/sec and Page-Out/sec (file system related pages), as well as PageSpace-In/sec and PageSpace-Out/sec.reportsAIX - Memory Usage memory reportsWindows - CPU UsageMemory usage cannot be fully understood by using just one metric. For example, if you see a spike in Page-in/sec, maybe there is also page out activity. But if the active virtual memory has a minimal increase and there is a large amount of free memory on the system, then there is not a memory shortage on the server. However, there is some memory turnover occurring. This might be the nature of the application or it might suggest that there is an opportunity to tune the application on the server.AIX - Memory Usage exampleAIX - Memory Usage exampleSource tables[Presentation View].[AIX DIST Date][Presentation View].[AIX DIST Hour][Presentation View].[AIX DIST TIME][Presentation View].[AIX Memory Fact][Presentation View].[AIX IMAGE]Drill through to the following reportCPU: AIX - CPU UsageThis report shows the percentage of CPU used from both a virtual and physical perspective.Standard prompts:date rangetime rangeinterval bandtime zoneshowReport-specific promptsServer NameThe AIX - Memory Usage report contains the following information:server nameThe name of the server against which the report is run.FREE PHYS MEMORY (MB)The amount of physical memory currently unused and available. In a virtualized environment this is the free memory available to the operating systemFREE VIRT MEMORY (MB)The amount of virtual memory currently unused and available.ACTIVE VIRT MEMORY (MB)The current number of active pages in virtual memory. Note that this metric is expressed in pages. Pages are 4K in size, so the number of pages are multiplied by 4, then divided by 1024 to get MB. Page In / SecThe number of page-in operations per second from the file system.Page Out / SecThe number of page-out operations per second from the file system.Pagespace In / SecNumber of page-in operations per second from the paging space.Pagespace Out / SecNumber of page-out operations per second from the paging space.Time Zone Summary tableThis table displays the time zone summary information for a selected server and contains the following elements:TIME ZONE (REPORT)The time zone used when displaying the report. The format is UTC -5:00.START DATE TIME (REPORT)The start date and time in reporting the time zone. The start date and time is the latest time between the start date and time in the prompt and the earliest date and time of available data in the database.END DATE TIME (REPORT)The end date and time in reporting the time zone. The start date and time is the earliest time between the end date and time in the prompt and the latest date and time of available data in the database.TIME ZONE (SERVER OS)The local time of the selected server. The format is GMT -5:00the server time zone is the time zone of the server OS and not the time of where the server is physically located.START DATE TIME (SERVER OS)The start date and time in the selected server local time zone. The start date and time is the latest time between the start date and the time in the prompt and the earliest date and time of available data in the database.END DATE TIME (SERVER OS)The end date and time in the selected server local time zone. The end date and time is the earliest time between the end date and the time in the prompt and the latest date and time of available data in the database.Report and workspace referenceIBM Capacity Management Analytics provides several predefined reports and workspaces that let you monitor and analyze the capacity of your IT services and the IT infrastructure.

Memory: AIX - Memory Usage

This report shows the free physical memory, free virtual memory, and active virtual memory on the system. The report also displays metric showing the turnover of physical memory, such as Page-In/sec and Page-Out/sec (file system related pages), as well as PageSpace-In/sec and PageSpace-Out/sec.

Memory usage cannot be fully understood by using just one metric. For example, if you see a spike in Page-in/sec, maybe there is also page out activity. But if the active virtual memory has a minimal increase and there is a large amount of free memory on the system, then there is not a memory shortage on the server. However, there is some memory turnover occurring. This might be the nature of the application or it might suggest that there is an opportunity to tune the application on the server.

Figure 41. AIX - Memory Usage example

Source tables

[Presentation View].[AIX® DIST Date]

[Presentation View].[AIX DIST Hour]

[Presentation View].[AIX DIST TIME]

[Presentation View].[AIX Memory Fact]

[Presentation View].[AIX IMAGE]

Drill through to the following report

CPU: AIX - CPU Usage

Standard prompts:

date range

time range

interval band

time zone

show

Report-specific prompts

Server Name

The AIX - Memory Usage report contains the following information:

server name

The name of the server against which the report is run.

FREE PHYS MEMORY (MB)

The amount of physical memory currently unused and available. In a virtualized environment this is the free memory available to the operating system

FREE VIRT MEMORY (MB)

The amount of virtual memory currently unused and available.

ACTIVE VIRT MEMORY (MB)

The current number of active pages in virtual memory. Note that this metric is expressed in pages. Pages are 4K in size, so the number of pages are multiplied by 4, then divided by 1024 to get MB.

Page In / Sec

The number of page-in operations per second from the file system.

Page Out / Sec

The number of page-out operations per second from the file system.

Pagespace In / Sec

Number of page-in operations per second from the paging space.

Pagespace Out / Sec

Number of page-out operations per second from the paging space.

Time Zone Summary table

This table displays the time zone summary information for a selected server and contains the following elements:

TIME ZONE (REPORT)

The time zone used when displaying the report. The format is UTC -5:00.

START DATE TIME (REPORT)

The start date and time in reporting the time zone. The start date and time is the latest time between the start date and time in the prompt and the earliest date and time of available data in the database.

END DATE TIME (REPORT)

The end date and time in reporting the time zone. The start date and time is the earliest time between the end date and time in the prompt and the latest date and time of available data in the database.

TIME ZONE (SERVER OS)

The local time of the selected server. The format is GMT -5:00

Important: the server time zone is the time zone of the server OS and not the time of where the server is physically located.

START DATE TIME (SERVER OS)

The start date and time in the selected server local time zone. The start date and time is the latest time between the start date and the time in the prompt and the earliest date and time of available data in the database.

END DATE TIME (SERVER OS)

The end date and time in the selected server local time zone. The end date and time is the earliest time between the end date and the time in the prompt and the latest date and time of available data in the database.

Parent topic: Report and workspace reference

Memory: CSA/ECSA/SQA/ESQA Utilization This report provides information on detailed memory utilization to help you decide whether you need to adjust and rebalance memory allocations to prevent costly system outages.reportsCSA/ECSA/SQA/ESQA Utilizationmemory reportsCSA/ECSA/SQA/ESQA UtilizationThe report shows the peak (maximum) utilization for common virtual storage areas.Common service area (CSA)Extended common service area (ECSA)System queue area (SQA)Extended system queue area (ESQA)System outages can occur when CSA or ECSA storage is exhausted, so it is critical to monitor the CSA, ECSA, SQA, and ESQA utilization.Memory: CSA/ECSA/SQA/ESQA Utilization sample reportMemory: CSA/ECSA/SQA/ESQA Utilization sample reportSource tables[Presentation View].[CSA/ECSA/SQA/ESQA Usage Metrics]Drill through to the following reportsNo drill through available.Standard prompts:date rangetime rangeinterval bandshowReport-specific promptssystemThe Memory: CSA/ECSA/SQA/ESQA Utilization report contains the following information:systemThe z/OS system ID.CSAThe maximum percentage of CSA used below the 16MB lineECSAThe maximum percentage of CSA used above the 16MB line. SQAThe maximum percentage of SQA used below the 16MB line. ESQAThe maximum percentage of SQA used above the 16MB line.Report and workspace referenceIBM Capacity Management Analytics provides several predefined reports and workspaces that let you monitor and analyze the capacity of your IT services and the IT infrastructure.

Memory: CSA/ECSA/SQA/ESQA Utilization

This report provides information on detailed memory utilization to help you decide whether you need to adjust and rebalance memory allocations to prevent costly system outages.

The report shows the peak (maximum) utilization for common virtual storage areas.

Common service area (CSA)

Extended common service area (ECSA)

System queue area (SQA)

Extended system queue area (ESQA)

System outages can occur when CSA or ECSA storage is exhausted, so it is critical to monitor the CSA, ECSA, SQA, and ESQA utilization.

Figure 42. Memory: CSA/ECSA/SQA/ESQA Utilization sample report

Source tables

[Presentation View].[CSA/ECSA/SQA/ESQA Usage Metrics]

Drill through to the following reports

No drill through available.

Standard prompts:

date range

time range

interval band

show

Report-specific prompts

system

The Memory: CSA/ECSA/SQA/ESQA Utilization report contains the following information:

system

The z/OS system ID.

CSA

The maximum percentage of CSA used below the 16MB line

ECSA

The maximum percentage of CSA used above the 16MB line.

SQA

The maximum percentage of SQA used below the 16MB line.

ESQA

The maximum percentage of SQA used above the 16MB line.

Parent topic: Report and workspace reference

Memory: Linux for System x - Memory UsageThis report shows the installed (visible) memory, free physical memory, and free virtual memory on the server.reportsLinux on System x - Memory Usage memory reportsLinux on System x - Memory Usage Linux on System x - Memory Usage exampleLinux on System x - Memory Usage exampleSource table[Presentation View].[Linux for System X DIST Date][Presentation View].[Linux for System X DIST Hour][Presentation View].[Linux for System X DIST Time][Presentation View].[Linux for System X Memory Fact][Presentation View].[Linux for System X Image]Drill through to the following reportCPU: Linux for System x - CPU UsageThis report shows the percentage of CPU used from both a virtual and physical perspective.Standard prompts:date rangetime rangeinterval bandtime zoneshowReport-specific promptsServer NameThe Linux for System x - Memory Usage report contains the following information:server nameThe name of the server against which the report is run.FREE PHYS MEMORY(MB)The amount of physical memory currently unused and available. In a virtualized environment, this is the free memory available to the operating system.FREE VIRT MEMORY(MB)The amount of virtual memory currently unused and available.VISIBLE MEMORY SIZE(MB)The total amount of physical memory available to the operating system. This value does not necessarily indicate the true amount of physical memory, but indicates the amount that is reported as being available to the operating system.Time Zone Summary tableThis table displays the time zone summary information for a selected server and contains the following elements:TIME ZONE (REPORT)The time zone used when displaying the report. The format is UTC -5:00.START DATE TIME (REPORT)The start date and time in reporting the time zone. The start date and time is the latest time between the start date and time in the prompt and the earliest date and time of available data in the database.END DATE TIME (REPORT)The end date and time in reporting the time zone. The start date and time is the earliest time between the end date and time in the prompt and the latest date and time of available data in the database.TIME ZONE (SERVER OS)The local time of the selected server. The format is GMT -5:00the server time zone is the time zone of the server OS and not the time of where the server is physically located.START DATE TIME (SERVER OS)The start date and time in the selected server local time zone. The start date and time is the latest time between the start date and the time in the prompt and the earliest date and time of available data in the database.END DATE TIME (SERVER OS)The end date and time in the selected server local time zone. The end date and time is the earliest time between the end date and the time in the prompt and the latest date and time of available data in the database.Report and workspace referenceIBM Capacity Management Analytics provides several predefined reports and workspaces that let you monitor and analyze the capacity of your IT services and the IT infrastructure.

Memory: Linux for System x - Memory Usage

This report shows the installed (visible) memory, free physical memory, and free virtual memory on the server.

Figure 43. Linux on System x - Memory Usage example

Source table

[Presentation View].[Linux for System X DIST Date]

[Presentation View].[Linux for System X DIST Hour]

[Presentation View].[Linux for System X DIST Time]

[Presentation View].[Linux for System X Memory Fact]

[Presentation View].[Linux for System X Image]

Drill through to the following report

CPU: Linux for System x - CPU Usage

Standard prompts:

date range

time range

interval band

time zone

show

Report-specific prompts

Server Name

The Linux for System x - Memory Usage report contains the following information:

server name

The name of the server against which the report is run.

FREE PHYS MEMORY(MB)

The amount of physical memory currently unused and available. In a virtualized environment, this is the free memory available to the operating system.

FREE VIRT MEMORY(MB)

The amount of virtual memory currently unused and available.

VISIBLE MEMORY SIZE(MB)

The total amount of physical memory available to the operating system. This value does not necessarily indicate the true amount of physical memory, but indicates the amount that is reported as being available to the operating system.

Time Zone Summary table

This table displays the time zone summary information for a selected server and contains the following elements:

TIME ZONE (REPORT)

The time zone used when displaying the report. The format is UTC -5:00.

START DATE TIME (REPORT)

The start date and time in reporting the time zone. The start date and time is the latest time between the start date and time in the prompt and the earliest date and time of available data in the database.

END DATE TIME (REPORT)

The end date and time in reporting the time zone. The start date and time is the earliest time between the end date and time in the prompt and the latest date and time of available data in the database.

TIME ZONE (SERVER OS)

The local time of the selected server. The format is GMT -5:00

Important: the server time zone is the time zone of the server OS and not the time of where the server is physically located.

START DATE TIME (SERVER OS)

The start date and time in the selected server local time zone. The start date and time is the latest time between the start date and the time in the prompt and the earliest date and time of available data in the database.

END DATE TIME (SERVER OS)

The end date and time in the selected server local time zone. The end date and time is the earliest time between the end date and the time in the prompt and the latest date and time of available data in the database.

Parent topic: Report and workspace reference

Memory: Linux for System z - Memory UsageThis report shows the installed (visible) memory, free physical memory, and free virtual memory on the server.reportsLinux for System z - Memory Usage memory reportsLinux for System z - Memory Usage Memory: Linux for System z - Memory Usage sample reportMemory: Linux for System z - Memory Usage sample reportSource table[Presentation View].[Linux for System Z DIST Date][Presentation View].[Linux for System Z DIST Hour][Presentation View].[Linux for System Z DIST Time][Presentation View].[Linux for System Z Memory FACT][Presentation View].[Linux for System Z Image]Drill through to the following reportCPU: Linux for System z - CPU UsageThis report shows the percentage of CPU used from both a virtual and physical perspective. Standard prompts:date rangetime rangeinterval bandtime zoneshowReport-specific promptsServer NameThe Linux for System z - Memory Usage report contains the following information:server nameThe name of the server against which the report is run.FREE PHYS MEMORY(MB)The amount of physical memory currently unused and available. In a virtualized environment, this value is the free memory that is available to the operating system.FREE VIRT MEMORY(MB)The amount of virtual memory currently unused and available.VISIBLE MEMORY SIZE(MB)The total amount of physical memory available to the operating system. This value does not necessarily indicate the true amount of physical memory, but indicates the amount that is reported as being available to the operating system.Time Zone Summary tableThis table displays the time zone summary information for a selected server and contains the following elements:TIME ZONE (REPORT)The time zone used when displaying the report. The format is UTC -5:00.START DATE TIME (REPORT)The start date and time in reporting the time zone. The start date and time is the latest time between the start date and time in the prompt and the earliest date and time of available data in the database.END DATE TIME (REPORT)The end date and time in reporting the time zone. The start date and time is the earliest time between the end date and time in the prompt and the latest date and time of available data in the database.TIME ZONE (SERVER OS)The local time of the selected server. The format is GMT -5:00the server time zone is the time zone of the server OS and not the time of where the server is physically located.START DATE TIME (SERVER OS)The start date and time in the selected server local time zone. The start date and time is the latest time between the start date and the time in the prompt and the earliest date and time of available data in the database.END DATE TIME (SERVER OS)The end date and time in the selected server local time zone. The end date and time is the earliest time between the end date and the time in the prompt and the latest date and time of available data in the database.Report and workspace referenceIBM Capacity Management Analytics provides several predefined reports and workspaces that let you monitor and analyze the capacity of your IT services and the IT infrastructure.

Memory: Linux for System z - Memory Usage

This report shows the installed (visible) memory, free physical memory, and free virtual memory on the server.

Figure 44. Memory: Linux for System z - Memory Usage sample report

Source table

[Presentation View].[Linux for System Z DIST Date]

[Presentation View].[Linux for System Z DIST Hour]

[Presentation View].[Linux for System Z DIST Time]

[Presentation View].[Linux for System Z Memory FACT]

[Presentation View].[Linux for System Z Image]

Drill through to the following report

CPU: Linux for System z - CPU Usage

Standard prompts:

date range

time range

interval band

time zone

show

Report-specific prompts

Server Name

The Linux for System z® - Memory Usage report contains the following information:

server name

The name of the server against which the report is run.

FREE PHYS MEMORY(MB)

The amount of physical memory currently unused and available. In a virtualized environment, this value is the free memory that is available to the operating system.

FREE VIRT MEMORY(MB)

The amount of virtual memory currently unused and available.

VISIBLE MEMORY SIZE(MB)

The total amount of physical memory available to the operating system. This value does not necessarily indicate the true amount of physical memory, but indicates the amount that is reported as being available to the operating system.

Time Zone Summary table

This table displays the time zone summary information for a selected server and contains the following elements:

TIME ZONE (REPORT)

The time zone used when displaying the report. The format is UTC -5:00.

START DATE TIME (REPORT)

The start date and time in reporting the time zone. The start date and time is the latest time between the start date and time in the prompt and the earliest date and time of available data in the database.

END DATE TIME (REPORT)

The end date and time in reporting the time zone. The start date and time is the earliest time between the end date and time in the prompt and the latest date and time of available data in the database.

TIME ZONE (SERVER OS)

The local time of the selected server. The format is GMT -5:00

Important: the server time zone is the time zone of the server OS and not the time of where the server is physically located.

START DATE TIME (SERVER OS)

The start date and time in the selected server local time zone. The start date and time is the latest time between the start date and the time in the prompt and the earliest date and time of available data in the database.

END DATE TIME (SERVER OS)

The end date and time in the selected server local time zone. The end date and time is the earliest time between the end date and the time in the prompt and the latest date and time of available data in the database.

Parent topic: Report and workspace reference

Memory: Windows - Memory UsageThis report shows how much memory is installed and the amount of free memory that is available for the server.reportsWindows - Memory Usagememory reportsWindows - Memory UsageWindows - Memory Usage exampleWindows - Memory Usage exampleSource table[Presentation View].[WIN DIST DATE][Presentation View].[WIN DIST HOUR][Presentation View].[WIN DIST TIME][Presentation View].[WIN Memory Fact][Presentation View].[WIN IMAGE]Drill through to the following reportCPU: Windows - CPU UsageThis report shows the percentage of CPU used from both a virtual and physical perspective.Standard prompts:date rangetime rangeinterval bandtime zoneshowReport-specific promptsServer NameThe Windows - Memory Usage report contains the following information:server nameThe name of the server against which the report is run.MEMORY SIZE AVAIL(MB)Amount of free and available memory on this system.MEMORY SIZE INSTALLED(MB)The amount of memory that is installed on this system.Time Zone Summary tableThis table displays the time zone summary information for a selected server and contains the following elements:TIME ZONE (REPORT)The time zone used when displaying the report. The format is UTC -5:00.START DATE TIME (REPORT)The start date and time in reporting the time zone. The start date and time is the latest time between the start date and time in the prompt and the earliest date and time of available data in the database.END DATE TIME (REPORT)The end date and time in reporting the time zone. The start date and time is the earliest time between the end date and time in the prompt and the latest date and time of available data in the database.TIME ZONE (SERVER OS)The local time of the selected server. The format is GMT -5:00the server time zone is the time zone of the server OS and not the time of where the server is physically located.START DATE TIME (SERVER OS)The start date and time in the selected server local time zone. The start date and time is the latest time between the start date and the time in the prompt and the earliest date and time of available data in the database.END DATE TIME (SERVER OS)The end date and time in the selected server local time zone. The end date and time is the earliest time between the end date and the time in the prompt and the latest date and time of available data in the database.Report and workspace referenceIBM Capacity Management Analytics provides several predefined reports and workspaces that let you monitor and analyze the capacity of your IT services and the IT infrastructure.

Memory: Windows - Memory Usage

This report shows how much memory is installed and the amount of free memory that is available for the server.

Figure 45. Windows - Memory Usage example

Source table

[Presentation View].[WIN DIST DATE]

[Presentation View].[WIN DIST HOUR]

[Presentation View].[WIN DIST TIME]

[Presentation View].[WIN Memory Fact]

[Presentation View].[WIN IMAGE]

Drill through to the following report

CPU: Windows - CPU Usage

Standard prompts:

date range

time range

interval band

time zone

show

Report-specific prompts

Server Name

The Windows - Memory Usage report contains the following information:

server name

The name of the server against which the report is run.

MEMORY SIZE AVAIL(MB)

Amount of free and available memory on this system.

MEMORY SIZE INSTALLED(MB)

The amount of memory that is installed on this system.

Time Zone Summary table

This table displays the time zone summary information for a selected server and contains the following elements:

TIME ZONE (REPORT)

The time zone used when displaying the report. The format is UTC -5:00.

START DATE TIME (REPORT)

The start date and time in reporting the time zone. The start date and time is the latest time between the start date and time in the prompt and the earliest date and time of available data in the database.

END DATE TIME (REPORT)

The end date and time in reporting the time zone. The start date and time is the earliest time between the end date and time in the prompt and the latest date and time of available data in the database.

TIME ZONE (SERVER OS)

The local time of the selected server. The format is GMT -5:00

Important: the server time zone is the time zone of the server OS and not the time of where the server is physically located.

START DATE TIME (SERVER OS)

The start date and time in the selected server local time zone. The start date and time is the latest time between the start date and the time in the prompt and the earliest date and time of available data in the database.

END DATE TIME (SERVER OS)

The end date and time in the selected server local time zone. The end date and time is the earliest time between the end date and the time in the prompt and the latest date and time of available data in the database.

Parent topic: Report and workspace reference

SCA: LPAR MSU Utilization This report helps you understand and forecast your costs based on your MSU usage. You can compare your historical MSU usage with the forecasted usage to see upcoming trends and take proactive action to adjust your workload and capacity limits to help control your bill.reportsLPAR MSU UtilizationSCA reportsLPAR MSU UtilizationThe report shows MSU utilization metrics from an LPAR perspective. It includes the following metrics:LPAR utilization MSULPAR 4 Hour Rolling Average (4HRA) utilization MSU—The 4 hour rolling average of the MSU that is used by the LPAR each hour compared with the forecast and the recommended optimization.LPAR billable MSUProduct MSU utilizationThe report displays data for each Software Cost Analysis scenario: Observed (can also display data at the SMF collection interval) ForecastedOptimizedSCA: LPAR MSU Utilization exampleSCA: LPAR MSU Utilization exampleSub-capacity pricing has several pricing structures. One of the more popular structures is WLC which prices the product based on the MSU usage of the entire LPAR. This pricing model presents the quesion of what is driving the LPAR’s usage and thus driving the cost of the WLC-based products on this LPAR. The report provides the LPAR 4HRA peak for the selected date range. It also provides a breakdown of all of the registered product’s MSU usage during each interval. The report totals the MSUs that are used by all products and subtracts that value from the LPAR MSU usage to arrive at the MSU usage of all other things on the LPAR, which is called the "Other" bucket. The Observed view shows which products are driving the usage of the LPAR. Comparing the overall LPAR MSU usage and individual product MSU usage provides some guidance as to the cost effectiveness of running each product on this LPAR.The forecast view provides a forecast for each individual product, including the "Other" bucket. You can select all products and see the breakdown of Observed MSU and the aggregate forecast of all products. Based on the Observed MSU usage of a select set of products, you might want to see the forecast for just those products. The report provides the aggregate MSU forecast for the group of products that you select. If the "Other" bucket represents a large portion of the MSU usage on this LPAR, then you might want to move some of that workload to another LPAR to reduce the WLC costs for this LPAR.Source tables and views[Presentation View].[Timezone Metrics][Presentation View].[Prompts/Product Prompt for LPAR MSU Report View] [Presentation View].[Prompts/LPAR MSU Prompt View] [Presentation View].[Product MSU Forecast Metrics] [Presentation View].[Product MSU Optimization Metrics] [Presentation View].[Raw LPAR MSU Utilization Metrics] [Presentation View].[Raw Product MSU Utilization Metrics] [Presentation View].[Raw LPAR Product MSU Utilization Metrics] [Presentation View].[LPAR MSU Metrics] [Presentation View].[LPAR MSU Optimization Metrics] [Presentation View].[LPAR MSU Forecast Metrics] [Presentation View].[Movement Suggestion by Optimization Metrics][Presentation View].[LPAR Level Metrics] Drill through to the following reportsCPU: MIPS Used - zServer/LPAR LevelThis report shows the observed average or peak CPU MIPS used at the zServer/CPC level and the forecasted average or peak CPU MIPS used along with their confidence interval. The average CPU MIPS used (observed and forecasted) and peak CPU MIPS used (observed and forecasted) can be displayed for each processor type (CP, zIIP, zAAP, IFL, or ICF) at each interval band and is broken down to the LPAR level. The forecasted peak CPU MIPS used can be displayed for each processor type at each interval band on the CPC level.CPU: MIPS Used - Service Class Period Level This report provides a comprehensive view of all of the applications that are running across the entire systems landscape. This helps you quickly rebalance system allocation and to focus on the most critical applications.SCA: NO89 Product MSU and PriceThis report helps you understand and forecast your costs based on your MSU usage. It also shows you the monetary cost of your MSU for each NO89 product. You can compare the historical MSU usage with the forecasted usage, see upcoming trends, and take proactive measures to adjust your workload and capacity limits to help control your costs.SCA: Product MSU and PriceThis report shows a registered IBM product's billable MSUs and monetary cost from a single CPC perspective for each scenario: Observed, Forecasted, and Optimized.Standard promptsdate rangetime rangeshow first SCRT cycletime zoneshowReport-specific promptsscenariocpu serial nolpar_system_idproductSMF collection intervalThe scenario prompt is independent of other prompts. This means that the values in other prompts, such as cpu serial no and lpar system id, will include all the possible values of the three scenarios. This also means you might sometimes see no data available even when there are cpu serial no, lpar system id, and product values are available in prompts.All the charts and tables that are shown in this report are in the hourly interval band except when you select the SMF collection interval. When you select the SMF collection interval, another chart (Observed Product MSU Utilization in SMF Collection Interval) reports the product MSU utilization in SMF collection interval, and more columns are added to the Observed LPAR MSU Utilization table to show the observed LPAR and product MSU Utilization in SMF Collection Interval. Only the observed scenario has data in the SMF collection interval; forecasted and optimized scenarios are in the hourly interval band.The LPAR MSU Utilization report includes the following charts and tables. Summary table The Summary table shows the summary information about the percent of data that is collected and highest MSU. This table is always displayed if any scenario is selected. The table includes the following information: CATEGORYThe scenario name: Observed, Forecasted or Optimized.INPUT DATA STARTThe earliest date with data in the database during the selected date range.INPUT DATA ENDThe latest date with data in the database during the selected date range.REPORT PERIOD % DATAThe time period with data in the database for this LPAR vs. the selected period. In the observed scenario, the data is from the DRL.MVSPM_LPAR_MSU_T table, which is from SMF70 records. In the forecasted scenario, the data is from the HCM.LPAR_MSU_FORECAST table, which is the result of forecast streams. In the optimized scenario, the data is from the HCM.LPAR_MSU_OPTIMIZATION table, which is the result of optimization streams. BELOW 95%If the value of REPORT PERIOD % DATA is below 95%, then YES, otherwise NO. This value and the related REPORT PERIOD % DATA are highlighted by red font color.HIGHEST MSUThe highest billable MSU on this LPAR during this period.HIGHEST DATE/TIMEThe date and time at which the highest billable MSU first occurred. Time Zone Summary tableThis table displays the time zone summary information for a selected LPAR and contains the following elements:TIME ZONE (REPORT)The time zone used when displaying the report. The format is UTC -5:00.START DATE TIME (REPORT)The start date and time in reporting the time zone. The start date and time is the latest time between the start date and time in the prompt and the earliest date and time of available data in the database.END DATE TIME (REPORT)The end date and time in reporting the time zone. The start date and time is the earliest time between the end date and time in the prompt and the latest date and time of available data in the database.TIME ZONE (LPAR OS)The local time of the selected server. The format is GMT -5:00the LPAR time zone is the time zone of the server OS and not the time of where the server is physically located.START DATE TIME (LPAR OS)The start date and time in the selected server local time zone. The start date and time is the latest time between the start date and the time in the prompt and the earliest date and time of available data in the database.END DATE TIME (LPAR OS)The end date and time in the selected server local time zone. The end date and time is the earliest time between the end date and the time in the prompt and the latest date and time of available data in the database.Observed/Forecasted/Optimized LPAR MSU Utilization charts and tablesThese charts and tables show MSU utilization metrics in different scenarios from an LPAR perspective. Metrics include LPAR utilization MSU, LPAR 4HRA utilization MSU, LPAR billable MSU, and product MSU utilization for each scenario. Clicking any of these charts or tables drills through to the Product MSU and Price report.The charts and tables include the following information:PRODUCT UTILIZED MSUThe MSU utilized by the selected product at each hour.LPAR UTILIZED MSU/LPAR MSU (FORECASTED)/LPAR MSU (OPTIMIZED) The MSU LPAR utilized at each hour, which can be compared with the forecast and the recommended optimization. If the values of a scenario are all NULL, then in the chart, the line is not shown; in the table, this column is not shown for this scenario. LPAR 4HRA MSU/ LPAR 4HRA MSU (FORECASTED) / LPAR 4HRA MSU(OPTIMIZED) The 4 hour rolling average of the MSU being used by the LPAR each hour, which can be compared with the forecast and recommended optimization. If the values of a scenario are all NULL, then in the chart, the line is not shown; in the table, this column is not shown for this scenario. DEFINED CAPACITY/ DEFINED CAPACITY (FORECASTED)/ DEFINED CAPACITY (OPTIMIZED) The defined capacity of this LPAR at each hour of the related scenario. If some or all values of a scenario are NULL, then in the chart, the line is not shown; in the table, this value is shown as 0 for this scenario. LPAR BILLABLE MSU / LPAR BILLABLE MSU (FORECASTED) / LPAR BILLABLE MSU (OPTIMIZED) The billable MSU of LPAR that is the minimum value of LPAR 4HRA MSU and defined capacity at each hour compared with the forecast and the recommended optimization. If the values of a scenario are all NULL, then in the chart, the line is not shown; in the table, this column is not shown for this scenario. PEAK BILLABLE MSU The peak billable MSU point during the specified date and time range of the selected scenario. This metric appears only in charts. If LPAR BILLABLE MSU values of a scenario are all NULL, then the point is not shown. GROUP CAPACITY NAME The name of the group the LPAR is in at each hour. This metric appears only in tables. If some or all values of a scenario are NULL, then the value is shown as N/A for this scenario. GROUP CAPACITY LIMIT The upper capacity limit of the group the LPAR is in at each hour. This metric appears only in tables. If some or all values of a scenario are NULL, the value is shown as 0 for this scenario. IS HIGHEST BILLABLE MSU If the LPAR BILLABLE MSU is the highest MSU during the specified date and time range, then the value is YES, and the font color of LPAR BILLABLE MSU and this field is red. Otherwise, the value is NO. This metric appears only in tables. If LPAR BILLABLE MSU values of a scenario are all NULL, all values are NO for this scenario. PROC CAPACITY / PROC CAPACITY (FORECASTED) / PROC CAPACITY (OPTIMIZED) The MSU rating of the LPAR using the logical CP count. If this value is double the maximum of the total MIPS usage, then this line does not appear in the chart. Showing the line would make the chart appear small. If the values of a scenario are all NULL, then the line is not shown in the chart and this column is not shown in the table. GUARANTEED CAPACITY The guaranteed capacity in MSU on this LPAR. This value is calculated according to the weight assigned to the LPAR. If the LPAR uses dedicated processors, then the value is shown as N/A. If this value is double the maximum of the total MIPS usage, then this line does not appear in the chart. Showing the line would make the chart appear small. The product utilized MSU value is retrieved from the DRL.MVSPM_PROD_T table, which is from SMF89 records. The product utilized MSU for some products might always be 0. In this case, the report is not displayed these products in charts or tables.In charts, the product utilized MSU of each scenario is shown in the chart and table of the related scenario. So, observed product utilized MSU is shown in the chart and table for the observed scenario; forecasted product utilized MSU is shown in the chart and table for the forecasted scenario; optimized product utilized MSU is shown in the chart and table for the optimized scenario.In charts, there is always a legend for product utilized MSU. If the product selected has no data or 0 MSU, PRODUCT UTILIZED MSU still appears in the legend.In charts, if defined capacity is not set or is 0, then the line is not shown in the chart but DEFINED CAPACITY still appears in the legend.In the Observed LPAR MSU Utilization table, products are grouped by IBM CORP or other vendor’s name.There is a product that is named OTHERS whose MSU is the MSU utilized by NO89 products, non-IBM products, and LPAR management on the specified LPAR. In the chart and table for the Observed scenario, only observed data is shown. For the Forecasted scenario, in addition to forecasted data, OBSERVED LPAR BILLABLE MSU is also shown in the chart as a dotted line. OBSERVED LPAR UTILIZED MSU, LPAR 4HRA MSU, LPAR BILLABLE MSU, and IS HIGHEST BILLABLE MSU are also shown in the table. For the Optimized scenario, in addition to optimized data, FORECASTED LPAR BILLABLE MSU is also shown in the chart as a dotted line. FORECASTED LPAR UTILIZED MSU, LPAR 4HRA MSU, LPAR BILLABLE MSU, and IS HIGHEST BILLABLE MSU are also shown in the table. Observed Product MSU Utilization in SMF Collection Interval chart and table This chart and table show product MSU utilization at SMF collection interval in the Observed scenario from an LPAR perspective. Clicking any of these charts or tables drills through to the Product MSU and Price report. The chart displays the PRODUCT UTILIZED MSU metric, which is the MSU utilized by the selected product at each SMF collection interval. The table displays all the same metrics in the Observed LPAR MSU Utilization table, as well as the product utilized MSU for each product and LPAR MSU utilization at each SMF collection interval. This chart and table apply only to Observed scenario. When you select the SMF Collection Interval option, this chart appears in addition to the Observed LPAR MSU Utilization chart. When you select the SMF Collection Interval option, the Observed LPAR MSU Utilization table is replaced by this table. Suggested Actions in Optimized Scenario table This table shows the suggested actions that are related to the selected LPAR from the optimization stream's result. FROM CPU SERIAL NOThe CPU Serial No from which it is suggested to move the product's MSU.FROM LPARThe LPAR name from which it is suggested to move the product's MSU.FROM SYSTEMThe system ID from which it is suggested to move the product's MSU.TO CPU SERIAL NOThe CPU Serial No to which it is suggested to move the product's MSU.TO LPARThe LPAR to which it is suggested to move the product's MSU.TO SYSTEMThe system ID to which it is suggested to move the product's MSU.PRODUCT NAMEThe product name of the product suggested to move.PRODUCT IDThe product ID of the product suggested to move.PRICING STRUCTUREThe pricing structure of the product suggested to move.MOVE ALLIf all MSUs are suggested to be moved, then YES; otherwise, NO.MOVING MSUIf all MSUs are suggested to be moved, then N/A; otherwise, it's the number of MSU to be moved. Report and workspace referenceIBM Capacity Management Analytics provides several predefined reports and workspaces that let you monitor and analyze the capacity of your IT services and the IT infrastructure.

SCA: LPAR MSU Utilization

This report helps you understand and forecast your costs based on your MSU usage. You can compare your historical MSU usage with the forecasted usage to see upcoming trends and take proactive action to adjust your workload and capacity limits to help control your bill.

The report shows MSU utilization metrics from an LPAR perspective. It includes the following metrics:

LPAR utilization MSU

LPAR 4 Hour Rolling Average (4HRA) utilization MSU—The 4 hour rolling average of the MSU that is used by the LPAR each hour compared with the forecast and the recommended optimization.

LPAR billable MSU

Product MSU utilization

The report displays data for each Software Cost Analysis scenario:

Observed (can also display data at the SMF collection interval)

Forecasted

Optimized

Figure 46. SCA: LPAR MSU Utilization example

Sub-capacity pricing has several pricing structures. One of the more popular structures is WLC which prices the product based on the MSU usage of the entire LPAR. This pricing model presents the quesion of what is driving the LPAR’s usage and thus driving the cost of the WLC-based products on this LPAR. The report provides the LPAR 4HRA peak for the selected date range. It also provides a breakdown of all of the registered product’s MSU usage during each interval. The report totals the MSUs that are used by all products and subtracts that value from the LPAR MSU usage to arrive at the MSU usage of all other things on the LPAR, which is called the "Other" bucket. The Observed view shows which products are driving the usage of the LPAR. Comparing the overall LPAR MSU usage and individual product MSU usage provides some guidance as to the cost effectiveness of running each product on this LPAR.

The forecast view provides a forecast for each individual product, including the "Other" bucket. You can select all products and see the breakdown of Observed MSU and the aggregate forecast of all products. Based on the Observed MSU usage of a select set of products, you might want to see the forecast for just those products. The report provides the aggregate MSU forecast for the group of products that you select.

If the "Other" bucket represents a large portion of the MSU usage on this LPAR, then you might want to move some of that workload to another LPAR to reduce the WLC costs for this LPAR.

Source tables and views

[Presentation View].[Timezone Metrics]

[Presentation View].[Prompts/Product Prompt for LPAR MSU Report View]

[Presentation View].[Prompts/LPAR MSU Prompt View]

[Presentation View].[Product MSU Forecast Metrics]

[Presentation View].[Product MSU Optimization Metrics]

[Presentation View].[Raw LPAR MSU Utilization Metrics]

[Presentation View].[Raw Product MSU Utilization Metrics]

[Presentation View].[Raw LPAR Product MSU Utilization Metrics]

[Presentation View].[LPAR MSU Metrics]

[Presentation View].[LPAR MSU Optimization Metrics]

[Presentation View].[LPAR MSU Forecast Metrics]

[Presentation View].[Movement Suggestion by Optimization Metrics]

[Presentation View].[LPAR Level Metrics]

Drill through to the following reports

CPU: MIPS Used - zServer/LPAR Level

CPU: MIPS Used - Service Class Period Level

SCA: NO89 Product MSU and Price

SCA: Product MSU and Price

Standard prompts

date range

time range

show first SCRT cycle

time zone

show

Report-specific prompts

scenario

cpu serial no

lpar_system_id

product

SMF collection interval

The scenario prompt is independent of other prompts. This means that the values in other prompts, such as cpu serial no and lpar system id, will include all the possible values of the three scenarios. This also means you might sometimes see no data available even when there are cpu serial no, lpar system id, and product values are available in prompts.

All the charts and tables that are shown in this report are in the hourly interval band except when you select the SMF collection interval. When you select the SMF collection interval, another chart (Observed Product MSU Utilization in SMF Collection Interval) reports the product MSU utilization in SMF collection interval, and more columns are added to the Observed LPAR MSU Utilization table to show the observed LPAR and product MSU Utilization in SMF Collection Interval. Only the observed scenario has data in the SMF collection interval; forecasted and optimized scenarios are in the hourly interval band.

The LPAR MSU Utilization report includes the following charts and tables.

Summary table

The Summary table shows the summary information about the percent of data that is collected and highest MSU. This table is always displayed if any scenario is selected. The table includes the following information:

CATEGORY

The scenario name: Observed, Forecasted or Optimized.

INPUT DATA START

The earliest date with data in the database during the selected date range.

INPUT DATA END

The latest date with data in the database during the selected date range.

REPORT PERIOD % DATA

The time period with data in the database for this LPAR vs. the selected period. In the observed scenario, the data is from the DRL.MVSPM_LPAR_MSU_T table, which is from SMF70 records. In the forecasted scenario, the data is from the HCM.LPAR_MSU_FORECAST table, which is the result of forecast streams. In the optimized scenario, the data is from the HCM.LPAR_MSU_OPTIMIZATION table, which is the result of optimization streams.

BELOW 95%

If the value of REPORT PERIOD % DATA is below 95%, then "YES", otherwise "NO". This value and the related REPORT PERIOD % DATA are highlighted by red font color.

HIGHEST MSU

The highest billable MSU on this LPAR during this period.

HIGHEST DATE/TIME

The date and time at which the highest billable MSU first occurred.

Time Zone Summary table

This table displays the time zone summary information for a selected LPAR and contains the following elements:

TIME ZONE (REPORT)

The time zone used when displaying the report. The format is UTC -5:00.

START DATE TIME (REPORT)

The start date and time in reporting the time zone. The start date and time is the latest time between the start date and time in the prompt and the earliest date and time of available data in the database.

END DATE TIME (REPORT)

The end date and time in reporting the time zone. The start date and time is the earliest time between the end date and time in the prompt and the latest date and time of available data in the database.

TIME ZONE (LPAR OS)

The local time of the selected server. The format is GMT -5:00

Important: the LPAR time zone is the time zone of the server OS and not the time of where the server is physically located.

START DATE TIME (LPAR OS)

The start date and time in the selected server local time zone. The start date and time is the latest time between the start date and the time in the prompt and the earliest date and time of available data in the database.

END DATE TIME (LPAR OS)

The end date and time in the selected server local time zone. The end date and time is the earliest time between the end date and the time in the prompt and the latest date and time of available data in the database.

Observed/Forecasted/Optimized LPAR MSU Utilization charts and tables

These charts and tables show MSU utilization metrics in different scenarios from an LPAR perspective. Metrics include LPAR utilization MSU, LPAR 4HRA utilization MSU, LPAR billable MSU, and product MSU utilization for each scenario. Clicking any of these charts or tables drills through to the Product MSU and Price report.

The charts and tables include the following information:

PRODUCT UTILIZED MSU

The MSU utilized by the selected product at each hour.

LPAR UTILIZED MSU/LPAR MSU (FORECASTED)/LPAR MSU (OPTIMIZED)

The MSU LPAR utilized at each hour, which can be compared with the forecast and the recommended optimization.

If the values of a scenario are all NULL, then in the chart, the line is not shown; in the table, this column is not shown for this scenario.

LPAR 4HRA MSU/ LPAR 4HRA MSU (FORECASTED) / LPAR 4HRA MSU(OPTIMIZED)

The 4 hour rolling average of the MSU being used by the LPAR each hour, which can be compared with the forecast and recommended optimization.

If the values of a scenario are all NULL, then in the chart, the line is not shown; in the table, this column is not shown for this scenario.

DEFINED CAPACITY/ DEFINED CAPACITY (FORECASTED)/ DEFINED CAPACITY (OPTIMIZED)

The defined capacity of this LPAR at each hour of the related scenario.

If some or all values of a scenario are NULL, then in the chart, the line is not shown; in the table, this value is shown as 0 for this scenario.

LPAR BILLABLE MSU / LPAR BILLABLE MSU (FORECASTED) / LPAR BILLABLE MSU (OPTIMIZED)

The billable MSU of LPAR that is the minimum value of LPAR 4HRA MSU and defined capacity at each hour compared with the forecast and the recommended optimization.

If the values of a scenario are all NULL, then in the chart, the line is not shown; in the table, this column is not shown for this scenario.

PEAK BILLABLE MSU

The peak billable MSU point during the specified date and time range of the selected scenario.

This metric appears only in charts. If LPAR BILLABLE MSU values of a scenario are all NULL, then the point is not shown.

GROUP CAPACITY NAME

The name of the group the LPAR is in at each hour.

This metric appears only in tables. If some or all values of a scenario are NULL, then the value is shown as N/A for this scenario.

GROUP CAPACITY LIMIT

The upper capacity limit of the group the LPAR is in at each hour.

This metric appears only in tables. If some or all values of a scenario are NULL, the value is shown as 0 for this scenario.

IS HIGHEST BILLABLE MSU

If the LPAR BILLABLE MSU is the highest MSU during the specified date and time range, then the value is "YES", and the font color of LPAR BILLABLE MSU and this field is red. Otherwise, the value is "NO".

This metric appears only in tables. If LPAR BILLABLE MSU values of a scenario are all NULL, all values are "NO" for this scenario.

PROC CAPACITY / PROC CAPACITY (FORECASTED) / PROC CAPACITY (OPTIMIZED)

The MSU rating of the LPAR using the logical CP count.

If this value is double the maximum of the total MIPS usage, then this line does not appear in the chart. Showing the line would make the chart appear small.

If the values of a scenario are all NULL, then the line is not shown in the chart and this column is not shown in the table.

GUARANTEED CAPACITY

The guaranteed capacity in MSU on this LPAR. This value is calculated according to the weight assigned to the LPAR.

If the LPAR uses dedicated processors, then the value is shown as N/A.

If this value is double the maximum of the total MIPS usage, then this line does not appear in the chart. Showing the line would make the chart appear small.

The product utilized MSU value is retrieved from the DRL.MVSPM_PROD_T table, which is from SMF89 records. The product utilized MSU for some products might always be 0. In this case, the report is not displayed these products in charts or tables.

In charts, the product utilized MSU of each scenario is shown in the chart and table of the related scenario. So, observed product utilized MSU is shown in the chart and table for the observed scenario; forecasted product utilized MSU is shown in the chart and table for the forecasted scenario; optimized product utilized MSU is shown in the chart and table for the optimized scenario.

In charts, there is always a legend for product utilized MSU. If the product selected has no data or 0 MSU, PRODUCT UTILIZED MSU still appears in the legend.

In charts, if defined capacity is not set or is 0, then the line is not shown in the chart but DEFINED CAPACITY still appears in the legend.

In the Observed LPAR MSU Utilization table, products are grouped by IBM CORP or other vendor’s name.

There is a product that is named "OTHERS" whose MSU is the MSU utilized by NO89 products, non-IBM products, and LPAR management on the specified LPAR.

In the chart and table for the Observed scenario, only observed data is shown.

For the Forecasted scenario, in addition to forecasted data, OBSERVED LPAR BILLABLE MSU is also shown in the chart as a dotted line. OBSERVED LPAR UTILIZED MSU, LPAR 4HRA MSU, LPAR BILLABLE MSU, and IS HIGHEST BILLABLE MSU are also shown in the table.

For the Optimized scenario, in addition to optimized data, FORECASTED LPAR BILLABLE MSU is also shown in the chart as a dotted line. FORECASTED LPAR UTILIZED MSU, LPAR 4HRA MSU, LPAR BILLABLE MSU, and IS HIGHEST BILLABLE MSU are also shown in the table.

Observed Product MSU Utilization in SMF Collection Interval chart and table

This chart and table show product MSU utilization at SMF collection interval in the Observed scenario from an LPAR perspective. Clicking any of these charts or tables drills through to the Product MSU and Price report.

The chart displays the PRODUCT UTILIZED MSU metric, which is the MSU utilized by the selected product at each SMF collection interval.

The table displays all the same metrics in the Observed LPAR MSU Utilization table, as well as the product utilized MSU for each product and LPAR MSU utilization at each SMF collection interval.

This chart and table apply only to Observed scenario.

When you select the SMF Collection Interval option, this chart appears in addition to the Observed LPAR MSU Utilization chart.

When you select the SMF Collection Interval option, the Observed LPAR MSU Utilization table is replaced by this table.

Suggested Actions in Optimized Scenario table

This table shows the suggested actions that are related to the selected LPAR from the optimization stream's result.

FROM CPU SERIAL NO

The CPU Serial No from which it is suggested to move the product's MSU.

FROM LPAR

The LPAR name from which it is suggested to move the product's MSU.

FROM SYSTEM

The system ID from which it is suggested to move the product's MSU.

TO CPU SERIAL NO

The CPU Serial No to which it is suggested to move the product's MSU.

TO LPAR

The LPAR to which it is suggested to move the product's MSU.

TO SYSTEM

The system ID to which it is suggested to move the product's MSU.

PRODUCT NAME

The product name of the product suggested to move.

PRODUCT ID

The product ID of the product suggested to move.

PRICING STRUCTURE

The pricing structure of the product suggested to move.

MOVE ALL

If all MSUs are suggested to be moved, then "YES"; otherwise, "NO".

MOVING MSU

If all MSUs are suggested to be moved, then "N/A"; otherwise, it's the number of MSU to be moved.

Parent topic: Report and workspace reference

SCA: NO89 Products Matrix This report provides a single place to review all of the NO89 products that use sub-capacity pricing. It also shows where these products are designated to be run (both the CPCs and the LPARs). It shows all of the locations where a product is running along with all of the other NO89 products to provide a planning resource for LPAR and CPC consolidations. From this report, you can also drill to the NO89 Product MSU and Price report for any product.reportsNO89 Products MatrixSCA reportsNO89 Products MatrixThis report provides a listing of LPARs and CPCs where the unregistered IBM NO89 products run.SCA: NO89 Products Matrix exampleSCA: NO89 Products Matrix exampleSource tables[Presentation View].[NO89 Products][Presentation View].[Sub-Capacity Program Pricing Structures]Drill through to the following reportsSCA: Registered Products MatrixThis report provides a listing of LPARs and CPCs where the SCA registered products are running.Report-specific promptscpu serial noThis report contains a single table, the NO89 Products Matrix table, which shows the LPARs and CPCs where the unregistered IBM NO89 products run. The table contains the following information:CPC MODEL NO-CPC SERIAL NOThe zServer/CEC model number and serial number.LPAR NAMEThe LPAR name.PRODUCT The product display name, which uses the syntax [PRODUCT_ID]_[PRODUCT_NAME]_[PRODUCT_PRICING_STRUCTURE]. For more information about naming rules, see Product display namesTo help you select products easily in report prompts, Capacity Management Analytics shows products using the product display name. . Products on LPAR Total CountThe total number of NO89 products that are running on the LPAR.LPARs for Product Total CountThe total number of LPARs that the product is running.CPCs for Product Total CountThe total number of CPCs that the product is running.In this table, if the product is running on this LPAR, then the cross point of LPAR NAME and PRODUCT is marked by a check mark Check mark icon.Report and workspace referenceIBM Capacity Management Analytics provides several predefined reports and workspaces that let you monitor and analyze the capacity of your IT services and the IT infrastructure.

SCA: NO89 Products Matrix

This report provides a single place to review all of the NO89 products that use sub-capacity pricing. It also shows where these products are designated to be run (both the CPCs and the LPARs). It shows all of the locations where a product is running along with all of the other NO89 products to provide a planning resource for LPAR and CPC consolidations. From this report, you can also drill to the NO89 Product MSU and Price report for any product.

This report provides a listing of LPARs and CPCs where the unregistered IBM® NO89 products run.

Figure 47. SCA: NO89 Products Matrix example

Source tables

[Presentation View].[NO89 Products]

[Presentation View].[Sub-Capacity Program Pricing Structures]

Drill through to the following reports

SCA: Registered Products Matrix

Report-specific prompts

cpu serial no

This report contains a single table, the NO89 Products Matrix table, which shows the LPARs and CPCs where the unregistered IBM NO89 products run. The table contains the following information:

CPC MODEL NO-CPC SERIAL NO

The zServer/CEC model number and serial number.

LPAR NAME

The LPAR name.

PRODUCT

The product display name, which uses the syntax [PRODUCT_ID]_[PRODUCT_NAME]_[PRODUCT_PRICING_STRUCTURE].

For more information about naming rules, see Product display names.

Products on LPAR Total Count

The total number of NO89 products that are running on the LPAR.

LPARs for Product Total Count

The total number of LPARs that the product is running.

CPCs for Product Total Count

The total number of CPCs that the product is running.

In this table, if the product is running on this LPAR, then the cross point of LPAR NAME and PRODUCT is marked by a check mark Check mark icon.

Parent topic: Report and workspace reference

SCA: NO89 Product MSU and Price This report helps you understand and forecast your costs based on your MSU usage. It also shows you the monetary cost of your MSU for each NO89 product. You can compare the historical MSU usage with the forecasted usage, see upcoming trends, and take proactive measures to adjust your workload and capacity limits to help control your costs.reportsNO89 Product MSU and PriceSCA reportsNO89 Product MSU and PriceThis report shows the billable MSUs for the IBM NO89 products, along with their prices.This report can identify which LPARs are driving the cost of each NO89 product. You can then view the LPAR MSU report to see which other products are driving the MSU usage up on the LPARs where the NO89 product runs. By using a variety of reports, you can see what is driving a product’s cost up. You can also determine where you can move a product to reduce costs.SCA: NO89 Product MSU and Price exampleSCA: NO89 Product MSU and Price exampleSource tables[Presentation View].[Customer Pricing Information] [Presentation View].[LPAR MSU Forecast Metrics] [Presentation View].[LPAR MSU Metrics] [Presentation View].[LPAR MSU Optimization Metrics] [Presentation View].[NO89 Products] [Presentation View].[Parents of Sub-Capacity Reference-based programs] [Presentation View].[Product Billable MSU Forecast Metrics] [Presentation View].[Product Billable MSU Optimization Metrics] [Presentation View].[Product MSU Utilization Metrics Without Z/OS] [Presentation View].[Product MSU Utilization Metrics] [Presentation View].[Prompts/LPAR MSU Prompt View] [Presentation View].[Raw Product MSU Utilization Metrics] [Presentation View].[Sub-Capacity Program Pricing Structures] [Presentation View].[Timezone Metrics]Drill through to the following reportSCA: LPAR MSU UtilizationThis report helps you understand and forecast your costs based on your MSU usage. You can compare your historical MSU usage with the forecasted usage to see upcoming trends and take proactive action to adjust your workload and capacity limits to help control your bill.SCA: Registered Products MatrixThis report provides a listing of LPARs and CPCs where the SCA registered products are running.Standard promptsdate rangetime rangeshow first SCRT cycletimezoneshowReport-specific promptsscenarioproductproduct filter (search and select product from product lists)cpu serial nolpar_system idThe scenario prompt is independent of other prompts. This means that the values in other prompts, such as cpu serial no and lpar system id, will include all the possible values of the three scenarios. This also means you may sometimes see no data available even when cpu serial no, lpar system id, and product values are available in prompts.All the charts and tables shown in this report are in hourly interval bands, except when the product is in IPLA Reference-based pricing structure. When the product is in IPLA reference-based pricing structure, the lpar_system id prompt is not available to select. All LPARs are counted in calculations.The NO89 Product MSU and Price report contains the same tables and charts available in the SCA: Product MSU and PriceThis report shows a registered IBM product's billable MSUs and monetary cost from a single CPC perspective for each scenario: Observed, Forecasted, and Optimized. except tables and charts for z/OS and the optimized product suggested actions table.Report and workspace referenceIBM Capacity Management Analytics provides several predefined reports and workspaces that let you monitor and analyze the capacity of your IT services and the IT infrastructure.

SCA: NO89 Product MSU and Price

This report helps you understand and forecast your costs based on your MSU usage. It also shows you the monetary cost of your MSU for each NO89 product. You can compare the historical MSU usage with the forecasted usage, see upcoming trends, and take proactive measures to adjust your workload and capacity limits to help control your costs.

This report shows the billable MSUs for the IBM NO89 products, along with their prices.

This report can identify which LPARs are driving the cost of each NO89 product. You can then view the LPAR MSU report to see which other products are driving the MSU usage up on the LPARs where the NO89 product runs. By using a variety of reports, you can see what is driving a product’s cost up. You can also determine where you can move a product to reduce costs.

Figure 48. SCA: NO89 Product MSU and Price example

Source tables

[Presentation View].[Customer Pricing Information]

[Presentation View].[LPAR MSU Forecast Metrics]

[Presentation View].[LPAR MSU Metrics]

[Presentation View].[LPAR MSU Optimization Metrics]

[Presentation View].[NO89 Products]

[Presentation View].[Parents of Sub-Capacity Reference-based programs]

[Presentation View].[Product Billable MSU Forecast Metrics]

[Presentation View].[Product Billable MSU Optimization Metrics]

[Presentation View].[Product MSU Utilization Metrics Without Z/OS]

[Presentation View].[Product MSU Utilization Metrics]

[Presentation View].[Prompts/LPAR MSU Prompt View]

[Presentation View].[Raw Product MSU Utilization Metrics]

[Presentation View].[Sub-Capacity Program Pricing Structures]

[Presentation View].[Timezone Metrics]

Drill through to the following report

SCA: LPAR MSU Utilization

SCA: Registered Products Matrix

Standard prompts

date range

time range

show first SCRT cycle

timezone

show

Report-specific prompts

scenario

product

product filter (search and select product from product lists)

cpu serial no

lpar_system id

The scenario prompt is independent of other prompts. This means that the values in other prompts, such as cpu serial no and lpar system id, will include all the possible values of the three scenarios. This also means you may sometimes see no data available even when cpu serial no, lpar system id, and product values are available in prompts.

All the charts and tables shown in this report are in hourly interval bands, except when the product is in IPLA Reference-based pricing structure. When the product is in IPLA reference-based pricing structure, the lpar_system id prompt is not available to select. All LPARs are counted in calculations.

The NO89 Product MSU and Price report contains the same tables and charts available in the SCA: Product MSU and Price except tables and charts for z/OS and the optimized product suggested actions table.

Parent topic: Report and workspace reference

SCA: Product MSU and Price This report shows a registered IBM product's billable MSUs and monetary cost from a single CPC perspective for each scenario: Observed, Forecasted, and Optimized.reportsProduct MSU and PriceSCA reportsProduct MSU and PriceThis report not only helps you understand and forecast your costs based on your MSU usage, but it also shows you the monetary cost of your MSU by product. You can compare the historical MSU usage with the forecasted usage and see upcoming trends. You can then take proactive action to adjust your workloads and capacity limits to help manage your bill. You can also identify which products drive the LPAR’s usage and increase the cost of other products based on LPAR MSUs as a result. You can also see how a product runs on the other LPARs across your CPC to help determine consolidation plans.This report allows you to focus on one product so that you can see its MSU usage across all LPARs on a CPC. You can quickly select another CPC and see the usage across those LPARs as well. You can also see whether the product is being used as expected on each LPAR and CPC. It is common for a product that is licensed for an LPAR to provide the occasional but necessary use of the product from that LPAR. It is also common for workload usage to change over time. This report can show you where there are opportunities to reduce your license requirements based on your product usage. This can be based on currently observed usage or historical usage.You can also use the Forecast view to see what the pattern of usage will be in the future. The Forecast view provides a separate forecast for each LPAR that you select. The aggregate forecast for the product is shown for all selected LPARs. You can also view any number of LPARs you want and see the aggregate product forecast for that set of LPARs.SCA: Product MSU and Price exampleSCA: Product MSU and Price exampleSource tables and views[Presentation View].[Customer Pricing Information] [Presentation View].[LPAR MSU Forecast Metrics] [Presentation View].[LPAR MSU Metrics] [Presentation View].[LPAR MSU Optimization Metrics] [Presentation View].[Movement Suggestion by Optimization Metrics] [Presentation View].[Product Billable MSU Forecast Metrics] [Presentation View].[Product Billable MSU Optimization Metrics] [Presentation View].[Product MSU Optimization Metrics] [Presentation View].[Product MSU Utilization Metrics Without Z/OS] [Presentation View].[Product MSU Utilization Metrics] [Presentation View].[Prompts/Product Billable MSU Prompt View] [Presentation View].[Raw Product MSU Utilization Metrics] [Presentation View].[Sub-Capacity Program Pricing Structures] [Presentation View].[Parents of Sub-Capacity Reference-based programs] [Presentation View].[Timezone Metrics] [Presentation View].[Z/OS Product MSU Utilization Metrics]Drill through to the following reportSCA: LPAR MSU UtilizationThis report helps you understand and forecast your costs based on your MSU usage. You can compare your historical MSU usage with the forecasted usage to see upcoming trends and take proactive action to adjust your workload and capacity limits to help control your bill.Standard promptsdate rangetime rangeshow first SCRT cycletimezoneshowReport-specific promptsscenarioproductproduct filter (search and select product from product lists)cpu serial nolpar_system idThe scenario prompt is independent of other prompts. This means that the values in other prompts, such as cpu serial no and lpar system id, includes all the possible values of the three scenarios. This also means you might sometimes see no data available even when cpu serial no, lpar system id, and product values are available in prompts.All the charts and tables that are shown in this report are in hourly interval bands, except when the product is in IPLA Reference-based pricing structure. When the product is in IPLA reference-based pricing structure, the lpar_system id prompt is not available to select. All LPARs are counted in calculations.The Product MSU and Price report includes the following charts and tables.Time Zone Summary tableThis table displays the time zone summary information for a selected LPAR and contains the following elements:LPAR_SYSTEM IDThe LPAR name and its system ID.TIME ZONE (REPORT)The time zone used when displaying the report. The format is UTC -5:00.START DATE TIME (REPORT)The start date and time in reporting the time zone. The start date and time is the latest time between the start date and time in the prompt and the earliest date and time of available data in the database.END DATE TIME (REPORT)The end date and time in reporting the time zone. The start date and time is the earliest time between the end date and time in the prompt and the latest date and time of available data in the database.TIME ZONE (LPAR OS)The local time of the selected LPAR. The format is GMT -5:00the LPAR time zone is the time zone of the LPAR OS and not the time of where the server is physically located.START DATE TIME (LPAR OS)The start date and time in the selected LPAR local time zone. The start date and time is the latest time between the start date and the time in the prompt and the earliest date and time of available data in the database.END DATE TIME (LPAR OS)The end date and time in the selected LPAR local time zone. The end date and time is the earliest time between the end date and the time in the prompt and the latest date and time of available data in the database.Observed/Forecasted/Optimized Product MSU charts and tablesThese charts and tables show a registered IBM product's billable MSUs for each scenario from the perspective of a single CPC and the LPARs it contains. You can click a chart to drill through to the LPAR MSU Utilization report.The Optimized Product MSU charts and tables are not shown if the product is in IPLA Reference-based pricing structure.In the chart and table for the Observed scenario, only observed data is shown. For the Forecasted scenario, in addition to forecasted data, OBSERVED BILLABLE MSU, and Tier Level N Maximum/Minimum MSU Value for Observed are also shown in the chart as a dotted line, while OBSERVED BILLABLE MSU and IS HIGHEST are also shown in the table. For the Optimized scenario, in addition to optimized data, FORECASTED BILLABLE MSU, and Tier Level N Maximum/Minimum MSU Value for Forecasted are also shown in the chart as a dotted line, while FORECASTED BILLABLE MSU and IS HIGHEST are also shown in the table.For z/OS products, there are no Tier Level N Maximum/Minimum MSU Value lines in the chart.The Product MSU charts and tables include the following information:CPU SERIAL NOThe zServer/CEC serial number.LPAR SYSTEMThe LPAR name and system ID.PRODUCTThe product display name. For details on display name rules, see Product display namesTo help you select products easily in report prompts, Capacity Management Analytics shows products using the product display name. .BILLABLE MSUTOTAL BILLABLE MSU: the product billable MSU on a CPC at each hour.LPAR BILLABLE MSU: the product billable MSU on each LPAR at each hour. Only selected LPARs are shown in the chart and table.The calculation formula varies according to each pricing structure. If there are no records for a product at some hour, then in the table, the cell is empty. If the values of a scenario are all NULL, then in the chart, the line is not shown; in the table, this column is not shown for this scenario.PEAK MSUThe peak billable MSU point during the date and time range that is selected for the related scenario. This metric appears only in charts. If BILLABLE MSU values of a scenario are all NULL, the point is not shown.MSU AS IF EXECUTION-BASEDThe billable MSU value by assuming the product uses execution-based pricing structure. This metric appears only in the table for the Observed scenario. DIFFERENCEThe difference between BILLABLE MSU and MSU AS IF EXECUTION-BASED, which shows the amount of MSU you save from using the current pricing structure. This metric appears only in tables.IS HIGHESTIf the product BILLABLE MSU is the highest MSU during the selected date and time range, then the value is YES, and the product BILLABLE MSU and this field appear in red. Otherwise, the value is NO. This metric exists only in tables. If product BILLABLE MSU values of a scenario are all NULL, the value is shown as NO for this scenario.Tier Level N Maximum MSU Value The maximum MSU value of the tier to which the products’ highest MSU belongs. This metric appears only in charts. If BILLABLE MSU values of a scenario are all NULL, or the value of Maximum MSU is not defined, then the line is not shown in the chart, and the legend label for this line is displayed as "Tier Level N/A Maximum MSU Value ([SCENARIO_NAME])", Tier Level N Minimum MSU Value The minimum MSU value of the tier to which the products’ highest MSU belongs. This metric appears only in charts. If BILLABLE MSU values of a scenario are all NULL, then the line is not shown in the chart, and the legend label for this line is displayed as "Tier Level N/A Minimum MSU Value ([SCENARIO_NAME])". Observed/Forecasted/Optimized Price Summary tablesThese tables show an IBM product's billable MSUs and the monetary cost from a single CPC perspective for each of the observed, forecasted, and optimized scenarios. The displayed IPLA MSUs and price are only for the chosen CPC and not for the entire enterprise. To view IPLA MSUs and price for the entire enterprise, see the Software Cost Analysis Summary workspaceManaging System z capacity can be costly and complex. This workspace allows you to better manage your software costs on System z by providing graphs and tables that show both the details and the summary of the software costs..The Price Summary tables require that price values are correctly loaded into the HCM.CUSTOMER_PRICE table to display related monetary values. If price values are not loaded in the HCM.CUSTOMER_PRICE table, a No data available message appears in the Price Summary tables.The Price Summary tables contain the following information:ENTERPRISEThe enterprise name.CPU SERIAL NOThe zServer/CEC serial number.PROD NAME:The product name.PROD ID:The product ID.CATEGORYSub-capacity pricing of Monthly License Charge (MLC) or System z International Product License Agreement (IPLA).LICENSE CHARGEMonthly License Charge metrics name of MLC products or Value Unit Exhibit name of IPLA products.HIGHEST MSUThe peak billable MSU point during the specified date and time range of the related scenario. This metric exists only in the table for MLC products.TIER LEVELThe tier to which the highest MSU belongs. For MLC products, the tier is the one within a License Charge; for IPLA products, the tier is the one within a Value Unit Exhibit. TIER MIN MSUThe minimum MSU of the TIER LEVEL.TIER MAX MSUThe maximum MSU of the TIER LEVEL. If the value is not defined, then this column is not shown.ASSIGNED ENTITLED MSUThe assigned entitled MSU of the CPC. This metric exists only in the table for IPLA products.ASSIGNED ENTITLED VUThe assigned entitled VU of the CPC. This metric exists only in the table for IPLA products.TOTAL USAGE MSUThe peak billable MSU point during the specified date and time range of the related scenario. This metric exists only in the table for IPLA products.TOTAL USAGE VUThe usage of VU that is converted from TOTAL USAGE MSU. This metric exists only in the table for IPLA products.DELTA MSU(TOTAL USAGE MSU) – (ASSIGNED ENTITLED MSU). This metric exists only in the table for IPLA products.DELTA VU(TOTAL USAGE VU) - (ASSIGNED ENTITLED VU). This metric exists only in the table for IPLA products.MONETARY ENTITLEDThe assigned entitled monetary value of the CPC. This metric exists only in the table for IPLA products.MONETARY USAGEThe billable monetary value. If DELTA VU is negative, then (MONETARY USAGE) = (MONETARY ENTITLED); otherwise, (MONETARY USAGE) = (DELTA VU * PRICE PER VU). This metric exists only in the table for IPLA products.MONETARY DELTA(MONETARY USAGE) – (MONETARY ENTITLED). This metric exists only in the table for IPLA products.MONETARY SSS&S monetary value. MONETARY SS PER MONTH * the natural months of the specified date range. This metric exists only in the table for IPLA products.TOTAL Total monetary value. For IPLA products, if (MONETARY DELTA) is more than 0, then (MONETARY DELTA) + (MONETARY SS); otherwise,MONETARY SS. For MLC products, the total monetary value that is calculated by the method of its license charge.Observed/Forecasted/Optimized Parent Billable MSU chart and Billable MSU Summary for Reference-Based Products tableThese charts and tables show the billable MSUs for each parent product that has IBM Reference-Based charging from a single CPC perspective for each of the observed, forecasted, and optimized scenarios.These charts and tables exist only for IPLA Reference-Based products. If there is no record for any parent of a child product, then the chart or table displays a No data available message.You can click a parent product name in the Billable MSU Summary for Reference-Based Products table, or click a piece of the pie of the Parent Billable MSU chart, to drill through to the SCA: Product MSU and PriceThis report shows a registered IBM product's billable MSUs and monetary cost from a single CPC perspective for each scenario: Observed, Forecasted, and Optimized..The charts and tables contain the following information:PARENT PEAK MSU The peak MSU of each parent product during the specified date and time range.CHILD BILLABLE MSUThe total MSU of all the parents' peak MSU. PEAK ZOS MSUThe peak MSU of z/OS on the CPC during the specified date and time range.BILLABLE MSUThe final billable MSU that is the minimum value between CHILD BILLABLE MSU and PEAK ZOS MSU.Observed/Forecasted/Optimized z/OS MSU chart and zNALC/Traditional Workloads tablesThese charts and tables show MSU summary and details for zNALC and traditional workloads for a z/OS product from a single CPC.There is one chart that is named z/OS MSU chart, and three tables: MSU Summary for zNALC and Traditional Workload table, Price Summary for zNALC and Traditional Workload table, and Billable MSU Details for zNALC and Traditional Workloads table.These charts and tables exist only for z/OS products. Particularly, the MSU Summary for zNALC and Traditional Workload table and Price Summary for zNALC and Traditional Workload table exist only when there are both zNALC and Traditional workloads are running on the CPC during the specified date and time range. In the z/OS MSU chart and Billable MSU Details for zNALC and Traditional Workloads table, for the Observed scenario, only observed data is shown. For the Forecasted scenario, in addition to forecasted data, OBSERVED BILLABLE MSU is also shown in the z/OS MSU chart as a dotted line, while OBSERVED BILLABLE MSU and IS HIGHEST appear in the Billable MSU Details for zNALC and Traditional Workloads table. For the Optimized scenario, in addition to optimized data, FORECASTED BILLABLE MSU is also shown in the z/OS MSU chart as a dotted line, while FORECASTED BILLABLE MSU and IS HIGHEST appear in the Billable MSU Details for zNALC and Traditional Workloads table.The Price Summary for zNALC and Traditional Workload tables requires that price values are correctly loaded into the HCM.CUSTOMER_PRICE table to display related monetary values. If price values are not loaded in the HCM.CUSTOMER_PRICE table, a No data available message appears in the tables.The MSU Summary for zNALC and Traditional Workload table contains the following information:CPU SERIAL NOThe zServer/CEC serial number. PROD IDThe product ID. PROD NAMEThe product name. FULL CAPACITY MSURated capacity of the CPC.z/OS HIGHEST MSUThe highest MSU of the z/OS product regardless of zNALC workloads or traditional workloads.TRAD HIGHEST MSUThe highest MSU of the z/OS product for traditional workloads. zNALC HIGHEST MSUThe highest MSU of the z/OS product for zNALC workloads.CHARGED TRAD MSUThe MSU used in monetary value calculations for traditional workloads.CHARGED zNALC MSUThe MSU used in monetary value calculations for zNALC workloads.The Price Summary for zNALC and Traditional Workload table contains the following information:LICENSE CHARGE Monthly License Charge metrics name. CHARGED MSUThe MSU used in monetary value calculations. TIER LEVELThe tier within a License Charge to which the CHARGED MSU belongs.TIER MIN MSUThe minimum MSU of the TIER LEVEL. TIER MAX MSUThe maximum MSU of the TIER LEVEL. If the value is not defined, then this column is not shown. MLC PRICETotal monetary value.The z/OS MSU charts and Billable MSU Details for zNALC and Traditional Workloads tables contain the following information:z/OS PROD NAMEThe z/OS product name. The z/OS product name presents the total z/OS MSU, while the z/OS product name with (zNALC) or (Traditional) presents the MSU for each type of workload. BILLABLE MSUThe sum of billable MSU for each LPAR the workload is running at each hour. If the values of a scenario are all NULL, then in the chart, the line is not shown; in the table, the column is not shown for this scenario. PEAK MSUThe peak billable MSU point during the specified date and time range of the related scenario. This metric appears only in charts. If BILLABLE MSU values of a scenario are all NULL, the point is not shown.IS HIGHESTIf the BILLABLE MSU is the highest MSU during the specified date and time range, then the value is YES and the font color of product BILLABLE MSU and this field is red. Otherwise, the value is NO. If BILLABLE MSU values of a scenario are all NULL, then the value is shown as NO for the scenario. This metric appears only in tables.Optimized Product Suggested Actions tableThis table shows the suggested actions that are related to the selected product from the optimization stream's results that are related to the selected product. The table contains the following information:PRODUCT CATEGORYSubcapacity pricing of Monthly License Charge (MLC) or System z International Product License Agreement (IPLA).PRODUCT NAMEThe product name of the product suggested to move. PRODUCT IDThe product ID of the product suggested to move.PRODUCT PRICING STRUCTUREThe pricing structure of the product suggested to move.FROM CPU SERIAL NOThe CPU Serial No from which it is suggested to move the product’s MSU.FROM LPARThe LPAR name from which it is suggested to move the product’s MSU.FROM SYSTEMThe system ID from which it is suggested to move the product’s MSU.TO CPU SERIAL NOThe CPU Serial No to which it is suggested to move the product’s MSU.TO LPARThe LPAR to which it is suggested to move the product’s MSU.TO SYSTEMThe system ID to which it is suggested to move the product’s MSU.MOVING MSUsIf all MSUs are suggested to be moved, then N/A; otherwise, it is the number of MSU to be moved.MOVE ALLIf all MSUs are suggested to be moved, then YES; otherwise, NO.Report and workspace referenceIBM Capacity Management Analytics provides several predefined reports and workspaces that let you monitor and analyze the capacity of your IT services and the IT infrastructure.

SCA: Product MSU and Price

This report shows a registered IBM® product's billable MSUs and monetary cost from a single CPC perspective for each scenario: Observed, Forecasted, and Optimized.

This report not only helps you understand and forecast your costs based on your MSU usage, but it also shows you the monetary cost of your MSU by product. You can compare the historical MSU usage with the forecasted usage and see upcoming trends. You can then take proactive action to adjust your workloads and capacity limits to help manage your bill. You can also identify which products drive the LPAR’s usage and increase the cost of other products based on LPAR MSUs as a result. You can also see how a product runs on the other LPARs across your CPC to help determine consolidation plans.

This report allows you to focus on one product so that you can see its MSU usage across all LPARs on a CPC. You can quickly select another CPC and see the usage across those LPARs as well. You can also see whether the product is being used as expected on each LPAR and CPC. It is common for a product that is licensed for an LPAR to provide the occasional but necessary use of the product from that LPAR. It is also common for workload usage to change over time. This report can show you where there are opportunities to reduce your license requirements based on your product usage. This can be based on currently observed usage or historical usage.

You can also use the Forecast view to see what the pattern of usage will be in the future. The Forecast view provides a separate forecast for each LPAR that you select. The aggregate forecast for the product is shown for all selected LPARs. You can also view any number of LPARs you want and see the aggregate product forecast for that set of LPARs.

Figure 49. SCA: Product MSU and Price example

Source tables and views

[Presentation View].[Customer Pricing Information]

[Presentation View].[LPAR MSU Forecast Metrics]

[Presentation View].[LPAR MSU Metrics]

[Presentation View].[LPAR MSU Optimization Metrics]

[Presentation View].[Movement Suggestion by Optimization Metrics]

[Presentation View].[Product Billable MSU Forecast Metrics]

[Presentation View].[Product Billable MSU Optimization Metrics]

[Presentation View].[Product MSU Optimization Metrics]

[Presentation View].[Product MSU Utilization Metrics Without Z/OS]

[Presentation View].[Product MSU Utilization Metrics]

[Presentation View].[Prompts/Product Billable MSU Prompt View]

[Presentation View].[Raw Product MSU Utilization Metrics]

[Presentation View].[Sub-Capacity Program Pricing Structures]

[Presentation View].[Parents of Sub-Capacity Reference-based programs]

[Presentation View].[Timezone Metrics]

[Presentation View].[Z/OS Product MSU Utilization Metrics]

Drill through to the following report

SCA: LPAR MSU Utilization

Standard prompts

date range

time range

show first SCRT cycle

timezone

show

Report-specific prompts

scenario

product

product filter (search and select product from product lists)

cpu serial no

lpar_system id

The scenario prompt is independent of other prompts. This means that the values in other prompts, such as cpu serial no and lpar system id, includes all the possible values of the three scenarios. This also means you might sometimes see no data available even when cpu serial no, lpar system id, and product values are available in prompts.

All the charts and tables that are shown in this report are in hourly interval bands, except when the product is in IPLA Reference-based pricing structure. When the product is in IPLA reference-based pricing structure, the lpar_system id prompt is not available to select. All LPARs are counted in calculations.

The Product MSU and Price report includes the following charts and tables.

Time Zone Summary table

This table displays the time zone summary information for a selected LPAR and contains the following elements:

LPAR_SYSTEM ID

The LPAR name and its system ID.

TIME ZONE (REPORT)

The time zone used when displaying the report. The format is UTC -5:00.

START DATE TIME (REPORT)

The start date and time in reporting the time zone. The start date and time is the latest time between the start date and time in the prompt and the earliest date and time of available data in the database.

END DATE TIME (REPORT)

The end date and time in reporting the time zone. The start date and time is the earliest time between the end date and time in the prompt and the latest date and time of available data in the database.

TIME ZONE (LPAR OS)

The local time of the selected LPAR. The format is GMT -5:00

Important: the LPAR time zone is the time zone of the LPAR OS and not the time of where the server is physically located.

START DATE TIME (LPAR OS)

The start date and time in the selected LPAR local time zone. The start date and time is the latest time between the start date and the time in the prompt and the earliest date and time of available data in the database.

END DATE TIME (LPAR OS)

The end date and time in the selected LPAR local time zone. The end date and time is the earliest time between the end date and the time in the prompt and the latest date and time of available data in the database.

Observed/Forecasted/Optimized Product MSU charts and tables

These charts and tables show a registered IBM product's billable MSUs for each scenario from the perspective of a single CPC and the LPARs it contains. You can click a chart to drill through to the LPAR MSU Utilization report.

The Optimized Product MSU charts and tables are not shown if the product is in IPLA Reference-based pricing structure.

In the chart and table for the Observed scenario, only observed data is shown.

For the Forecasted scenario, in addition to forecasted data, OBSERVED BILLABLE MSU, and Tier Level N Maximum/Minimum MSU Value for Observed are also shown in the chart as a dotted line, while OBSERVED BILLABLE MSU and IS HIGHEST are also shown in the table.

For the Optimized scenario, in addition to optimized data, FORECASTED BILLABLE MSU, and Tier Level N Maximum/Minimum MSU Value for Forecasted are also shown in the chart as a dotted line, while FORECASTED BILLABLE MSU and IS HIGHEST are also shown in the table.

For z/OS® products, there are no Tier Level N Maximum/Minimum MSU Value lines in the chart.

The Product MSU charts and tables include the following information:

CPU SERIAL NO

The zServer/CEC serial number.

LPAR SYSTEM

The LPAR name and system ID.

PRODUCT

The product display name. For details on display name rules, see Product display names.

BILLABLE MSU

TOTAL BILLABLE MSU: the product billable MSU on a CPC at each hour.

LPAR BILLABLE MSU: the product billable MSU on each LPAR at each hour. Only selected LPARs are shown in the chart and table.

The calculation formula varies according to each pricing structure. If there are no records for a product at some hour, then in the table, the cell is empty. If the values of a scenario are all NULL, then in the chart, the line is not shown; in the table, this column is not shown for this scenario.

PEAK MSU

The peak billable MSU point during the date and time range that is selected for the related scenario. This metric appears only in charts. If BILLABLE MSU values of a scenario are all NULL, the point is not shown.

MSU AS IF EXECUTION-BASED

The billable MSU value by assuming the product uses execution-based pricing structure. This metric appears only in the table for the Observed scenario.

DIFFERENCE

The difference between BILLABLE MSU and MSU AS IF EXECUTION-BASED, which shows the amount of MSU you save from using the current pricing structure. This metric appears only in tables.

IS HIGHEST

If the product BILLABLE MSU is the highest MSU during the selected date and time range, then the value is "YES", and the product BILLABLE MSU and this field appear in red. Otherwise, the value is "NO". This metric exists only in tables. If product BILLABLE MSU values of a scenario are all NULL, the value is shown as "NO" for this scenario.

Tier Level N Maximum MSU Value

The maximum MSU value of the tier to which the products’ highest MSU belongs. This metric appears only in charts. If BILLABLE MSU values of a scenario are all NULL, or the value of Maximum MSU is not defined, then the line is not shown in the chart, and the legend label for this line is displayed as "Tier Level N/A Maximum MSU Value ([SCENARIO_NAME])",

Tier Level N Minimum MSU Value

The minimum MSU value of the tier to which the products’ highest MSU belongs. This metric appears only in charts. If BILLABLE MSU values of a scenario are all NULL, then the line is not shown in the chart, and the legend label for this line is displayed as "Tier Level N/A Minimum MSU Value ([SCENARIO_NAME])".

Observed/Forecasted/Optimized Price Summary tables

These tables show an IBM product's billable MSUs and the monetary cost from a single CPC perspective for each of the observed, forecasted, and optimized scenarios.

The displayed IPLA MSUs and price are only for the chosen CPC and not for the entire enterprise. To view IPLA MSUs and price for the entire enterprise, see the Software Cost Analysis Summary workspace.

The Price Summary tables require that price values are correctly loaded into the HCM.CUSTOMER_PRICE table to display related monetary values. If price values are not loaded in the HCM.CUSTOMER_PRICE table, a No data available message appears in the Price Summary tables.

The Price Summary tables contain the following information:

ENTERPRISE

The enterprise name.

CPU SERIAL NO

The zServer/CEC serial number.

PROD NAME:

The product name.

PROD ID:

The product ID.

CATEGORY

Sub-capacity pricing of Monthly License Charge (MLC) or System z International Product License Agreement (IPLA).

LICENSE CHARGE

Monthly License Charge metrics name of MLC products or Value Unit Exhibit name of IPLA products.

HIGHEST MSU

The peak billable MSU point during the specified date and time range of the related scenario. This metric exists only in the table for MLC products.

TIER LEVEL

The tier to which the highest MSU belongs. For MLC products, the tier is the one within a License Charge; for IPLA products, the tier is the one within a Value Unit Exhibit.

TIER MIN MSU

The minimum MSU of the TIER LEVEL.

TIER MAX MSU

The maximum MSU of the TIER LEVEL. If the value is not defined, then this column is not shown.

ASSIGNED ENTITLED MSU

The assigned entitled MSU of the CPC. This metric exists only in the table for IPLA products.

ASSIGNED ENTITLED VU

The assigned entitled VU of the CPC. This metric exists only in the table for IPLA products.

TOTAL USAGE MSU

The peak billable MSU point during the specified date and time range of the related scenario. This metric exists only in the table for IPLA products.

TOTAL USAGE VU

The usage of VU that is converted from TOTAL USAGE MSU. This metric exists only in the table for IPLA products.

DELTA MSU

(TOTAL USAGE MSU) – (ASSIGNED ENTITLED MSU). This metric exists only in the table for IPLA products.

DELTA VU

(TOTAL USAGE VU) - (ASSIGNED ENTITLED VU). This metric exists only in the table for IPLA products.

MONETARY ENTITLED

The assigned entitled monetary value of the CPC. This metric exists only in the table for IPLA products.

MONETARY USAGE

The billable monetary value. If DELTA VU is negative, then (MONETARY USAGE) = (MONETARY ENTITLED); otherwise, (MONETARY USAGE) = (DELTA VU * PRICE PER VU). This metric exists only in the table for IPLA products.

MONETARY DELTA

(MONETARY USAGE) – (MONETARY ENTITLED). This metric exists only in the table for IPLA products.

MONETARY SS

S&S monetary value. MONETARY SS PER MONTH * the natural months of the specified date range. This metric exists only in the table for IPLA products.

TOTAL

Total monetary value. For IPLA products, if (MONETARY DELTA) is more than 0, then (MONETARY DELTA) + (MONETARY SS); otherwise,MONETARY SS. For MLC products, the total monetary value that is calculated by the method of its license charge.

Observed/Forecasted/Optimized Parent Billable MSU chart and Billable MSU Summary for Reference-Based Products table

These charts and tables show the billable MSUs for each parent product that has IBM Reference-Based charging from a single CPC perspective for each of the observed, forecasted, and optimized scenarios.

These charts and tables exist only for IPLA Reference-Based products. If there is no record for any parent of a child product, then the chart or table displays a No data available message.

You can click a parent product name in the Billable MSU Summary for Reference-Based Products table, or click a piece of the pie of the Parent Billable MSU chart, to drill through to the SCA: Product MSU and Price.

The charts and tables contain the following information:

PARENT PEAK MSU

The peak MSU of each parent product during the specified date and time range.

CHILD BILLABLE MSU

The total MSU of all the parents' peak MSU.

PEAK ZOS MSU

The peak MSU of z/OS on the CPC during the specified date and time range.

BILLABLE MSU

The final billable MSU that is the minimum value between CHILD BILLABLE MSU and PEAK ZOS MSU.

Observed/Forecasted/Optimized z/OS MSU chart and zNALC/Traditional Workloads tables

These charts and tables show MSU summary and details for zNALC and traditional workloads for a z/OS product from a single CPC.

There is one chart that is named z/OS MSU chart, and three tables: MSU Summary for zNALC and Traditional Workload table, Price Summary for zNALC and Traditional Workload table, and Billable MSU Details for zNALC and Traditional Workloads table.

These charts and tables exist only for z/OS products. Particularly, the MSU Summary for zNALC and Traditional Workload table and Price Summary for zNALC and Traditional Workload table exist only when there are both zNALC and Traditional workloads are running on the CPC during the specified date and time range.

In the z/OS MSU chart and Billable MSU Details for zNALC and Traditional Workloads table, for the Observed scenario, only observed data is shown. For the Forecasted scenario, in addition to forecasted data, OBSERVED BILLABLE MSU is also shown in the z/OS MSU chart as a dotted line, while OBSERVED BILLABLE MSU and IS HIGHEST appear in the Billable MSU Details for zNALC and Traditional Workloads table. For the Optimized scenario, in addition to optimized data, FORECASTED BILLABLE MSU is also shown in the z/OS MSU chart as a dotted line, while FORECASTED BILLABLE MSU and IS HIGHEST appear in the Billable MSU Details for zNALC and Traditional Workloads table.

The Price Summary for zNALC and Traditional Workload tables requires that price values are correctly loaded into the HCM.CUSTOMER_PRICE table to display related monetary values. If price values are not loaded in the HCM.CUSTOMER_PRICE table, a No data available message appears in the tables.

The MSU Summary for zNALC and Traditional Workload table contains the following information:

CPU SERIAL NO

The zServer/CEC serial number.

PROD ID

The product ID.

PROD NAME

The product name.

FULL CAPACITY MSU

Rated capacity of the CPC.

z/OS HIGHEST MSU

The highest MSU of the z/OS product regardless of zNALC workloads or traditional workloads.

TRAD HIGHEST MSU

The highest MSU of the z/OS product for traditional workloads.

zNALC HIGHEST MSU

The highest MSU of the z/OS product for zNALC workloads.

CHARGED TRAD MSU

The MSU used in monetary value calculations for traditional workloads.

CHARGED zNALC MSU

The MSU used in monetary value calculations for zNALC workloads.

The Price Summary for zNALC and Traditional Workload table contains the following information:

LICENSE CHARGE

Monthly License Charge metrics name.

CHARGED MSU

The MSU used in monetary value calculations.

TIER LEVEL

The tier within a License Charge to which the CHARGED MSU belongs.

TIER MIN MSU

The minimum MSU of the TIER LEVEL.

TIER MAX MSU

The maximum MSU of the TIER LEVEL. If the value is not defined, then this column is not shown.

MLC PRICE

Total monetary value.

The z/OS MSU charts and Billable MSU Details for zNALC and Traditional Workloads tables contain the following information:

z/OS PROD NAME

The z/OS product name. The z/OS product name presents the total z/OS MSU, while the z/OS product name with (zNALC) or (Traditional) presents the MSU for each type of workload.

BILLABLE MSU

The sum of billable MSU for each LPAR the workload is running at each hour. If the values of a scenario are all NULL, then in the chart, the line is not shown; in the table, the column is not shown for this scenario.

PEAK MSU

The peak billable MSU point during the specified date and time range of the related scenario. This metric appears only in charts. If BILLABLE MSU values of a scenario are all NULL, the point is not shown.

IS HIGHEST

If the BILLABLE MSU is the highest MSU during the specified date and time range, then the value is "YES" and the font color of product BILLABLE MSU and this field is red. Otherwise, the value is "NO". If BILLABLE MSU values of a scenario are all NULL, then the value is shown as "NO" for the scenario. This metric appears only in tables.

Optimized Product Suggested Actions table

This table shows the suggested actions that are related to the selected product from the optimization stream's results that are related to the selected product.

The table contains the following information:

PRODUCT CATEGORY

Subcapacity pricing of Monthly License Charge (MLC) or System z International Product License Agreement (IPLA).

PRODUCT NAME

The product name of the product suggested to move.

PRODUCT ID

The product ID of the product suggested to move.

PRODUCT PRICING STRUCTURE

The pricing structure of the product suggested to move.

FROM CPU SERIAL NO

The CPU Serial No from which it is suggested to move the product’s MSU.

FROM LPAR

The LPAR name from which it is suggested to move the product’s MSU.

FROM SYSTEM

The system ID from which it is suggested to move the product’s MSU.

TO CPU SERIAL NO

The CPU Serial No to which it is suggested to move the product’s MSU.

TO LPAR

The LPAR to which it is suggested to move the product’s MSU.

TO SYSTEM

The system ID to which it is suggested to move the product’s MSU.

MOVING MSUs

If all MSUs are suggested to be moved, then "N/A"; otherwise, it is the number of MSU to be moved.

MOVE ALL

If all MSUs are suggested to be moved, then "YES"; otherwise, "NO".

Parent topic: Report and workspace reference

SCA: Registered Products Matrix This report provides a listing of LPARs and CPCs where the SCA registered products are running. This matrix view of where the products are running allows a quick assessment of whether the products are running on the correct LPARs. The matrix also shows which products are co-located. This helps you plan your software costs and to determine where you might need to license a new product. The report shows which products are forecasted in a forecast stream. It also shows a new matrix after a product has been moved because of an optimization stream suggestion. The report shows observed, forecasted, and optimized scenarios. reportsRegistered Products MatrixSCA reportsRegistered Products MatrixSCA: Registered Products Matrix exampleSCA: Registered Products Matrix exampleSource tables[Presentation View].[Prompts/Product Billable MSU Prompt View] [Presentation View].[Raw Product MSU Utilization Metrics] [Presentation View].[Customer Pricing Information] [Presentation View].[Sub-Capacity Program Pricing Structures] [Presentation View].[Product MSU Forecast Metrics] [Presentation View].[Product MSU Optimization Metrics]Drill through to the following reportsSCA: Product MSU and PriceThis report shows a registered IBM product's billable MSUs and monetary cost from a single CPC perspective for each scenario: Observed, Forecasted, and Optimized.SCA: LPAR MSU UtilizationThis report helps you understand and forecast your costs based on your MSU usage. You can compare your historical MSU usage with the forecasted usage to see upcoming trends and take proactive action to adjust your workload and capacity limits to help control your bill.SCA: NO89 Products MatrixThis report provides a single place to review all of the NO89 products that use sub-capacity pricing. It also shows where these products are designated to be run (both the CPCs and the LPARs). It shows all of the locations where a product is running along with all of the other NO89 products to provide a planning resource for LPAR and CPC consolidations. From this report, you can also drill to the NO89 Product MSU and Price report for any product.Standard promptsdate rangetime rangeshow first SCRT cycletime zoneReport-specific promptsscenariocpu serial noThis report shows a table for each scenario selected. The tables contain the following information:CPC SERIAL NOThe zServer/CEC serial number.LPAR NAMEThe LPAR name.PRODUCT The product display name, which uses the syntax [PRODUCT_ID]_[PRODUCT_NAME]_[PRODUCT_PRICING_STRUCTURE]. See Product display namesTo help you select products easily in report prompts, Capacity Management Analytics shows products using the product display name. for details on naming rules. PRODUCT OWNERThe product's owner, for example, IBM CORP.Products on LPAR Total CountThe total number of 89 products that are running on the LPAR.LPARs for Product Total CountThe total number of LPARs that the product is running.CPCs for Product Total CountThe total number of CPCs that the product is running.In this table, if the product is running on this LPAR, then the cross point of LPAR NAME and PRODUCT is marked by a check mark Check mark icon.Report and workspace referenceIBM Capacity Management Analytics provides several predefined reports and workspaces that let you monitor and analyze the capacity of your IT services and the IT infrastructure.

SCA: Registered Products Matrix

This report provides a listing of LPARs and CPCs where the SCA registered products are running.

This matrix view of where the products are running allows a quick assessment of whether the products are running on the correct LPARs. The matrix also shows which products are co-located. This helps you plan your software costs and to determine where you might need to license a new product.

The report shows which products are forecasted in a forecast stream. It also shows a new matrix after a product has been moved because of an optimization stream suggestion. The report shows observed, forecasted, and optimized scenarios.

Figure 50. SCA: Registered Products Matrix example

Source tables

[Presentation View].[Prompts/Product Billable MSU Prompt View]

[Presentation View].[Raw Product MSU Utilization Metrics]

[Presentation View].[Customer Pricing Information]

[Presentation View].[Sub-Capacity Program Pricing Structures]

[Presentation View].[Product MSU Forecast Metrics]

[Presentation View].[Product MSU Optimization Metrics]

Drill through to the following reports

SCA: Product MSU and Price

SCA: LPAR MSU Utilization

SCA: NO89 Products Matrix

Standard prompts

date range

time range

show first SCRT cycle

time zone

Report-specific prompts

scenario

cpu serial no

This report shows a table for each scenario selected. The tables contain the following information:

CPC SERIAL NO

The zServer/CEC serial number.

LPAR NAME

The LPAR name.

PRODUCT

The product display name, which uses the syntax [PRODUCT_ID]_[PRODUCT_NAME]_[PRODUCT_PRICING_STRUCTURE].

See Product display names for details on naming rules.

PRODUCT OWNER

The product's owner, for example, IBM CORP.

Products on LPAR Total Count

The total number of 89 products that are running on the LPAR.

LPARs for Product Total Count

The total number of LPARs that the product is running.

CPCs for Product Total Count

The total number of CPCs that the product is running.

In this table, if the product is running on this LPAR, then the cross point of LPAR NAME and PRODUCT is marked by a check mark Check mark icon.

Parent topic: Report and workspace reference

WLM: Delays by Importance LevelThis report shows you the effect of delays for each WLM importance level that is running on the system. Delaying high importance workloads can result in missed Service Level Agreements (SLAs) which has a higher cost than delaying less important workloads. It is important to monitor these delays so that you can take timely action to control your costs and maintain your SLAs.reportsDelays by Importance LevelWLM reportsDelays by Importance LevelThis report shows the percentage of time when delays (CPU, IO, and paging) are negatively affecting work. Delays at the service class period level are aggregated and displayed for each WLM importance level. You can use this report to ensure that your most important work is not negatively affected by delays.WLM: Delays by Importance Level exampleWLM: Delays by Importance Level exampleSource tables[Presentation View].[WLM Service Class Period Usage/Delay Metrics]Drill through to the following reportsWLM: Delays by Service Class PeriodThis report shows where delays are having a negative impact on performance so that you can take corrective action. Monitoring delays is important because delays can cause you to miss SLAs and incur a penalty.Standard prompts:date rangetime rangeinterval bandshowReport-specific promptssystemimportance levelThe WLM: Delays by Importance Level report contains the following information:systemThe z/OS system id.importance levelThe WLM importance level that is assigned to a service class period.TOTAL DELAYTotal percentage of samples where the reported WLM service class period has been found delayed for any used type.CPU DELAYThe percentage of samples when a CPU delay existed (a TCB or SRB is waiting to be dispatched).IO DELAYThe percentage of samples when an IO delay existed (a TCB or SRB has initiated an I/O request that is delayed obtaining a path to the device). This includes IOSQ and Q+PEND components. QUEUE DELAYThe percentage of samples when a queue delay existed (work is waiting for a server address space or batch initiator).MPL DELAYThe percentage of samples when an MPL delay existed (ready to be dispatched but a swap-in has not started).CPU CAP DELAYThe percentage of samples when a CPU capping delay existed (a TCB or SRB is marked non-dispatchable because a resource group maximum is being enforced or because of discretionary goal management). That is, if certain types of work are overachieving their goals, that work might be capped so that the resources can be diverted to run discretionary work. STORAGE DELAYThe percentage of samples when a storage-related delay existed. Storage-related delays can be attributed to auxiliary paging from private, auxiliary paging from common, auxiliary paging from cross memory, auxiliary paging from VIO, auxiliary paging from standard hiperspaces, or auxiliary paging from ESO hiperspace (a page that is being read was not in the ESO hiperspace and must be read from DASD by the program that is managing the hiperspace).SWAP DELAYThe percentage of samples when a swap delay existed (a Swap-In has started but not completed).CONTENTION DELAYThe percentage of samples when a contention-related delay existed (work is waiting for resources).CRYPTO DELAYThe percentage of samples when a Crypto-related delay existed (a TCB or SRB was found to be waiting for a Crypto Asynchronous Message Processor, an Adjunct Processor, or a processor feature queue). SERVER DELAYThe percentage of samples when a server-related delay existed. Server-related delays can be attributed to a private area paging delay for a server address space, a VIO paging delay for a server address space, a hiperspace paging delay for a server address space, or a Swap-in delay for a server address space.Report and workspace referenceIBM Capacity Management Analytics provides several predefined reports and workspaces that let you monitor and analyze the capacity of your IT services and the IT infrastructure.

WLM: Delays by Importance Level

This report shows you the effect of delays for each WLM importance level that is running on the system. Delaying high importance workloads can result in missed Service Level Agreements (SLAs) which has a higher cost than delaying less important workloads. It is important to monitor these delays so that you can take timely action to control your costs and maintain your SLAs.

This report shows the percentage of time when delays (CPU, IO, and paging) are negatively affecting work. Delays at the service class period level are aggregated and displayed for each WLM importance level.

You can use this report to ensure that your most important work is not negatively affected by delays.

Figure 51. WLM: Delays by Importance Level example

Source tables

[Presentation View].[WLM Service Class Period Usage/Delay Metrics]

Drill through to the following reports

WLM: Delays by Service Class Period

Standard prompts:

date range

time range

interval band

show

Report-specific prompts

system

importance level

The WLM: Delays by Importance Level report contains the following information:

system

The z/OS system id.

importance level

The WLM importance level that is assigned to a service class period.

TOTAL DELAY

Total percentage of samples where the reported WLM service class period has been found delayed for any used type.

CPU DELAY

The percentage of samples when a CPU delay existed (a TCB or SRB is waiting to be dispatched).

IO DELAY

The percentage of samples when an IO delay existed (a TCB or SRB has initiated an I/O request that is delayed obtaining a path to the device). This includes IOSQ and Q+PEND components.

QUEUE DELAY

The percentage of samples when a queue delay existed (work is waiting for a server address space or batch initiator).

MPL DELAY

The percentage of samples when an MPL delay existed (ready to be dispatched but a swap-in has not started).

CPU CAP DELAY

The percentage of samples when a CPU capping delay existed (a TCB or SRB is marked non-dispatchable because a resource group maximum is being enforced or because of discretionary goal management). That is, if certain types of work are overachieving their goals, that work might be capped so that the resources can be diverted to run discretionary work.

STORAGE DELAY

The percentage of samples when a storage-related delay existed. Storage-related delays can be attributed to auxiliary paging from private, auxiliary paging from common, auxiliary paging from cross memory, auxiliary paging from VIO, auxiliary paging from standard hiperspaces, or auxiliary paging from ESO hiperspace (a page that is being read was not in the ESO hiperspace and must be read from DASD by the program that is managing the hiperspace).

SWAP DELAY

The percentage of samples when a swap delay existed (a Swap-In has started but not completed).

CONTENTION DELAY

The percentage of samples when a contention-related delay existed (work is waiting for resources).

CRYPTO DELAY

The percentage of samples when a Crypto-related delay existed (a TCB or SRB was found to be waiting for a Crypto Asynchronous Message Processor, an Adjunct Processor, or a processor feature queue).

SERVER DELAY

The percentage of samples when a server-related delay existed. Server-related delays can be attributed to a private area paging delay for a server address space, a VIO paging delay for a server address space, a hiperspace paging delay for a server address space, or a Swap-in delay for a server address space.

Parent topic: Report and workspace reference

WLM: Delays by Service Class PeriodThis report shows where delays are having a negative impact on performance so that you can take corrective action. Monitoring delays is important because delays can cause you to miss SLAs and incur a penalty.reportsDelays by Service Class PeriodWLM reportsDelays by Service Class PeriodThis report shows the percentage of time that delays (CPU, IO, and paging) are negatively impacting work. Delays are displayed at the service class period level. Use this report to ensure that your most important workloads (service classes) are not negatively impacted by delays.WLM: Delays by Service Class Period exampleWLM: Delays by Service Class Period exampleSource tables[Presentation View].[WLM Service Class Period Usage/Delay Metrics]Drill through to the following reportsWLM: Delays by Importance LevelThis report shows you the effect of delays for each WLM importance level that is running on the system. Delaying high importance workloads can result in missed Service Level Agreements (SLAs) which has a higher cost than delaying less important workloads. It is important to monitor these delays so that you can take timely action to control your costs and maintain your SLAs.Standard prompts:date rangetime rangeinterval bandshowReport-specific promptssystemimportance levelserviceclass periodThe WLM: Delays by Service Class Period report contains the following information:systemThe z/OS system id.importance levelThe WLM importance level that is assigned to a service class period.serviceclass periodThe WLM service class name and period. The concatenation of columns SERVICE_CLASS and SERV_CLASS_PERIOD.importance levelThe WLM importance level that is assigned to a service class period.CPU DELAYThe percentage of samples when a CPU delay existed (a TCB or SRB is waiting to be dispatched).IO DELAYThe percentage of samples when an IO delay existed (a TCB or SRB has initiated an I/O request that is delayed obtaining a path to the device). This includes IOSQ and Q+PEND components. QUEUE DELAYThe percentage of samples when a queue delay existed (work is waiting for a server address space or batch initiator).MPL DELAYThe percentage of samples when an MPL delay existed (ready to be dispatched but a swap-in has not started).CPU CAP DELAYThe percentage of samples when a CPU capping delay existed (a TCB or SRB is marked non-dispatchable because a resource group maximum is being enforced or because of discretionary goal management). That is, if certain types of work are overachieving their goals, that work might be capped so that the resources can be diverted to run discretionary work. STORAGE DELAYThe percentage of samples when a storage-related delay existed. Storage-related delays can be attributed to auxiliary paging from private, auxiliary paging from common, auxiliary paging from cross memory, auxiliary paging from VIO, auxiliary paging from standard hiperspaces, or auxiliary paging from ESO hiperspace (a page that is being read was not in the ESO hiperspace and must be read from DASD by the program that is managing the hiperspace).SWAP DELAYThe percentage of samples when a swap delay existed (a Swap-In has started but not completed).CONTENTION DELAYThe percentage of samples when a contention-related delay existed (work is waiting for resources).CRYPTO DELAYThe percentage of samples when a Crypto-related delay existed (a TCB or SRB was found to be waiting for a Crypto Asynchronous Message Processor, an Adjunct Processor, or a processor feature queue). SERVER DELAYThe percentage of samples when a server-related delay existed. Server-related delays can be attributed to a private area paging delay for a server address space, a VIO paging delay for a server address space, a hiperspace paging delay for a server address space, or a Swap-in delay for a server address space.Report and workspace referenceIBM Capacity Management Analytics provides several predefined reports and workspaces that let you monitor and analyze the capacity of your IT services and the IT infrastructure.

WLM: Delays by Service Class Period

This report shows where delays are having a negative impact on performance so that you can take corrective action. Monitoring delays is important because delays can cause you to miss SLAs and incur a penalty.

This report shows the percentage of time that delays (CPU, IO, and paging) are negatively impacting work. Delays are displayed at the service class period level. Use this report to ensure that your most important workloads (service classes) are not negatively impacted by delays.

Figure 52. WLM: Delays by Service Class Period example

Source tables

[Presentation View].[WLM Service Class Period Usage/Delay Metrics]

Drill through to the following reports

WLM: Delays by Importance Level

Standard prompts:

date range

time range

interval band

show

Report-specific prompts

system

importance level

serviceclass period

The WLM: Delays by Service Class Period report contains the following information:

system

The z/OS system id.

importance level

The WLM importance level that is assigned to a service class period.

serviceclass period

The WLM service class name and period. The concatenation of columns SERVICE_CLASS and SERV_CLASS_PERIOD.

importance level

The WLM importance level that is assigned to a service class period.

CPU DELAY

The percentage of samples when a CPU delay existed (a TCB or SRB is waiting to be dispatched).

IO DELAY

The percentage of samples when an IO delay existed (a TCB or SRB has initiated an I/O request that is delayed obtaining a path to the device). This includes IOSQ and Q+PEND components.

QUEUE DELAY

The percentage of samples when a queue delay existed (work is waiting for a server address space or batch initiator).

MPL DELAY

The percentage of samples when an MPL delay existed (ready to be dispatched but a swap-in has not started).

CPU CAP DELAY

The percentage of samples when a CPU capping delay existed (a TCB or SRB is marked non-dispatchable because a resource group maximum is being enforced or because of discretionary goal management). That is, if certain types of work are overachieving their goals, that work might be capped so that the resources can be diverted to run discretionary work.

STORAGE DELAY

The percentage of samples when a storage-related delay existed. Storage-related delays can be attributed to auxiliary paging from private, auxiliary paging from common, auxiliary paging from cross memory, auxiliary paging from VIO, auxiliary paging from standard hiperspaces, or auxiliary paging from ESO hiperspace (a page that is being read was not in the ESO hiperspace and must be read from DASD by the program that is managing the hiperspace).

SWAP DELAY

The percentage of samples when a swap delay existed (a Swap-In has started but not completed).

CONTENTION DELAY

The percentage of samples when a contention-related delay existed (work is waiting for resources).

CRYPTO DELAY

The percentage of samples when a Crypto-related delay existed (a TCB or SRB was found to be waiting for a Crypto Asynchronous Message Processor, an Adjunct Processor, or a processor feature queue).

SERVER DELAY

The percentage of samples when a server-related delay existed. Server-related delays can be attributed to a private area paging delay for a server address space, a VIO paging delay for a server address space, a hiperspace paging delay for a server address space, or a Swap-in delay for a server address space.

Parent topic: Report and workspace reference

WLM: Performance IndexesThis report helps you align goal achievement metrics with overall business performance measurements, such as the cost associated with system performance (for example, adequate response times). reportsPerformance IndexesWLM reportsPerformance IndexesThis report analyzes how well Workload Manager (WLM) is doing with achieving its goals. The report shows the distribution of WLM performance indexes across eight performance index ranges. It provides a view of how often WLM goals are met (performance index equal to or less than 1) or missed (performance index greater than 1), and to what degree. WLM: Performance Indexes exampleWLM: Performance Indexes exampleSource tables[Presentation View].[WLM Service Class Period Performance Metrics][Presentation View].[WLM Service Class Period Usage/Delay Metrics]Drill through to the following reportsThere is no drill through available on this report.Standard prompts:date rangetime rangeshowReport-specific promptssystemimportance levelserviceclass periodThe WLM: Performance Indexes report contains the following information:systemThe z/OS system id.importance levelThe WLM importance level that is assigned to a service class period.serviceclass periodThe WLM service class name and period. The concatenation of columns SERVICE_CLASS and SERV_CLASS_PERIOD.SYSTEM_IMPLVL_SERVICECLASS_PERIODThe concatenation of system, importance_level and serviceclass_period.PI Bucket (<0.5)The percentage of intervals analyzed where the PI was less than 0.5.PI Bucket (0.5 – 0.9)The percentage of intervals analyzed where the PI was greater than or equal to 0.5 and less than 0.9.PI Bucket (0.9 – 1.1)The percentage of intervals analyzed where the PI was greater than or equal to 0.9 and less than 1.1.PI Bucket (1.1 – 3.0)The percentage of intervals analyzed where the PI was greater than or equal to 1.1 and less than 3.0.PI Bucket (3.0 – 5.0)The percentage of intervals analyzed where the PI was greater than or equal to 3.0 and less than 5.0.PI Bucket (5.0 – 10.0)The percentage of intervals analyzed where the PI was greater than or equal to 5.0 and less than 10.0.PI Bucket (>= 10.0)The percentage of intervals analyzed where the PI was greater than or equal to 10.0.Report and workspace referenceIBM Capacity Management Analytics provides several predefined reports and workspaces that let you monitor and analyze the capacity of your IT services and the IT infrastructure.

WLM: Performance Indexes

This report helps you align goal achievement metrics with overall business performance measurements, such as the cost associated with system performance (for example, adequate response times).

This report analyzes how well Workload Manager (WLM) is doing with achieving its goals. The report shows the distribution of WLM performance indexes across eight performance index ranges. It provides a view of how often WLM goals are met (performance index equal to or less than 1) or missed (performance index greater than 1), and to what degree.

Figure 53. WLM: Performance Indexes example

Source tables

[Presentation View].[WLM Service Class Period Performance Metrics]

[Presentation View].[WLM Service Class Period Usage/Delay Metrics]

Drill through to the following reports

There is no drill through available on this report.

Standard prompts:

date range

time range

show

Report-specific prompts

system

importance level

serviceclass period

The WLM: Performance Indexes report contains the following information:

system

The z/OS system id.

importance level

The WLM importance level that is assigned to a service class period.

serviceclass period

The WLM service class name and period. The concatenation of columns SERVICE_CLASS and SERV_CLASS_PERIOD.

SYSTEM_IMPLVL_SERVICECLASS_PERIOD

The concatenation of system, importance_level and serviceclass_period.

PI Bucket (<0.5)

The percentage of intervals analyzed where the PI was less than 0.5.

PI Bucket (0.5 – 0.9)

The percentage of intervals analyzed where the PI was greater than or equal to 0.5 and less than 0.9.

PI Bucket (0.9 – 1.1)

The percentage of intervals analyzed where the PI was greater than or equal to 0.9 and less than 1.1.

PI Bucket (1.1 – 3.0)

The percentage of intervals analyzed where the PI was greater than or equal to 1.1 and less than 3.0.

PI Bucket (3.0 – 5.0)

The percentage of intervals analyzed where the PI was greater than or equal to 3.0 and less than 5.0.

PI Bucket (5.0 – 10.0)

The percentage of intervals analyzed where the PI was greater than or equal to 5.0 and less than 10.0.

PI Bucket (>= 10.0)

The percentage of intervals analyzed where the PI was greater than or equal to 10.0.

Parent topic: Report and workspace reference

zIIP/zAAP What-ifs - LPAR Level workspaceThis workspace puts analytical power at your fingertips. Scenario modeling lets you decide how to allocate processing costs to the zIIP/zAAP engines where it is most efficient and cost effective. Operational improvements that are based on the best scenario can drive significant cost reductions and savings.workspaceszIIP/zAAP What-ifszIIP/zAAP What-ifsThe workspace displays the observed zIIP and zAAP usage along with eligible workloads in MIPS and engines. You can use this workspace to see the impact of moving around workloads. To help you get started, two what-if scenarios are included that showIncreased usage from moving zIIP/zAAP-eligible MIPSEngines from CPs on zIIPs/zAAPsYou can also use this workspace to see the impact of adding zIIP/zAAPs to your environment or the impact of adding more zIIPs/zAAPs. All MIPS are normalized to a CP while engines are represented as actual engines. If zIIP/zAAP engines are not present, then the estimated zIIP/zAAP engines for the what-if scenarios that are based on the same speed as the installed CPs.zIIP/zAAP What-ifs - LPAR Level workspace samplezIIP/zAAP What-ifs - LPAR Level workspace sample reportSource tables[Presentation View].[WLM Service Class Period Usage/Delay Metrics] [Presentation View].[LPAR Level Metrics]Drill through to the following report:CPU: MIPS Used - Service Class Period Level This report provides a comprehensive view of all of the applications that are running across the entire systems landscape. This helps you quickly rebalance system allocation and to focus on the most critical applications.Workspace promptsdate rangetime rangeinterval bandcpu serial nolpar nameThe zIIP/zAAP What-ifs - LPAR Level workspace includes the following charts and tables. In some cases, a value can be too small to observe graphically on a chart. You can review all charted values in the zIIP/zAAP What-ifs table, which is included in the workspace. The engine numbers that are shown in a chart or table is estimated from MIPS used, and is not the actual required engine numbers. When you plan the configuration of your zIIP/zAAP, consider the following aspects. This list of considerations is not definitive, and you might have other considerations unique to your model or environment.With more engine numbers installed, LPAR management time is higher, so that more engines are required. The more processors that are installed, the fewer MIPS per processor are supported.Consider peak and average CPU utilization. Charts display average, but many systems need to support peak workload. (Peak-to-Average Ratio (PAR) and Saturation Design Point (SDP).) With greater CPU utilization, MIPS per transaction is higher. Generally, the CPU utilization that is required to maintain response time is different when engine numbers are different. With more engines, less utilization is required due to flexibility.Consider failover support requirements among all systems.Consider future workload increases or decreases.Observed MIPS chart This chart shows the average Observed MIPS utilization by CP and zIIP/zAAP. The top half of the chart shows the observed CP MIPS with the eligible zIIP/zAAP MIPS along with a ratio of zIIP/zAAP on CP. The bottom half shows just the observed zIIP/zAAP MIPS.As if all on zIIP chart This chart shows the what-if scenario for moving all the zIIP/zAAP eligible and all zAAP work onto zIIPs (zAAP on zIIP). The top half of the chart shows CP MIPS without the zIIP/zAAP eligible work. The bottom half of the chart shows all the zIIP/zAAP eligible work added to the zIIPs. Ratios of zIIP / zAAP eligible work to the total zIIP work is also shown.As if all on zIIP/zAAP chart This chart shows the what-if scenario that assumes all zIIP/zAAP eligible workloads that are run on zIIP/zAAP. The top and bottom half show the new total MIPS of the zIIP and zAAP eligible work added to the existing zIIP and zAAP MIPS separately. A ratio of the zIIP/zAAP eligible MIPS to total zIIP/zAAP work is also shown.Observed Engines chart This chart shows utilization based on the average number of observed engines for each processor type in respect to logical, dedicated, and weighted processor configuration for the LPAR. The top half of the chart shows engine counts for CPs including eligible zIIP/zAAP work. The bottom half shows the various engine counts for zIIP/zAAPs.As if all on zIIP Engines chart This chart illustrates the what-if scenario for moving all zIIP/zAAP eligible work onto zIIPs expressed in engine counts with respect to the LPAR configuration for logical, weighted, and dedicated processors. The top half of the chart shows various engine counts for CPs without zIIP/zAAP eligible work. The bottom half shows various engine counts of the zIIP/zAAP eligible work that was added to the existing zIIP counts. If zIIP/zAAP engines are not present, then the estimated zIIP/zAAP engines are based on the same speed of the installed CPs.As if all on zIIP/zAAP Engines chart This chart illustrates the what-if scenario for moving all zIIP/zAAP eligible work onto zIIPs/zAAPs expressed in engine counts with respect to the LPAR configuration for logical, weighted, and dedicated processors. The top and bottom half show various engine counts of the zIIP and zAAP eligible work added to the existing zIIP and zAAP counts separately. If zIIP/zAAP engines are not present, then the estimated zIIP/zAAP engines are based on the same speed of the installed CPs.zIIP/zAAP What-ifs table This table shows observed zIIP/zAAP usage along with eligible usage in MIPS and engines. In addition, two what-if scenarios are included showing the increased usage of zIIP/zAAP eligible MIPS from CPs on zIIPs/zAAPs. All MIPS are normalized to a CP while engines are represented as actual engines. If zIIP/zAAP engines are not present, then the estimated zIIP/zAAP engines for the what-if scenarios are based on the same speed of the installed CPs.Report and workspace referenceIBM Capacity Management Analytics provides several predefined reports and workspaces that let you monitor and analyze the capacity of your IT services and the IT infrastructure.

zIIP/zAAP What-ifs - LPAR Level workspace

This workspace puts analytical power at your fingertips. Scenario modeling lets you decide how to allocate processing costs to the zIIP/zAAP engines where it is most efficient and cost effective. Operational improvements that are based on the best scenario can drive significant cost reductions and savings.

The workspace displays the observed zIIP and zAAP usage along with eligible workloads in MIPS and engines. You can use this workspace to see the impact of moving around workloads. To help you get started, two what-if scenarios are included that show

Increased usage from moving zIIP/zAAP-eligible MIPS

Engines from CPs on zIIPs/zAAPs

You can also use this workspace to see the impact of adding zIIP/zAAPs to your environment or the impact of adding more zIIPs/zAAPs. All MIPS are normalized to a CP while engines are represented as actual engines. If zIIP/zAAP engines are not present, then the estimated zIIP/zAAP engines for the what-if scenarios that are based on the same speed as the installed CPs.

Figure 54. zIIP/zAAP What-ifs - LPAR Level workspace sample

Source tables

[Presentation View].[WLM Service Class Period Usage/Delay Metrics]

[Presentation View].[LPAR Level Metrics]

Drill through to the following report:

CPU: MIPS Used - Service Class Period Level

Workspace prompts

date range

time range

interval band

cpu serial no

lpar name

The zIIP/zAAP What-ifs - LPAR Level workspace includes the following charts and tables.

In some cases, a value can be too small to observe graphically on a chart. You can review all charted values in the zIIP/zAAP What-ifs table, which is included in the workspace.

The engine numbers that are shown in a chart or table is estimated from MIPS used, and is not the actual required engine numbers.

When you plan the configuration of your zIIP/zAAP, consider the following aspects. This list of considerations is not definitive, and you might have other considerations unique to your model or environment.

With more engine numbers installed, LPAR management time is higher, so that more engines are required. The more processors that are installed, the fewer MIPS per processor are supported.

Consider peak and average CPU utilization. Charts display average, but many systems need to support peak workload. (Peak-to-Average Ratio (PAR) and Saturation Design Point (SDP).)

With greater CPU utilization, MIPS per transaction is higher. Generally, the CPU utilization that is required to maintain response time is different when engine numbers are different. With more engines, less utilization is required due to flexibility.

Consider failover support requirements among all systems.

Consider future workload increases or decreases.

Observed MIPS chart

This chart shows the average Observed MIPS utilization by CP and zIIP/zAAP. The top half of the chart shows the observed CP MIPS with the eligible zIIP/zAAP MIPS along with a ratio of zIIP/zAAP on CP. The bottom half shows just the observed zIIP/zAAP MIPS.

As if all on zIIP chart

This chart shows the what-if scenario for moving all the zIIP/zAAP eligible and all zAAP work onto zIIPs (zAAP on zIIP). The top half of the chart shows CP MIPS without the zIIP/zAAP eligible work. The bottom half of the chart shows all the zIIP/zAAP eligible work added to the zIIPs. Ratios of zIIP / zAAP eligible work to the total zIIP work is also shown.

As if all on zIIP/zAAP chart

This chart shows the what-if scenario that assumes all zIIP/zAAP eligible workloads that are run on zIIP/zAAP. The top and bottom half show the new total MIPS of the zIIP and zAAP eligible work added to the existing zIIP and zAAP MIPS separately. A ratio of the zIIP/zAAP eligible MIPS to total zIIP/zAAP work is also shown.

Observed Engines chart

This chart shows utilization based on the average number of observed engines for each processor type in respect to logical, dedicated, and weighted processor configuration for the LPAR. The top half of the chart shows engine counts for CPs including eligible zIIP/zAAP work. The bottom half shows the various engine counts for zIIP/zAAPs.

As if all on zIIP Engines chart

This chart illustrates the what-if scenario for moving all zIIP/zAAP eligible work onto zIIPs expressed in engine counts with respect to the LPAR configuration for logical, weighted, and dedicated processors. The top half of the chart shows various engine counts for CPs without zIIP/zAAP eligible work. The bottom half shows various engine counts of the zIIP/zAAP eligible work that was added to the existing zIIP counts.

If zIIP/zAAP engines are not present, then the estimated zIIP/zAAP engines are based on the same speed of the installed CPs.

As if all on zIIP/zAAP Engines chart

This chart illustrates the what-if scenario for moving all zIIP/zAAP eligible work onto zIIPs/zAAPs expressed in engine counts with respect to the LPAR configuration for logical, weighted, and dedicated processors. The top and bottom half show various engine counts of the zIIP and zAAP eligible work added to the existing zIIP and zAAP counts separately.

If zIIP/zAAP engines are not present, then the estimated zIIP/zAAP engines are based on the same speed of the installed CPs.

zIIP/zAAP What-ifs table

This table shows observed zIIP/zAAP usage along with eligible usage in MIPS and engines. In addition, two what-if scenarios are included showing the increased usage of zIIP/zAAP eligible MIPS from CPs on zIIPs/zAAPs. All MIPS are normalized to a CP while engines are represented as actual engines.

If zIIP/zAAP engines are not present, then the estimated zIIP/zAAP engines for the what-if scenarios are based on the same speed of the installed CPs.

Parent topic: Report and workspace reference

Enterprise Dashboard workspaceThe Enterprise Dashboard workspace brings together information for all of your servers across the enterprise to provide an overview of your inventory and utilization.workspacesEnterprise DashboardEnterprise DashboardSummary tab exampleSummary tab exampleSource tables[Dimensional View].[Linux for System X CPU Hour Metrics] [Dimensional View].[Linux for System Z CPU Hour Metrics] [Dimensional View].[z/OS System Level Metrics] [Dimensional View].[WIN OPERATING CPU Hour Metrics] [Dimensional View].[AIX CPU Raw Hour Metrics] Standard promptsdate rangetime rangeinterval bandtime zoneThe charts and tables available on the Enterprise Dashboard are grouped on the following tabs:SummaryAIXLinux for System xLinux for System zWINDOWSz/OSSummary tab This enterprise-wide view shows the inventory count of servers that are reporting data metrics for each platform. It also shows the data for each individual server. Its peak CPU usage is tracked over time. This allows you to see what percent of the servers in each platform are running hot (running with a peak usage over 90% busy). It also lets you track SLAs for what percent of servers can be running hot for each platform. You can also use that same Peak CPU usage tracking and show the percent of servers in each platform that are very low CPU usage. If the peak usage is less than 10% busy, that is considered to be low usage. The percent of servers that are running very low CPU usage can be tracked to monitor progress of right sizing the servers over time. These thresholds are customizable. This report provides an overview of all the servers that have data that is being collected about its capacity. You can change the data range of the chart to see the changes that have occurred by platform. The two reports below the inventory pie chart are meant to provide a quick glance at each platform’s CPU utilization. If you have a high percentage of servers that have very low utilization, that is typically a target for cost reduction. If you have a high percentage of servers that are very high in CPU usage, that shows that you might need to spread out some work or increase the capacity of some servers. These high-level views allow tracking of how you are doing overall towards the balance of cost and SLA adherence. Over time, you might want to modify these reports to filter out specific servers that are supposed to be low usage or high usage. Servers Reporting by Operating System chart This chart shows the number of servers on which data was found during the reporting date/time range, by operating system.Percentage of Servers with High Usage chart This chart shows the percentage of the total number of servers that have a peak utilization greater than the high usage threshold. The default high usage threshold is 90% busy. There is a separate high usage report for each operating system group of servers. Peak utilization is calculated by using the 90 percentile CPU Busy value over the interval band.Percentage of Servers with Light Usage chart This chart shows the percentage of the total number of servers that have a peak utilization lower than the low usage threshold. The default low usage threshold is 10% busy. There is a separate low usage report for each operation system group of servers. Peak utilization is calculated by using the 90 percentile CPU Busy value over the interval band. From the Summary tab, you can see the percentage of servers at high usage and low usage. The platform tabs provide the list of the highest usage servers for the selected date range. It also shows their history of usage over time so you can see whether it is consistently at that level of usage or not. The lowest usage servers for the selected date are also shown. The server history of usage is shown over time so you can see whether it is consistently low usage or not. For both the high usage and low usage servers, this might be an action list of servers that require further review.AIX tab 10 Highest CPU Usage Peaks table This table shows the top 10 AIX servers with the highest 90 percentile CPU busy values.10 Lowest CPU Usage Peaks table This table shows the bottom 10 AIX servers with the lowest 90 percentile CPU busy values.Linux for System x tab 10 Highest CPU Usage Peaks table This table shows the top 10 Linux for System x servers with the highest 90 percentile CPU busy values.10 Lowest CPU Usage Peaks table This table shows the bottom 10 Linux for System x servers with the lowest 90 percentile CPU busy values.Linux for System z tab 10 Highest CPU Usage Peaks table This table shows the top 10 Linux for System z servers with the highest 90 percentile CPU busy values.10 Lowest CPU Usage Peaks table This table shows the bottom 10 Linux for System z servers with the lowest 90 percentile CPU busy values.Windows tab 10 Highest CPU Usage Peaks table This table shows the top 10 Windows servers with the highest 90 percentile CPU busy values.10 Lowest CPU Usage Peaks table This table shows the bottom 10 Windows servers with the lowest 90 percentile CPU busy values.z/OS tab Using high and low usage percentage is an effective method to determine which servers should be looked into. For consistency, the same report is included for the servers (LPARs) within the mainframe platform. Having a view of the high and low usage LPARs allows you to monitor changes in the behavior of your LPARs that can suggest further investigation is necessary. 10 Highest CPU Usage Peaks table This table shows the top 10 z/OS LPARs for System z with the highest 90 percentile CPU busy values.10 Lowest CPU Usage Peaks table This table shows the bottom 10 z/OS LPARs for System z with the lowest 90 percentile CPU busy values.Report and workspace referenceIBM Capacity Management Analytics provides several predefined reports and workspaces that let you monitor and analyze the capacity of your IT services and the IT infrastructure.

Enterprise Dashboard workspace

The Enterprise Dashboard workspace brings together information for all of your servers across the enterprise to provide an overview of your inventory and utilization.

Figure 55. Summary tab example

Source tables

[Dimensional View].[Linux for System X CPU Hour Metrics]

[Dimensional View].[Linux for System Z CPU Hour Metrics]

[Dimensional View].[z/OS System Level Metrics]

[Dimensional View].[WIN OPERATING CPU Hour Metrics]

[Dimensional View].[AIX CPU Raw Hour Metrics]

Standard prompts

date range

time range

interval band

time zone

The charts and tables available on the Enterprise Dashboard are grouped on the following tabs:

Summary

AIX

Linux for System x

Linux for System z

WINDOWS

z/OS

Summary tab

This enterprise-wide view shows the inventory count of servers that are reporting data metrics for each platform. It also shows the data for each individual server. Its peak CPU usage is tracked over time. This allows you to see what percent of the servers in each platform are running hot (running with a peak usage over 90% busy). It also lets you track SLAs for what percent of servers can be running hot for each platform. You can also use that same Peak CPU usage tracking and show the percent of servers in each platform that are very low CPU usage. If the peak usage is less than 10% busy, that is considered to be low usage. The percent of servers that are running very low CPU usage can be tracked to monitor progress of right sizing the servers over time. These thresholds are customizable.

This report provides an overview of all the servers that have data that is being collected about its capacity. You can change the data range of the chart to see the changes that have occurred by platform. The two reports below the inventory pie chart are meant to provide a quick glance at each platform’s CPU utilization. If you have a high percentage of servers that have very low utilization, that is typically a target for cost reduction. If you have a high percentage of servers that are very high in CPU usage, that shows that you might need to spread out some work or increase the capacity of some servers. These high-level views allow tracking of how you are doing overall towards the balance of cost and SLA adherence. Over time, you might want to modify these reports to filter out specific servers that are supposed to be low usage or high usage.

Servers Reporting by Operating System chart

This chart shows the number of servers on which data was found during the reporting date/time range, by operating system.

Percentage of Servers with High Usage chart

This chart shows the percentage of the total number of servers that have a peak utilization greater than the high usage threshold. The default high usage threshold is 90% busy. There is a separate high usage report for each operating system group of servers. Peak utilization is calculated by using the 90 percentile CPU Busy value over the interval band.

Percentage of Servers with Light Usage chart

This chart shows the percentage of the total number of servers that have a peak utilization lower than the low usage threshold. The default low usage threshold is 10% busy. There is a separate low usage report for each operation system group of servers. Peak utilization is calculated by using the 90 percentile CPU Busy value over the interval band.

From the Summary tab, you can see the percentage of servers at high usage and low usage. The platform tabs provide the list of the highest usage servers for the selected date range. It also shows their history of usage over time so you can see whether it is consistently at that level of usage or not. The lowest usage servers for the selected date are also shown. The server history of usage is shown over time so you can see whether it is consistently low usage or not. For both the high usage and low usage servers, this might be an action list of servers that require further review.

AIX tab

10 Highest CPU Usage Peaks table

This table shows the top 10 AIX servers with the highest 90 percentile CPU busy values.

10 Lowest CPU Usage Peaks table

This table shows the bottom 10 AIX servers with the lowest 90 percentile CPU busy values.

Linux for System x tab

10 Highest CPU Usage Peaks table

This table shows the top 10 Linux for System x servers with the highest 90 percentile CPU busy values.

10 Lowest CPU Usage Peaks table

This table shows the bottom 10 Linux for System x servers with the lowest 90 percentile CPU busy values.

Linux for System z tab

10 Highest CPU Usage Peaks table

This table shows the top 10 Linux for System z servers with the highest 90 percentile CPU busy values.

10 Lowest CPU Usage Peaks table

This table shows the bottom 10 Linux for System z servers with the lowest 90 percentile CPU busy values.

Windows tab

10 Highest CPU Usage Peaks table

This table shows the top 10 Windows servers with the highest 90 percentile CPU busy values.

10 Lowest CPU Usage Peaks table

This table shows the bottom 10 Windows servers with the lowest 90 percentile CPU busy values.

z/OS tab

Using high and low usage percentage is an effective method to determine which servers should be looked into. For consistency, the same report is included for the servers (LPARs) within the mainframe platform. Having a view of the high and low usage LPARs allows you to monitor changes in the behavior of your LPARs that can suggest further investigation is necessary.

10 Highest CPU Usage Peaks table

This table shows the top 10 z/OS LPARs for System z with the highest 90 percentile CPU busy values.

10 Lowest CPU Usage Peaks table

This table shows the bottom 10 z/OS LPARs for System z with the lowest 90 percentile CPU busy values.

Parent topic: Report and workspace reference

Software Cost Analysis Summary workspaceManaging System z capacity can be costly and complex. This workspace allows you to better manage your software costs on System z by providing graphs and tables that show both the details and the summary of the software costs.workspacesSoftware Cost AnalysisSoftware Cost AnalysisThis workspace shows MSU utilization and pricing information for all the registered IBM products for CPCs within an enterprise. The workspace displays information for the observed, forecasted, and optimized scenarios. Lists of the registered products in three scenarios and IBM NO89 products are also provided.The execution time of the Software Cost Analysis Summary workspace is directly proportional to the number of days, sub-capacity products, CPCs, and LPARs. Therefore, to minimize the waiting time for processing the workspace with a long date range over many products, CPC, and LPARs, you can schedule the source report of this workspace to run at regular intervals. The source report is named Software Cost Analysis Summary under the WorkspaceObjectsCharts & TablesSoftware Cost Analysis folder. When you do so, you can quickly view the most recently saved version of the contents without having to wait for the report to populate. Source tables[Presentation View].[Customer Pricing Information] [Presentation View].[LPAR MSU Forecast Metrics] [Presentation View].[LPAR MSU Metrics] [Presentation View].[LPAR MSU Optimization Metrics] [Presentation View].[Movement Suggestion by Optimization Metrics] [Presentation View].[NO89 Products] [Presentation View].[Product Billable MSU Forecast Metrics] [Presentation View].[Product Billable MSU Optimization Metrics] [Presentation View].[Product MSU Forecast Metrics] [Presentation View].[Product MSU Optimization Metrics] [Presentation View].[Product MSU Utilization Metrics Without Z/OS] [Presentation View].[Product MSU Utilization Metrics] [Presentation View].[Prompts/Product Billable MSU Prompt View] [Presentation View].[Raw Product MSU Utilization Metrics] [Presentation View].[Sub-Capacity Program Pricing Structures] [Presentation View].[Parents of Sub-Capacity Reference-based programs] [Presentation View].[Timezone Metrics] [Presentation View].[Z/OS Product MSU Utilization Metrics] Standard promptsdate rangetime rangetimezoneShow First SCRT CycleShow Monetary Valueenterprisecpu serial noThe values for the Enterprise and cpu serial no prompts are retrieved from the HCM.CUSTOMER_PRICE table. You must ensure that the required values are loaded in the table. Refer to the correct Configuration section for loading. Workspace objectsThe widgets in the Software Cost Analysis Summary workspace are in the WorkspaceObjectsCharts & TablesSoftware Cost Analysis folder.The tables available on the Software Cost Analysis Summary are grouped in the following tabs that are at the bottom of the workspace: Observed—shows the machine level summary and product summary information for the observed scenario.Forecasted—shows the summary information, for comparison purposes, of both the forecasted and observed scenarios.Optimized—shows the summary information, for comparison purposes, of both the optimized and forecasted scenarios.Products Matrix—shows a list of registered products in three scenarios (Observed, Forecasted and Optimized) and IBM NO89 products.Software Cost Analysis Summary workspace Observed tabSoftware Cost Analysis Summary workspace Observed tabObserved tab: Machine Level Summary table This table shows the summary information for each machine that you select in the prompts. If the machine model changes during the specified period for a machine, the table shows you the highest machine model observed in the specified period. Data Start and Data End is the date and time range within the specified period and with observed data for the specific CPC. If the value of Report Period % Data is below 95%, the value displays in red for quick identification. Observed tab: Product Summary table This table shows the billable MSU and/or monetary value for each registered IBM product that is running on the selected enterprise. When Show Monetary Value is not selected, the Product Summary table is split into two tables: one for all IPLA and MLC products except z/OS, and another for z/OS with zNALC consideration. When Show Monetary Value is checked, the Product Summary table is split into three tables: one for IPLA, one for MLC except z/OS, and one for z/OS with zNALC consideration. When Show Monetary Value is checked, if the price of some products on some CPCs is not set in the HCM.CUSTOMER_PRICE table, then the billable MSUs of them is counted in the Total Billable MSUs column for All Machines, and not counted in the Monetary Total column for All Machines. The Monetary Total column for All Machines can calculate only the total of all CPCs with the price. The Summary row is used to do the total aggregation for the Monetary Total column and the MSU columns. If a product is running on some CPCs, but not on all the user-specified CPCs, then the related billable MSUs is shown as empty and the related monetary value appears as N/A or empty. If all the values of a column are NULL, the column is not displayed in the Product Summary table.Software Cost Analysis Summary workspace Forecasted tabSoftware Cost Analysis Summary workspace Forecasted tabForecasted tab: Machine Level Summary table This table shows the Forecasted and Observed summary information for each machine that you select in the prompts. If the machine model changes during the specified period for a machine, the table shows you the highest machine model in forecast streams for the specified period. If there is no data for the Observed or Forecasted scenario for all machines, only the Below 95% and Highest MSU Hour Count columns are displayed in the table, with N/A or 0 values.Forecasted tab: Product Summary table This table shows the product summary information for both Forecasted and Observed scenarios. When Show Monetary Value is not selected, the Product Summary table is split into two tables: one for all IPLA and MLC products except z/OS, and another for z/OS with zNALC consideration. When Show Monetary Value is checked, the Product Summary table is split into three tables: one for IPLA, one for MLC except z/OS, and one for z/OS with zNALC consideration. When Show Monetary Value is checked, if the price of some products on some CPCs is not set in the HCM.CUSTOMER_PRICE table, then the billable MSUs of them is counted in the Total Billable MSUs column for All Machines, and not counted in the Monetary Total column for All Machines. The Monetary Total column for All Machines can calculate only the total of all CPCs with the price. If any product's price on some CPC is not set in the HCM.CUSTOMER_PRICE table, although there might be MSU, the CPC will not be shown in each CPC’s section. While in Total Billable MSUs column for All Machines, the MSU from CPC without price will be added up. The Optimized Monetary Total column for All Machines can only calculate the total of all CPCs with price. The Summary row is used to do the total aggregation for the Monetary Total column and the MSU columns. If a product is running on some CPCs, but not on all the user-specified CPCs, then the related billable MSUs is shown as empty and the related monetary value appears as N/A or empty. If all the values of a column are NULL, the column is not displayed in the Product Summary table.Software Cost Analysis Summary workspace Optimized tabSoftware Cost Analysis Summary workspace Optimized tabOptimized tab: Machine Level Summary table This table shows the Optimized and Forecasted summary information for each machine that you select in the prompts. If the machine model changes during the specified period for a machine, the table shows you the highest machine model in optimization streams for the specified period. If there is no data for the Optimized or Forecasted scenario for all machines, only the Below 95% and Highest MSU Hour Count columns are displayed in the table, with N/A or 0 values.Optimized tab: Product Summary table This table shows the product summary information for both Optimized and Forecasted scenarios. When Show Monetary Value is not selected, the Product Summary table is split into two tables: one for all IPLA and MLC products except z/OS, and another for z/OS with zNALC consideration. When Show Monetary Value is checked, the Product Summary table is split into three tables: one for IPLA, one for MLC except z/OS, and one for z/OS with zNALC consideration. The Summary row is used to do the total aggregation for the Monetary Total column and the MSU columns. When Show Monetary Value is checked, if the price of some products on some CPCs is not set in the HCM.CUSTOMER_PRICE table, then the billable MSUs of them is counted in the Total Billable MSUs column for All Machines, and not counted in the Monetary Total column for All Machines. The Monetary Total column for All Machines can calculate only the total of all CPCs with the price. If any product's price on some CPC is not set in the HCM.CUSTOMER_PRICE table, although there might be MSU, the CPC will not be shown in each CPC’s section. While in Total Billable MSUs column for All Machines, the MSU from CPC without price will be added up. The Optimized Monetary Total column for All Machines can only calculate the total of all CPCs with price. If a product is running on some CPCs, but not on all the user-specified CPCs, then the related billable MSUs is shown as empty and the related monetary value appears as N/A or empty. If all the values of a column are NULL, the column is not displayed in the Product Summary table.Optimized tab: Suggested Actions table This table shows the suggested actions from the optimization stream results. When Show Monetary Value is not selected, this table does not appear on the Optimized tab. Provide the overall suggested actions on all CPCs, so that the enterprise and cpu serial no prompts do not affect the result. To provide the overall suggested actions for all CPCs, the result in the Suggested Actions table is not filtered by the enterprise and cpu serial number value prompts in the workspace global area.Products Matrix tab This tab shows the following tables: Registered Products Matrix for Observed ScenarioRegistered Products Matrix for Forecasted ScenarioRegistered Products Matrix for Optimized ScenarioNO89 Products Matrix table If the product is running on this LPAR, then the cross point of LPAR NAME and PRODUCT is marked by a check mark Check mark icon. For more information, see SCA: Registered Products MatrixThis report provides a listing of LPARs and CPCs where the SCA registered products are running. and SCA: NO89 Products MatrixThis report provides a single place to review all of the NO89 products that use sub-capacity pricing. It also shows where these products are designated to be run (both the CPCs and the LPARs). It shows all of the locations where a product is running along with all of the other NO89 products to provide a planning resource for LPAR and CPC consolidations. From this report, you can also drill to the NO89 Product MSU and Price report for any product..Report and workspace referenceIBM Capacity Management Analytics provides several predefined reports and workspaces that let you monitor and analyze the capacity of your IT services and the IT infrastructure.

Software Cost Analysis Summary workspace

Managing System z® capacity can be costly and complex. This workspace allows you to better manage your software costs on System z by providing graphs and tables that show both the details and the summary of the software costs.

This workspace shows MSU utilization and pricing information for all the registered IBM® products for CPCs within an enterprise. The workspace displays information for the observed, forecasted, and optimized scenarios. Lists of the registered products in three scenarios and IBM NO89 products are also provided.

The execution time of the Software Cost Analysis Summary workspace is directly proportional to the number of days, sub-capacity products, CPCs, and LPARs. Therefore, to minimize the waiting time for processing the workspace with a long date range over many products, CPC, and LPARs, you can schedule the source report of this workspace to run at regular intervals. The source report is named Software Cost Analysis Summary under the Workspace > Objects > Charts & Tables > Software Cost Analysis folder. When you do so, you can quickly view the most recently saved version of the contents without having to wait for the report to populate.

Source tables

[Presentation View].[Customer Pricing Information]

[Presentation View].[LPAR MSU Forecast Metrics]

[Presentation View].[LPAR MSU Metrics]

[Presentation View].[LPAR MSU Optimization Metrics]

[Presentation View].[Movement Suggestion by Optimization Metrics]

[Presentation View].[NO89 Products]

[Presentation View].[Product Billable MSU Forecast Metrics]

[Presentation View].[Product Billable MSU Optimization Metrics]

[Presentation View].[Product MSU Forecast Metrics]

[Presentation View].[Product MSU Optimization Metrics]

[Presentation View].[Product MSU Utilization Metrics Without Z/OS]

[Presentation View].[Product MSU Utilization Metrics]

[Presentation View].[Prompts/Product Billable MSU Prompt View]

[Presentation View].[Raw Product MSU Utilization Metrics]

[Presentation View].[Sub-Capacity Program Pricing Structures]

[Presentation View].[Parents of Sub-Capacity Reference-based programs]

[Presentation View].[Timezone Metrics]

[Presentation View].[Z/OS Product MSU Utilization Metrics]

Standard prompts

date range

time range

timezone

Show First SCRT Cycle

Show Monetary Value

enterprise

cpu serial no

The values for the Enterprise and cpu serial no prompts are retrieved from the HCM.CUSTOMER_PRICE table. You must ensure that the required values are loaded in the table. Refer to the correct Configuration section for loading.

Workspace objects

The widgets in the Software Cost Analysis Summary workspace are in the Workspace > Objects > Charts & Tables > Software Cost Analysis folder.

The tables available on the Software Cost Analysis Summary are grouped in the following tabs that are at the bottom of the workspace:

Observed—shows the machine level summary and product summary information for the observed scenario.

Forecasted—shows the summary information, for comparison purposes, of both the forecasted and observed scenarios.

Optimized—shows the summary information, for comparison purposes, of both the optimized and forecasted scenarios.

Products Matrix—shows a list of registered products in three scenarios (Observed, Forecasted and Optimized) and IBM NO89 products.

Figure 56. Software Cost Analysis Summary workspace Observed tab

Observed tab: Machine Level Summary table

This table shows the summary information for each machine that you select in the prompts.

If the machine model changes during the specified period for a machine, the table shows you the highest machine model observed in the specified period.

Data Start and Data End is the date and time range within the specified period and with observed data for the specific CPC. If the value of Report Period % Data is below 95%, the value displays in red for quick identification.

Observed tab: Product Summary table

This table shows the billable MSU and/or monetary value for each registered IBM product that is running on the selected enterprise.

When Show Monetary Value is not selected, the Product Summary table is split into two tables: one for all IPLA and MLC products except z/OS®, and another for z/OS with zNALC consideration.

When Show Monetary Value is checked, the Product Summary table is split into three tables: one for IPLA, one for MLC except z/OS, and one for z/OS with zNALC consideration.

Important: When Show Monetary Value is checked, if the price of some products on some CPCs is not set in the HCM.CUSTOMER_PRICE table, then the billable MSUs of them is counted in the Total Billable MSUs column for All Machines, and not counted in the Monetary Total column for All Machines. The Monetary Total column for All Machines can calculate only the total of all CPCs with the price.

The Summary row is used to do the total aggregation for the Monetary Total column and the MSU columns.

If a product is running on some CPCs, but not on all the user-specified CPCs, then the related billable MSUs is shown as empty and the related monetary value appears as "N/A" or empty.

If all the values of a column are NULL, the column is not displayed in the Product Summary table.

Figure 57. Software Cost Analysis Summary workspace Forecasted tab

Forecasted tab: Machine Level Summary table

This table shows the Forecasted and Observed summary information for each machine that you select in the prompts.

If the machine model changes during the specified period for a machine, the table shows you the highest machine model in forecast streams for the specified period.

If there is no data for the Observed or Forecasted scenario for all machines, only the Below 95% and Highest MSU Hour Count columns are displayed in the table, with "N/A" or 0 values.

Forecasted tab: Product Summary table

This table shows the product summary information for both Forecasted and Observed scenarios.

When Show Monetary Value is not selected, the Product Summary table is split into two tables: one for all IPLA and MLC products except z/OS, and another for z/OS with zNALC consideration.

When Show Monetary Value is checked, the Product Summary table is split into three tables: one for IPLA, one for MLC except z/OS, and one for z/OS with zNALC consideration.

Important: When Show Monetary Value is checked, if the price of some products on some CPCs is not set in the HCM.CUSTOMER_PRICE table, then the billable MSUs of them is counted in the Total Billable MSUs column for All Machines, and not counted in the Monetary Total column for All Machines. The Monetary Total column for All Machines can calculate only the total of all CPCs with the price.

The Summary row is used to do the total aggregation for the Monetary Total column and the MSU columns.

If a product is running on some CPCs, but not on all the user-specified CPCs, then the related billable MSUs is shown as empty and the related monetary value appears as "N/A" or empty.

If all the values of a column are NULL, the column is not displayed in the Product Summary table.

Figure 58. Software Cost Analysis Summary workspace Optimized tab

Optimized tab: Machine Level Summary table

This table shows the Optimized and Forecasted summary information for each machine that you select in the prompts.

If the machine model changes during the specified period for a machine, the table shows you the highest machine model in optimization streams for the specified period.

If there is no data for the Optimized or Forecasted scenario for all machines, only the Below 95% and Highest MSU Hour Count columns are displayed in the table, with "N/A" or 0 values.

Optimized tab: Product Summary table

This table shows the product summary information for both Optimized and Forecasted scenarios.

When Show Monetary Value is not selected, the Product Summary table is split into two tables: one for all IPLA and MLC products except z/OS, and another for z/OS with zNALC consideration.

When Show Monetary Value is checked, the Product Summary table is split into three tables: one for IPLA, one for MLC except z/OS, and one for z/OS with zNALC consideration.

The Summary row is used to do the total aggregation for the Monetary Total column and the MSU columns.

Important: When Show Monetary Value is checked, if the price of some products on some CPCs is not set in the HCM.CUSTOMER_PRICE table, then the billable MSUs of them is counted in the Total Billable MSUs column for All Machines, and not counted in the Monetary Total column for All Machines. The Monetary Total column for All Machines can calculate only the total of all CPCs with the price.

If a product is running on some CPCs, but not on all the user-specified CPCs, then the related billable MSUs is shown as empty and the related monetary value appears as "N/A" or empty.

If all the values of a column are NULL, the column is not displayed in the Product Summary table.

Optimized tab: Suggested Actions table

This table shows the suggested actions from the optimization stream results.

When Show Monetary Value is not selected, this table does not appear on the Optimized tab. Provide the overall suggested actions on all CPCs, so that the enterprise and cpu serial no prompts do not affect the result.

To provide the overall suggested actions for all CPCs, the result in the Suggested Actions table is not filtered by the enterprise and cpu serial number value prompts in the workspace global area.

Products Matrix tab

This tab shows the following tables:

Registered Products Matrix for Observed Scenario

Registered Products Matrix for Forecasted Scenario

Registered Products Matrix for Optimized Scenario

NO89 Products Matrix table

If the product is running on this LPAR, then the cross point of LPAR NAME and PRODUCT is marked by a check mark Check mark icon. For more information, see SCA: Registered Products Matrix and SCA: NO89 Products Matrix.

Parent topic: Report and workspace reference

zServer Monitoring Dashboard workspaceThis workspace provides a 360-degree view of the zServer system environment. Through timely identification of capacity bottlenecks and potential performance issues, IT can take steps to eliminate deficiencies and optimize provisioning for critical business functions. The benefits of using this dashboard include growing revenue by meeting business goals and SLAs for constant availability and scalability. It also helps with cost savings and cost avoidance of unnecessary software licenses. There are also efficiency and productivity gains from the reduction of administrative efforts. workspaceszServer Monitoring DashboardzServer Monitoring DashboardThis workspace provides a quick visual status of a zServer, its LPARs, and disk subsystems. Key metrics are provided at the overall machine level. CPU and Storage metrics and I/O and Network metrics are displayed at the LPAR level.Standard promptsdate rangetime rangeinterval bandcpu serial nolpar nameThe charts and tables available on the zServer Monitoring Dashboard are grouped on tabs that are located at the bottom of the workspace:Machine LevelCPU & Storage at LPAR LevelI/O & Network at LPAR LevelzServer Monitoring Dashboard Machine Level tab examplezServer Monitoring Dashboard Machine Level tab exampleMachine Level tab: LPAR Configuration Metrics table This table shows the latest LPAR configuration information of a zServer over the specified date and time range. Metrics include dedicated processor numbers, defined logical processor numbers, defined weight, and weight percentage over all LPARs for each processor type of each LPAR. Source table[Presentation View].[LPAR Level Metrics]Machine Level tab: Over/Under Share Weight table This table shows the usage of each LPAR against the total number of engines of each type in the shared pool of a CPC. It also shows the usage percentage over or under its guaranteed share of each LPAR due to the competition among LPARs. Each processor type is shown separately. Source table[Presentation View].[LPAR Level Metrics]Machine Level tab: MIPS Used at Machine Level table This table shows summary level information for the zServer, including the number of installed engines, the number of dedicated engines, installed MIPS, MIPS used, and the percentage of installed MIPS used. When there is value change during the selected date and time period, there is an asterisk marker (*) behind the value, and a footnote is displayed in top of the table that states "The value with * behind is maximum value for every Processor Type". Source table[Presentation View].[LPAR Level Metrics]Machine Level tab: DASD I/O Channel Utilization - Top 10 at Machine Level table This table displays when and where the top 10 DASD I/O Channel Utilization occurs and helps you identify potential problem points in the I/O channel. Although the default table configuration displays the top 10, you can modify the table to display any top number, as described in Customizing top N display for the zServer Monitoring DashboardMany tables and charts on the zServer Monitoring Dashboard are configured to display the top five or top ten items from a query. You can modify the widget associated with a given table or chart to return any top number (top N) of items.. Source table[Presentation View].[Channel Path Activity Metrics]Machine Level tab: LCU I/O Queuing Delay Time - Top 10 at Machine Level chart This chart shows the top 10 longest delays by LCU, LPAR Name, and MVS System ID. The amount of time that is required to generate this chart increases as the date range for the chart is increased. To minimize waiting time for chart generation, run live reports for weekly or fewer date ranges, and run scheduled reports for monthly or longer date ranges. Although the default chart configuration displays the top 10, you can modify the table to display any top number, as described in Customizing top N display for the zServer Monitoring DashboardMany tables and charts on the zServer Monitoring Dashboard are configured to display the top five or top ten items from a query. You can modify the widget associated with a given table or chart to return any top number (top N) of items.. Source tables[Presentation View].[Raw Device Activity Metrics] [Presentation View].[LPAR Level Metrics]Machine Level tab: DASD I/O Queuing Delay Intensity - Top 10 at Machine Level table This table identifies the top 10 largest queuing delay intensities by Volume Serial Number (VOLSER) and LPAR name. Although the default table configuration displays the top 10, you can modify the table to display any top number, as described in Customizing top N display for the zServer Monitoring DashboardMany tables and charts on the zServer Monitoring Dashboard are configured to display the top five or top ten items from a query. You can modify the widget associated with a given table or chart to return any top number (top N) of items.. Source table[Presentation View].[Device Activity Metrics]zServer Monitoring Dashboard CPU & Storage at LPAR Level tab examplezServer Monitoring Dashboard CPU & Storage at LPAR Level tab exampleCPU & Storage at LPAR Level tab: MIPS Used at LPAR Level chart This chart shows the actual and forecasted CPU MIPS used at the LPAR level separated by processor type. Source tables[Presentation View].[LPAR Level Metrics] [Presentation View].[WLM Service Class Period Usage/Delay Metrics][Presentation View].LPAR Level Forecast Metrics]Drill through to the following report: CPU: MIPS Used - Service Class Period LevelCPU & Storage at LPAR Level tab: CPU - Latent Demand chart This chart shows latent demand in terms of In & Ready Address Spaces or Work Units (for z/OS 1.12 or higher) in relation to the CPU Delay counts by WLM Importance Level. Source tables[Presentation View].[z/OS System Level Metrics] [Presentation View].[LPAR Level Metrics] [Presentation View].[WLM Service Class Period Usage/Delay Metrics]Drill through to the following report: WLM: Delays by Importance LevelThis report shows you the effect of delays for each WLM importance level that is running on the system. Delaying high importance workloads can result in missed Service Level Agreements (SLAs) which has a higher cost than delaying less important workloads. It is important to monitor these delays so that you can take timely action to control your costs and maintain your SLAs.CPU & Storage at LPAR Level tab: Memory Usage chart This chart shows minimum, maximum, and average available central storage, along with page-in and page-out values. Source tables[Presentation View].[MVS Paging Activity Metrics] [Presentation View].[LPAR Level Metrics]zServer Monitoring Dashboard I/O & Network at LPAR Level tab examplezServer Monitoring Dashboard I/O & Network at LPAR Level tab exampleI/O & Network at LPAR Level tab: DASD I/O Response Time Components & I/O Rate chart This chart shows DASD I/O Response Time components and I/O Rate by interval and LPAR, assuming LPAR names are unique across all zServers. Source table[Presentation View].[Device Activity Metrics]I/O & Network at LPAR Level tab: Top 5 DASD I/O Channel Path Utilizations chart This chart shows the top five DASD I/O Channel Path Utilizations, which occur at a System Management Facilities (SMF) time interval on a channel path. When the interval band you select coincides with the SMF time interval, the chart displays five stacked bars. When the interval band is larger than the SMF time interval, there might be fewer than five stacked bars on the chart if the same channel path ID occurs in the top five at different time points. Although the default chart configuration displays the top five, you can modify the table to display any top number, as described in Customizing top N display for the zServer Monitoring DashboardMany tables and charts on the zServer Monitoring Dashboard are configured to display the top five or top ten items from a query. You can modify the widget associated with a given table or chart to return any top number (top N) of items.. Source table[Presentation View].[Channel Path Activity Metrics]I/O & Network at LPAR Level tab: Top 5 Max TCP/IP Server Ports Max Connections chart This chart shows the top five maximum TCP/IP Server Port connections. Although the default chart configuration displays the top five, you can modify the table to display any top number, as described in Customizing top N display for the zServer Monitoring DashboardMany tables and charts on the zServer Monitoring Dashboard are configured to display the top five or top ten items from a query. You can modify the widget associated with a given table or chart to return any top number (top N) of items.. Source tables[Presentation View].[TCP Server Port Connection Metrics][Presentation View].[LPAR Level Metrics]Report and workspace referenceIBM Capacity Management Analytics provides several predefined reports and workspaces that let you monitor and analyze the capacity of your IT services and the IT infrastructure.

zServer Monitoring Dashboard workspace

This workspace provides a 360-degree view of the zServer system environment. Through timely identification of capacity bottlenecks and potential performance issues, IT can take steps to eliminate deficiencies and optimize provisioning for critical business functions. The benefits of using this dashboard include growing revenue by meeting business goals and SLAs for constant availability and scalability. It also helps with cost savings and cost avoidance of unnecessary software licenses. There are also efficiency and productivity gains from the reduction of administrative efforts.

This workspace provides a quick visual status of a zServer, its LPARs, and disk subsystems. Key metrics are provided at the overall machine level. CPU and Storage metrics and I/O and Network metrics are displayed at the LPAR level.

Standard prompts

date range

time range

interval band

cpu serial no

lpar name

The charts and tables available on the zServer Monitoring Dashboard are grouped on tabs that are located at the bottom of the workspace:

Machine Level

CPU & Storage at LPAR Level

I/O & Network at LPAR Level

Figure 59. zServer Monitoring Dashboard Machine Level tab example

Machine Level tab: LPAR Configuration Metrics table

This table shows the latest LPAR configuration information of a zServer over the specified date and time range. Metrics include dedicated processor numbers, defined logical processor numbers, defined weight, and weight percentage over all LPARs for each processor type of each LPAR.

Source table

[Presentation View].[LPAR Level Metrics]

Machine Level tab: Over/Under Share Weight table

This table shows the usage of each LPAR against the total number of engines of each type in the shared pool of a CPC. It also shows the usage percentage over or under its guaranteed share of each LPAR due to the competition among LPARs.

Each processor type is shown separately.

Source table

[Presentation View].[LPAR Level Metrics]

Machine Level tab: MIPS Used at Machine Level table

This table shows summary level information for the zServer, including the number of installed engines, the number of dedicated engines, installed MIPS, MIPS used, and the percentage of installed MIPS used.

When there is value change during the selected date and time period, there is an asterisk marker (*) behind the value, and a footnote is displayed in top of the table that states "The value with * behind is maximum value for every Processor Type".

Source table

[Presentation View].[LPAR Level Metrics]

Machine Level tab: DASD I/O Channel Utilization - Top 10 at Machine Level table

This table displays when and where the top 10 DASD I/O Channel Utilization occurs and helps you identify potential problem points in the I/O channel.

Although the default table configuration displays the top 10, you can modify the table to display any top number, as described in Customizing top N display for the zServer Monitoring Dashboard.

Source table

[Presentation View].[Channel Path Activity Metrics]

Machine Level tab: LCU I/O Queuing Delay Time - Top 10 at Machine Level chart

This chart shows the top 10 longest delays by LCU, LPAR Name, and MVS™ System ID.

The amount of time that is required to generate this chart increases as the date range for the chart is increased. To minimize waiting time for chart generation, run live reports for weekly or fewer date ranges, and run scheduled reports for monthly or longer date ranges.

Although the default chart configuration displays the top 10, you can modify the table to display any top number, as described in Customizing top N display for the zServer Monitoring Dashboard.

Source tables

[Presentation View].[Raw Device Activity Metrics]

[Presentation View].[LPAR Level Metrics]

Machine Level tab: DASD I/O Queuing Delay Intensity - Top 10 at Machine Level table

This table identifies the top 10 largest queuing delay intensities by Volume Serial Number (VOLSER) and LPAR name.

Although the default table configuration displays the top 10, you can modify the table to display any top number, as described in Customizing top N display for the zServer Monitoring Dashboard.

Source table

[Presentation View].[Device Activity Metrics]

Figure 60. zServer Monitoring Dashboard CPU & Storage at LPAR Level tab example

CPU & Storage at LPAR Level tab: MIPS Used at LPAR Level chart

This chart shows the actual and forecasted CPU MIPS used at the LPAR level separated by processor type.

Source tables

[Presentation View].[LPAR Level Metrics]

[Presentation View].[WLM Service Class Period Usage/Delay Metrics]

[Presentation View].LPAR Level Forecast Metrics]

Drill through to the following report:

CPU: MIPS Used - Service Class Period Level

CPU & Storage at LPAR Level tab: CPU - Latent Demand chart

This chart shows latent demand in terms of In & Ready Address Spaces or Work Units (for z/OS® 1.12 or higher) in relation to the CPU Delay counts by WLM Importance Level.

Source tables

[Presentation View].[z/OS System Level Metrics]

[Presentation View].[LPAR Level Metrics]

[Presentation View].[WLM Service Class Period Usage/Delay Metrics]

Drill through to the following report:

WLM: Delays by Importance Level

CPU & Storage at LPAR Level tab: Memory Usage chart

This chart shows minimum, maximum, and average available central storage, along with page-in and page-out values.

Source tables

[Presentation View].[MVS Paging Activity Metrics]

[Presentation View].[LPAR Level Metrics]

Figure 61. zServer Monitoring Dashboard I/O & Network at LPAR Level tab example

I/O & Network at LPAR Level tab: DASD I/O Response Time Components & I/O Rate chart

This chart shows DASD I/O Response Time components and I/O Rate by interval and LPAR, assuming LPAR names are unique across all zServers.

Source table

[Presentation View].[Device Activity Metrics]

I/O & Network at LPAR Level tab: Top 5 DASD I/O Channel Path Utilizations chart

This chart shows the top five DASD I/O Channel Path Utilizations, which occur at a System Management Facilities (SMF) time interval on a channel path.

When the interval band you select coincides with the SMF time interval, the chart displays five stacked bars. When the interval band is larger than the SMF time interval, there might be fewer than five stacked bars on the chart if the same channel path ID occurs in the top five at different time points.

Although the default chart configuration displays the top five, you can modify the table to display any top number, as described in Customizing top N display for the zServer Monitoring Dashboard.

Source table

[Presentation View].[Channel Path Activity Metrics]

I/O & Network at LPAR Level tab: Top 5 Max TCP/IP Server Ports Max Connections chart

This chart shows the top five maximum TCP/IP Server Port connections.

Although the default chart configuration displays the top five, you can modify the table to display any top number, as described in Customizing top N display for the zServer Monitoring Dashboard.

Source tables

[Presentation View].[TCP Server Port Connection Metrics]

[Presentation View].[LPAR Level Metrics]

Parent topic: Report and workspace reference

Product VersionThe Product Version report displays the IBM Capacity Management Analytics copyright, version, and build information.reportsProduct VersionProduct Version reportThe report is available in the IBM Capacity Management Analytics Solution Kit folder in IBM Cognos Connection.The same information is available in other IBM Capacity Management Analytics reports by clicking the help icon Help icon, and clicking About IBM CMA.Report and workspace referenceIBM Capacity Management Analytics provides several predefined reports and workspaces that let you monitor and analyze the capacity of your IT services and the IT infrastructure.

Product Version

The Product Version report displays the IBM® Capacity Management Analytics copyright, version, and build information.

The report is available in the IBM Capacity Management Analytics Solution Kit folder in IBM Cognos® Connection.

The same information is available in other IBM Capacity Management Analytics reports by clicking the help icon Help icon, and clicking About IBM CMA.

Parent topic: Report and workspace reference

Uninstalling Capacity Management AnalyticsFollow these procedures to uninstall IBM Capacity Management Analytics.uninstallingUninstalling Capacity Management AnalyticsUninstalling the Capacity Management Analytics Solution Kit contentYou can delete the solution kit report content by deleting the IBM Capacity Management Analytics Solution Kit folder from the Public folders tab in IBM Cognos Connection, and then deleting the artifacts from IBM Cognos Administration.Uninstalling CPLEX components from the serverOnce you have stopped all the services, you can proceed to uninstalling CPLEX components from the server.Uninstalling SPSS components from the serverOnce you have stopped all the services, you can proceed to uninstalling SPSS components from the server.Uninstalling Cognos Analytics from the serverOnce you have stopped all the services, you can proceed to uninstalling Cognos Analytics components from the server.Uninstalling DB2 from the serverOnce you have stopped all the services, you can proceed to uninstalling DB2 components from the server.Uninstalling the solution installerIf you used the solution installer to install IBM Capacity Management Analytics on Red Hat Enterprise Linux operating systems, you can uninstall the solution installer separately.

Uninstalling Capacity Management Analytics

Follow these procedures to uninstall IBM® Capacity Management Analytics.

Uninstalling Capacity Management Analytics

Uninstalling the Capacity Management Analytics Solution Kit content
You can delete the solution kit report content by deleting the IBM Capacity Management Analytics Solution Kit folder from the Public folders tab in IBM Cognos Connection, and then deleting the artifacts from IBM Cognos Administration.

Uninstalling CPLEX components from the server
Once you have stopped all the services, you can proceed to uninstalling CPLEX components from the server.

Uninstalling SPSS components from the server
Once you have stopped all the services, you can proceed to uninstalling SPSS components from the server.

Uninstalling Cognos Analytics from the server
Once you have stopped all the services, you can proceed to uninstalling Cognos Analytics components from the server.

Uninstalling DB2 from the server
Once you have stopped all the services, you can proceed to uninstalling DB2® components from the server.

Uninstalling the solution installer
If you used the solution installer to install IBM Capacity Management Analytics on Red Hat Enterprise Linux operating systems, you can uninstall the solution installer separately.

Uninstalling Capacity Management AnalyticsuninstallingDrop the IBM Capacity Management Analytics database.You must always drop the Capacity Management Analytics database first.Run cmabase.py --dropdb [--run] [--verbose] [--trace=tracelevel]You can run cmabase.py --dropdb first to review the commands and run cmabase.py --dropdb --run to drop the Capacity Management Analytics database.Drop the sample database. Run cmasampledb.py --dropdb [--run] [--verbose] [--trace=tracelevel].You can run cmasampledb.py --dropdb first to review the commands and run cmasampledb.py --dropdb --run to drop the sample database.Clean up CMAINSTANCE and the Capacity Management Analytics features. Clean up of the Capacity Management Analytics features removes all related files in your CMAINSTANCE directory, including logs and input files. Clean up of CMAINSTANCE deletes the entire CMAINSTANCE directory.Run cmainstance.py --cleanup=<feature> [--verbose] [--trace=tracelevel]--cleanup=featureCleanup of the Capacity Management Analytics Instance. BASE (CMA base), AA (Application Analytics), PI (Anomaly Detection), CPF (Capacity Planning and Forecasting), SCA (Software Cost Analysis), SMO (Systems Management and Optimizations), SAMPLE (Sample Database), INSTANCE (Instance Directory) For instance, AA, SCA, PI. You also can run the Capacity Management Analytics feature program to clean up individual features. For instance, cmabase.py –cleanup. Additionally, if you clean up any features by mistake, you can always create a new one by running cmainstance.py --instance or another feature program, for instance, cmabase.py --instance. For more information, see Manually installing IBM Capacity Management Analytics 2.1.1If you want to run IBM Capacity Management Analyticson a platform other than 64-bit Red Hat Enterprise Linux Server Edition version 6 and 7, you must install the product manually.Uninstall Capacity Management Analytics feature and the Capacity Management Analytics Solution Kit. You can uninstall one or more Capacity Management Analytics features or uninstall the entire Solution Kit.Run cmahome.py –uninstall=<feature> [--verbose] [--trace=tracelevel]--uninstall=featureUninstall Capacity Management Analytics. BASE (CMA base), AA (Application Analytics), PI (Anomaly Detection), CPF (Capacity Planning and Forecasting), SCA (Software Cost Analysis), SMO (Systems Management and Optimizations), SAMPLE (Sample Database), SK (CMA Solution Kit) For instance, AA, SCA, PI. You can also run the Capacity Management Analytics feature program to uninstall individual features For instance, cmabase.py --uninstall. Additionally, you can always reinstall Capacity Management Analytics features by running cmahome.py --install --feature=<feature>. If you want to reinstall the Capacity Management Analytics Solution Kit, you have to manually extract the tarball.Uninstalling Capacity Management AnalyticsFollow these procedures to uninstall IBM Capacity Management Analytics.

Uninstalling Capacity Management Analytics

Procedure

Drop the IBM® Capacity Management Analytics database.

Note: You must always drop the Capacity Management Analytics database first.

Run cmabase.py --dropdb [--run] [--verbose] [--trace=tracelevel]

Note: You can run cmabase.py --dropdb first to review the commands and run cmabase.py --dropdb --run to drop the Capacity Management Analytics database.

Drop the sample database. Run cmasampledb.py --dropdb [--run] [--verbose] [--trace=tracelevel].

Note: You can run cmasampledb.py --dropdb first to review the commands and run cmasampledb.py --dropdb --run to drop the sample database.

Clean up CMAINSTANCE and the Capacity Management Analytics features. Clean up of the Capacity Management Analytics features removes all related files in your CMAINSTANCE directory, including logs and input files. Clean up of CMAINSTANCE deletes the entire CMAINSTANCE directory.

Run cmainstance.py --cleanup=<feature> [--verbose] [--trace=tracelevel]

--cleanup=feature

Cleanup of the Capacity Management Analytics Instance. BASE (CMA base), AA (Application Analytics), PI (Anomaly Detection), CPF (Capacity Planning and Forecasting), SCA (Software Cost Analysis), SMO (Systems Management and Optimizations), SAMPLE (Sample Database), INSTANCE (Instance Directory) For instance, AA, SCA, PI.

Note: You also can run the Capacity Management Analytics feature program to clean up individual features. For instance, cmabase.py –cleanup. Additionally, if you clean up any features by mistake, you can always create a new one by running cmainstance.py --instance or another feature program, for instance, cmabase.py --instance. For more information, see Manually installing IBM Capacity Management Analytics 2.1.1

Uninstall Capacity Management Analytics feature and the Capacity Management Analytics Solution Kit. You can uninstall one or more Capacity Management Analytics features or uninstall the entire Solution Kit.

Run cmahome.py –uninstall=<feature> [--verbose] [--trace=tracelevel]

--uninstall=feature

Uninstall Capacity Management Analytics. BASE (CMA base), AA (Application Analytics), PI (Anomaly Detection), CPF (Capacity Planning and Forecasting), SCA (Software Cost Analysis), SMO (Systems Management and Optimizations), SAMPLE (Sample Database), SK (CMA Solution Kit) For instance, AA, SCA, PI.

Note: You can also run the Capacity Management Analytics feature program to uninstall individual features For instance, cmabase.py --uninstall. Additionally, you can always reinstall Capacity Management Analytics features by running cmahome.py --install --feature=<feature>. If you want to reinstall the Capacity Management Analytics Solution Kit, you have to manually extract the tarball.

Parent topic: Uninstalling Capacity Management Analytics

Uninstalling the Capacity Management Analytics Solution Kit contentYou can delete the solution kit report content by deleting the IBM Capacity Management Analytics Solution Kit folder from the Public folders tab in IBM Cognos Connection, and then deleting the artifacts from IBM Cognos Administration.uninstallingCognos deploymentIn IBM Cognos Connection, click Public folders.Select the IBM Capacity Management Analytics Solution Kit folder, and click the Delete icon.In IBM Cognos Connection, click LaunchIBM Cognos Administration.Click the Configuration tab.Select the CMADW entry, then click the Delete icon.On the Configuration tab, click Content Administration.Select the deployment entry and click the Delete icon.Go to the Cognos_install_dir/deployment directory on the computer where IBM Cognos Business Intelligence is installed and delete cma-2.1.zip.Uninstalling Capacity Management AnalyticsFollow these procedures to uninstall IBM Capacity Management Analytics.

Uninstalling the Capacity Management Analytics Solution Kit content

You can delete the solution kit report content by deleting the IBM Capacity Management Analytics Solution Kit folder from the Public folders tab in IBM Cognos Connection, and then deleting the artifacts from IBM Cognos Administration.

Procedure

In IBM Cognos Connection, click Public folders.

Select the IBM Capacity Management Analytics Solution Kit folder, and click the Delete icon.

In IBM Cognos Connection, click Launch > IBM Cognos Administration.

Click the Configuration tab.

Select the CMADW entry, then click the Delete icon.

On the Configuration tab, click Content Administration.

Select the deployment entry and click the Delete icon.

Go to the Cognos_install_dir/deployment directory on the computer where IBM® Cognos® Business Intelligence is installed and delete cma-2.1.zip.

Parent topic: Uninstalling Capacity Management Analytics

Uninstalling CPLEX components from the serverOnce you have stopped all the services, you can proceed to uninstalling CPLEX components from the server.To uninstall CPLEX, see the ILOG CPLEX Optimization Studio ILOG CPLEX Optimization Studio(http://www.ibm.com/support/knowledgecenter/SSSA5P)Log in as the Capacity Management Analytics administrator or an ID that has sudo or root.Run the following commands:sudo /opt/ibm/ILOG/CPLEX_Studio1263/Uninstall/Uninstallsudo rm -rf /opt/ibm/ILOGUninstalling Capacity Management AnalyticsFollow these procedures to uninstall IBM Capacity Management Analytics.

Uninstalling CPLEX components from the server

Once you have stopped all the services, you can proceed to uninstalling CPLEX components from the server.

About this task

To uninstall CPLEX, see the ILOG CPLEX Optimization Studio ILOG CPLEX Optimization Studio(http://www.ibm.com/support/knowledgecenter/SSSA5P)

Procedure

Log in as the Capacity Management Analytics administrator or an ID that has sudo or root.

Run the following commands:

sudo /opt/ibm/ILOG/CPLEX_Studio1263/Uninstall/Uninstall

sudo rm -rf /opt/ibm/ILOG

Parent topic: Uninstalling Capacity Management Analytics

Uninstalling SPSS components from the serverOnce you have stopped all the services, you can proceed to uninstalling SPSS components from the server.To uninstall SPSS Modeler, see the SPSS Modeler documentation at SPSS Modeler V17.1.0 documentation(http://www.ibm.com/support/knowledgecenter/SS3RA7_17.1.0/clementine/knowledge_center/product_landing.html).Log in as the Capacity Management Analytics administrator or an ID that has sudo or root.Run the following commands: sudo /opt/ibm/SPSS/ModelerBatch/17.1/Uninstall_IBM_SPSS_MODELER_BATCH/Uninstall_IBM_SPSS_MODELER_BATCHsudo /opt/ibm/SPSS/ModelerServer/17.1/Uninstall_IBM_SPSS_PREMIUM_SERVER/Uninstall_IBM_SPSS_Premium_SERVERsudo /opt/ibm/SPSS/ModelerServer/17.1/Uninstall_IBM_SPSS_MODELER_SERVER/Uninstall_IBM_SPSS_MODELER_SERVERsudo rm -rf /opt/ibm/SPSSUninstalling Capacity Management AnalyticsFollow these procedures to uninstall IBM Capacity Management Analytics.

Uninstalling SPSS components from the server

Once you have stopped all the services, you can proceed to uninstalling SPSS components from the server.

About this task

To uninstall SPSS Modeler, see the SPSS Modeler documentation at SPSS Modeler V17.1.0 documentation(http://www.ibm.com/support/knowledgecenter/SS3RA7_17.1.0/clementine/knowledge_center/product_landing.html).

Procedure

Log in as the Capacity Management Analytics administrator or an ID that has sudo or root.

Run the following commands:

sudo /opt/ibm/SPSS/ModelerBatch/17.1/Uninstall_IBM_SPSS_MODELER_BATCH/Uninstall_IBM_SPSS_MODELER_BATCH

sudo /opt/ibm/SPSS/ModelerServer/17.1/Uninstall_IBM_SPSS_PREMIUM_SERVER/Uninstall_IBM_SPSS_Premium_SERVER

sudo /opt/ibm/SPSS/ModelerServer/17.1/Uninstall_IBM_SPSS_MODELER_SERVER/Uninstall_IBM_SPSS_MODELER_SERVER

sudo rm -rf /opt/ibm/SPSS

Parent topic: Uninstalling Capacity Management Analytics

Uninstalling Cognos Analytics from the serverOnce you have stopped all the services, you can proceed to uninstalling Cognos Analytics components from the server.To uninstall IBM Cognos Business Intelligence, see the IBM Cognos Analytics documentation at IBM Cognos Analytics 11.0 documentation(http://www.ibm.com/support/knowledgecenter/SSEP7J_11.0.0/com.ibm.swg.ba.cognos.cbi.doc/welcome.html).Log in as the Capacity Management Analytics administrator or an ID that has sudo or root.Run the following commands:sudo /opt/ibm/cognos/analytics/Uninstall_IBM_Cognos_Analyticssudo rm -rf /opt/ibm/cognosUninstalling Capacity Management AnalyticsFollow these procedures to uninstall IBM Capacity Management Analytics.

Uninstalling Cognos Analytics from the server

Once you have stopped all the services, you can proceed to uninstalling Cognos Analytics components from the server.

About this task

To uninstall IBM Cognos Business Intelligence, see the IBM Cognos Analytics documentation at IBM Cognos Analytics 11.0 documentation(http://www.ibm.com/support/knowledgecenter/SSEP7J_11.0.0/com.ibm.swg.ba.cognos.cbi.doc/welcome.html).

Procedure

Log in as the Capacity Management Analytics administrator or an ID that has sudo or root.

Run the following commands:

sudo /opt/ibm/cognos/analytics/Uninstall_IBM_Cognos_Analytics

sudo rm -rf /opt/ibm/cognos

Parent topic: Uninstalling Capacity Management Analytics

Uninstalling DB2 from the serverOnce you have stopped all the services, you can proceed to uninstalling DB2 components from the server.To uninstall IBM DB2, see the IBM DB2 documentation at IBM DB2 10.5 for Linux, Unix and Windows documentation(http://www.ibm.com/support/knowledgecenter/SSEPGG_10.5.0/com.ibm.db2.luw.kc.doc/welcome.html).Remove the licence and iso-swid directories.Uninstalling Capacity Management AnalyticsFollow these procedures to uninstall IBM Capacity Management Analytics.

Uninstalling DB2 from the server

Once you have stopped all the services, you can proceed to uninstalling DB2® components from the server.

About this task

To uninstall IBM DB2, see the IBM DB2 documentation at IBM DB2 10.5 for Linux, Unix and Windows documentation(http://www.ibm.com/support/knowledgecenter/SSEPGG_10.5.0/com.ibm.db2.luw.kc.doc/welcome.html).

Procedure

Remove the licence and iso-swid directories.

Parent topic: Uninstalling Capacity Management Analytics

Uninstalling the solution installerIf you used the solution installer to install IBM Capacity Management Analytics on Red Hat Enterprise Linux operating systems, you can uninstall the solution installer separately. When you uninstall the solution installer, the processes that are used by the solution installer are stopped and removed from your computer. Also, all of the files in the solution installer directory are removed. This includes the Capacity Management Analytics server component installation files that you copied into the solution installer directory. If you want to preserve those files, you must copy them to another directory before you uninstall the solution installer. Uninstalling the solution installer does not uninstall the Capacity Management Analytics component applications. If you want to uninstall those components, you must use each component's uninstallation programs.Log in as the Capacity Management Analytics administrator or with an ID that has sudo or root.Optional: go to the SolutionInstaller/NodeRoot/Downloads/ software directory, and copy the *.tar.gz files outside of the SolutionInstaller directory.Remove the Chef client.sudo rpm –ev chefRemove NodeJS by running the following:sudo – sh –c ‘export PATH=${PATH}:/opt/ibm/nodejs1.2.0.1/bin; forever stop 0’sudo – sh –c ‘export PATH=${PATH}:/opt/ibm/nodejs1.2.0.1/bin; npm uninstall express’sudo – sh –c ‘export PATH=${PATH}:/opt/ibm/nodejs1.2.0.1/bin; npm uninstall asyncsudo – sh –c ‘export PATH=${PATH}:/opt/ibm/nodejs1.2.0.1/bin; npm uninstall body-parsersudo – sh –c ‘export PATH=${PATH}:/opt/ibm/nodejs1.2.0.1/bin; npm uninstall forversudo rm –rf /opt/ibm/nodejs1.2.0.1/Remove the solution installer folder.Run sudo rm –rf SolutionInstallerUninstalling Capacity Management AnalyticsFollow these procedures to uninstall IBM Capacity Management Analytics.

Uninstalling the solution installer

If you used the solution installer to install IBM® Capacity Management Analytics on Red Hat Enterprise Linux operating systems, you can uninstall the solution installer separately.

When you uninstall the solution installer, the processes that are used by the solution installer are stopped and removed from your computer. Also, all of the files in the solution installer directory are removed. This includes the Capacity Management Analytics server component installation files that you copied into the solution installer directory. If you want to preserve those files, you must copy them to another directory before you uninstall the solution installer.

Note: Uninstalling the solution installer does not uninstall the Capacity Management Analytics component applications. If you want to uninstall those components, you must use each component's uninstallation programs.

Procedure

Log in as the Capacity Management Analytics administrator or with an ID that has sudo or root.

Note: Optional: go to the SolutionInstaller/NodeRoot/Downloads/ software directory, and copy the *.tar.gz files outside of the SolutionInstaller directory.

Remove the Chef client.

sudo rpm –ev chef

Remove NodeJS by running the following:

sudo – sh –c ‘export PATH=${PATH}:/opt/ibm/nodejs1.2.0.1/bin; forever stop 0’

sudo – sh –c ‘export PATH=${PATH}:/opt/ibm/nodejs1.2.0.1/bin; npm uninstall express’

sudo – sh –c ‘export PATH=${PATH}:/opt/ibm/nodejs1.2.0.1/bin; npm uninstall async

sudo – sh –c ‘export PATH=${PATH}:/opt/ibm/nodejs1.2.0.1/bin; npm uninstall body-parser

sudo – sh –c ‘export PATH=${PATH}:/opt/ibm/nodejs1.2.0.1/bin; npm uninstall forver

sudo rm –rf /opt/ibm/nodejs1.2.0.1/

Remove the solution installer folder.

Run sudo rm –rf SolutionInstaller

Parent topic: Uninstalling Capacity Management Analytics

Accessibility featuresAccessibility features help users who have a physical disability, such as restricted mobility or limited vision, to use information technology products.accessibilityKeyboard shortcutsStandard Microsoft Windows navigation keys are used in addition to application-specific keys.IBM and accessibilitySee the IBM Accessibility Center (http://www.ibm.com/able) for more information about the commitment that IBM has to accessibility.

Accessibility features

Accessibility features help users who have a physical disability, such as restricted mobility or limited vision, to use information technology products.

Keyboard shortcuts
Standard Microsoft Windows navigation keys are used in addition to application-specific keys.

IBM and accessibility
See the IBM® Accessibility Center (http://www.ibm.com/able) for more information about the commitment that IBM has to accessibility.

Keyboard shortcutsStandard Microsoft Windows navigation keys are used in addition to application-specific keys.accessibilitykeyboard shortcutskeyboard shortcutsaccessibilityYou can use keyboard shortcuts to navigate through the application and perform tasks. If you are using a screen reader, you might want to maximize your window so the keyboard shortcut table is completely expanded and accessible. You might want to turn high contrast on in your operating system so the lines in diagrams and charts in the application are more visible.Note: The following keyboard shortcuts are based on U.S. standard keyboards.Keyboard shortcutsActionShortcut keysEnables or disables the accessibility feature - an IBM Cognos Report Studio restart is required. The default is disabled.Ctrl+Alt+AOpens the context menu for the selected item, if available.Shift+F10Switches focus from or to the main menu bar and the main worksheet.Ctrl+F10Opens the online help.F1Closes the Web browser window.Alt+F4Copies objects.Ctrl+CPastes objects.Ctrl+VCloses objects, such as a dialog box, menu, drop-down list, or page.EscCycles through objects, such as the tabs in a dialog, the objects in a page, the cells in a list, the rows in a crosstab, or check boxes.Tab, Shift+Tab, Arrow keysSelects the first or last item in a list.Home/Pg Up or End/Pg Dn keysSelects or clicks one or multiple objects, such as a radio button, menu or toolbar item, or node in a tree widget.In the Properties pane, expands or collapses a property group.Space or Enter, Ctrl+space or Shift+space, Ctrl+Enter or Shift+EnterAccessibility featuresAccessibility features help users who have a physical disability, such as restricted mobility or limited vision, to use information technology products.

Keyboard shortcuts

Standard Microsoft Windows navigation keys are used in addition to application-specific keys.

You can use keyboard shortcuts to navigate through the application and perform tasks. If you are using a screen reader, you might want to maximize your window so the keyboard shortcut table is completely expanded and accessible. You might want to turn high contrast on in your operating system so the lines in diagrams and charts in the application are more visible.

Note: The following keyboard shortcuts are based on U.S. standard keyboards.

Table 27. Keyboard shortcuts

Action

Shortcut keys

Enables or disables the accessibility feature - an IBM® Cognos® Report Studio restart is required. The default is disabled.

Ctrl+Alt+A

Opens the context menu for the selected item, if available.

Shift+F10

Switches focus from or to the main menu bar and the main worksheet.

Ctrl+F10

Opens the online help.

F1

Closes the Web browser window.

Alt+F4

Copies objects.

Ctrl+C

Pastes objects.

Ctrl+V

Closes objects, such as a dialog box, menu, drop-down list, or page.

Esc

Cycles through objects, such as the tabs in a dialog, the objects in a page, the cells in a list, the rows in a crosstab, or check boxes.

Tab, Shift+Tab, Arrow keys

Selects the first or last item in a list.

Home/Pg Up or End/Pg Dn keys

Selects or clicks one or multiple objects, such as a radio button, menu or toolbar item, or node in a tree widget.

In the Properties pane, expands or collapses a property group.

Space or Enter, Ctrl+space or Shift+space, Ctrl+Enter or Shift+Enter

Parent topic: Accessibility features

IBM and accessibilitySee the IBM Accessibility Center (http://www.ibm.com/able) for more information about the commitment that IBM has to accessibility.accessibilityAccessibility featuresAccessibility features help users who have a physical disability, such as restricted mobility or limited vision, to use information technology products.

IBM and accessibility

See the IBM® Accessibility Center (http://www.ibm.com/able) for more information about the commitment that IBM has to accessibility.

Parent topic: Accessibility features

Optional: Manual configuration tasksThe tasks in this chapter are run automatically by the IBM Capacity Management Analytics Solution Kit installation. Use these tasks only if you want to perform any of the configuration tasks manually.All of the tasks in this section are optional.Changing the default schema names in Framework Manager modelsThe IBM Capacity Management Analytics Framework Manager models use default schema names for the data sources. If you use different schema names, you must update the references to the appropriate schema names in the Framework Manager models. Use IBM DB2 Analytics Accelerator (IDAA) with IBM Capacity Management AnalyticsTo use IBM DB2 Analytics Accelerator (IDAA) with IBM Capacity Management Analytics, you can either configure ZPARMs in your DB2 configuration or enable IDAA in IBM Cognos Business Intelligence and IBM SPSS Modeler.Configuring IBM SPSS Modeler Server with DB2 CLI/ODBC on Linux on System z operating systemsComplete this procedure to configure IBM SPSS Modeler Server with DB2 CLI/ODBC on Linux on System z operating systems.Import Capacity Management Analytics content to IBM Cognos Business IntelligenceView IBM Capacity Management Analytics reports in the IBM Cognos Business Intelligence portal. The reports are imported to the portal from a deployment zip file that is included in the Capacity Management Analytics installation.Calibrating streams and models for new hardware generationsIf you are using IBM Capacity Management Analytics with new generations of hardware, you must ensure that the Capacity Management Analytics assets (the streams and models) are calibrated correctly.

Optional: Manual configuration tasks

The tasks in this chapter are run automatically by the IBM® Capacity Management Analytics Solution Kit installation. Use these tasks only if you want to perform any of the configuration tasks manually.

All of the tasks in this section are optional.

Changing the default schema names in Framework Manager models
The IBM Capacity Management Analytics Framework Manager models use default schema names for the data sources. If you use different schema names, you must update the references to the appropriate schema names in the Framework Manager models.

Use IBM DB2 Analytics Accelerator (IDAA) with IBM Capacity Management Analytics
To use IBM DB2® Analytics Accelerator (IDAA) with IBM Capacity Management Analytics, you can either configure ZPARMs in your DB2 configuration or enable IDAA in IBM Cognos® Business Intelligence and IBM SPSS® Modeler.

Configuring IBM SPSS Modeler Server with DB2 CLI/ODBC on Linux on System z operating systems
Complete this procedure to configure IBM SPSS Modeler Server with DB2 CLI/ODBC on Linux on System z® operating systems.

Import Capacity Management Analytics content to IBM Cognos Business Intelligence
View IBM Capacity Management Analytics reports in the IBM Cognos Business Intelligence portal. The reports are imported to the portal from a deployment zip file that is included in the Capacity Management Analytics installation.

Calibrating streams and models for new hardware generations
If you are using IBM Capacity Management Analytics with new generations of hardware, you must ensure that the Capacity Management Analytics assets (the streams and models) are calibrated correctly.

Changing the default schema names in Framework Manager modelsThe IBM Capacity Management Analytics Framework Manager models use default schema names for the data sources. If you use different schema names, you must update the references to the appropriate schema names in the Framework Manager models. There are 2 models: CMA.cpf and CMA DW.cpf. Each model has references to 3 data sources that contain the schema names: DRL, DRLSYS, and HCM. After you change the schema names in the models, you must republish the Framework Manager packages to IBM Cognos Business Intelligence. For more information about publishing packages, see IBM Knowledge Center (www.ibm.com/support/knowledgecenter/SSEP7J_10.2.1/com.ibm.swg.ba.cognos.ug_fm.10.2.1.doc/t_publishapackage.html%23PublishaPackage)..Launch IBM Cognos Framework Manager.From the Welcome page, click Open a project.Navigate to the location where you installed Capacity Management Analytics.In the fm_model folder, select CMA.cpf, and click Open.In the Project Viewer pane, expand Data Sources.Select CMADW_DRL, and in the Properties pane, enter the appropriate schema name in the Schema box.Select CMADW_DRLSYS, and in the Properties pane, enter the appropriate schema name in the Schema box.Select CMADW_HCM, and in the Properties pane, enter the appropriate schema name in the Schema box.Click FileSave.Click FileOpen.In the fm_model\CMA DW folder, select CMA DW.cpf, and click Open.In the Project Viewer pane, expand Data Sources.Select CMADW_DRL, and in the Properties pane, enter the appropriate schema name in the Schema box.Select CMADW_DRLSYS, and in the Properties pane, enter the appropriate schema name in the Schema box.Select CMADW_HCM, and in the Properties pane, enter the appropriate schema name in the Schema box.Click FileSave.Republish the packages.Open the CMA.cpf model again.In the Project Viewer pane, expand Packages.Right-click IBM Capacity Management Analytics, and click Publish Package.Right-click IBM Capacity Management Analytics Development, and click Publish Package.Optional: Manual configuration tasksThe tasks in this chapter are run automatically by the IBM Capacity Management Analytics Solution Kit installation. Use these tasks only if you want to perform any of the configuration tasks manually.

Changing the default schema names in Framework Manager models

The IBM® Capacity Management Analytics Framework Manager models use default schema names for the data sources. If you use different schema names, you must update the references to the appropriate schema names in the Framework Manager models.

There are 2 models: CMA.cpf and CMA DW.cpf. Each model has references to 3 data sources that contain the schema names: DRL, DRLSYS, and HCM.

After you change the schema names in the models, you must republish the Framework Manager packages to IBM Cognos® Business Intelligence.

For more information about publishing packages, see IBM Knowledge Center (www.ibm.com/support/knowledgecenter/SSEP7J_10.2.1/com.ibm.swg.ba.cognos.ug_fm.10.2.1.doc/t_publishapackage.html%23PublishaPackage)..

Procedure

Launch IBM Cognos Framework Manager.

From the Welcome page, click Open a project.

Navigate to the location where you installed Capacity Management Analytics.

In the fm_model folder, select CMA.cpf, and click Open.

In the Project Viewer pane, expand Data Sources.

Select CMADW_DRL, and in the Properties pane, enter the appropriate schema name in the Schema box.

Select CMADW_DRLSYS, and in the Properties pane, enter the appropriate schema name in the Schema box.

Select CMADW_HCM, and in the Properties pane, enter the appropriate schema name in the Schema box.

Click File > Save.

Click File > Open.

In the fm_model\CMA DW folder, select CMA DW.cpf, and click Open.

In the Project Viewer pane, expand Data Sources.

Select CMADW_DRL, and in the Properties pane, enter the appropriate schema name in the Schema box.

Select CMADW_DRLSYS, and in the Properties pane, enter the appropriate schema name in the Schema box.

Select CMADW_HCM, and in the Properties pane, enter the appropriate schema name in the Schema box.

Click File > Save.

Republish the packages.

Open the CMA.cpf model again.

In the Project Viewer pane, expand Packages.

Right-click IBM Capacity Management Analytics, and click Publish Package.

Right-click IBM Capacity Management Analytics Development, and click Publish Package.

Parent topic: Optional: Manual configuration tasks

Use IBM DB2 Analytics Accelerator (IDAA) with IBM Capacity Management AnalyticsTo use IBM DB2 Analytics Accelerator (IDAA) with IBM Capacity Management Analytics, you can either configure ZPARMs in your DB2 configuration or enable IDAA in IBM Cognos Business Intelligence and IBM SPSS Modeler. For information about configuring ZPARMs, see Setting ZPARMs for IDAA in DB2 for z/OSYou can enable IBM DB2 Analytics Accelerator (IDAA) acceleration for IBM Capacity Management Analytics by setting the appropriate ZPARMS in your DB2 configuration..For information about configuring Cognos and SPSS Modeler, see Configuring IBM Capacity Management Analytics to use IBM DB2 Analytics AcceleratorTo use IBM DB2 Analytics Accelerator (IDAA) with IBM Capacity Management Analytics, you can add a DB2 open session command block to the data source connection in IBM Cognos Business Intelligence and enable IDAA in SPSS Modeler..Before you enable IDAA To use IDAA with IBM Capacity Management Analytics, you must ensure that all database object's (the database, tables, tablespaces, UDFs, and stored procedures for both DRL and HCM databases) CCSID uses EBCDIC encoding. For example, the following function is encoded in EBCDIC by using the PARAMETER CCSID option: CREATE FUNCTION HCM.YEAR_BAND(d date) RETURNS DATE LANGUAGE SQL PARAMETER CCSID EBCDIC DETERMINISTIC READS SQL DATA NO EXTERNAL ACTION RETURN ( date ( varchar(year(d)) || ’-01-01’) ); The Tivoli Decision Support for z/OS DRL database uses EBCDIC encoding. To use IDAA, the HCM database must also use EBCDIC encoding. If you use Unicode for the HCM database, you must create a separate EBCDIC encoded database with a different schema and use the PARAMETER CCSID option.Optional: Manual configuration tasksThe tasks in this chapter are run automatically by the IBM Capacity Management Analytics Solution Kit installation. Use these tasks only if you want to perform any of the configuration tasks manually.Setting ZPARMs for IDAA in DB2 for z/OSYou can enable IBM DB2 Analytics Accelerator (IDAA) acceleration for IBM Capacity Management Analytics by setting the appropriate ZPARMS in your DB2 configuration.Configuring IBM Capacity Management Analytics to use IBM DB2 Analytics AcceleratorTo use IBM DB2 Analytics Accelerator (IDAA) with IBM Capacity Management Analytics, you can add a DB2 open session command block to the data source connection in IBM Cognos Business Intelligence and enable IDAA in SPSS Modeler.Verify that IDAA is being used in IBM Capacity Management AnalyticsYou can verify that the query or stream was run with IDAA by using IBM Data Studio or IBM z/OS System Display and Search Facility (SDSF) on the LPAR where the database resides.

Use IBM DB2 Analytics Accelerator (IDAA) with IBM Capacity Management Analytics

To use IBM® DB2® Analytics Accelerator (IDAA) with IBM Capacity Management Analytics, you can either configure ZPARMs in your DB2 configuration or enable IDAA in IBM Cognos® Business Intelligence and IBM SPSS® Modeler.

For information about configuring ZPARMs, see Setting ZPARMs for IDAA in DB2 for z/OS.

For information about configuring Cognos and SPSS Modeler, see Configuring IBM Capacity Management Analytics to use IBM DB2 Analytics Accelerator.

Before you enable IDAA

To use IDAA with IBM Capacity Management Analytics, you must ensure that all database object's (the database, tables, tablespaces, UDFs, and stored procedures for both DRL and HCM databases) CCSID uses EBCDIC encoding.

For example, the following function is encoded in EBCDIC by using the PARAMETER CCSID option:

CREATE FUNCTION HCM.YEAR_BAND(d date)
RETURNS DATE
LANGUAGE SQL
PARAMETER CCSID EBCDIC
DETERMINISTIC
READS SQL DATA
NO EXTERNAL ACTION
RETURN
(
date ( varchar(year(d)) || ’-01-01’)
);

The Tivoli Decision Support for z/OS DRL database uses EBCDIC encoding. To use IDAA, the HCM database must also use EBCDIC encoding. If you use Unicode for the HCM database, you must create a separate EBCDIC encoded database with a different schema and use the PARAMETER CCSID option.

Setting ZPARMs for IDAA in DB2 for z/OS
You can enable IBM DB2 Analytics Accelerator (IDAA) acceleration for IBM Capacity Management Analytics by setting the appropriate ZPARMS in your DB2 configuration.

Configuring IBM Capacity Management Analytics to use IBM DB2 Analytics Accelerator
To use IBM DB2 Analytics Accelerator (IDAA) with IBM Capacity Management Analytics, you can add a DB2 open session command block to the data source connection in IBM Cognos Business Intelligence and enable IDAA in SPSS Modeler.

Verify that IDAA is being used in IBM Capacity Management Analytics
You can verify that the query or stream was run with IDAA by using IBM Data Studio or IBM z™/OS System Display and Search Facility (SDSF) on the LPAR where the database resides.

Parent topic: Optional: Manual configuration tasks

Setting ZPARMs for IDAA in DB2 for z/OSYou can enable IBM DB2 Analytics Accelerator (IDAA) acceleration for IBM Capacity Management Analytics by setting the appropriate ZPARMS in your DB2 configuration. For more information, see the following topics in IBM Knowledge Center: Setting ZPARMs for IBM DB2 Analytics Accelerator in DB2 11 for z/OS (www.ibm.com/support/knowledgecenter/SS4LQ8_4.1.0/com.ibm.datatools.aqt.doc/installmanual/task/t_idaa_inst_zparms_db2_11.html)Enabling DB2 to work with IBM DB2 Analytics Accelerator for z/OS (www.ibm.com/support/knowledgecenter/SSEPEK_11.0.0/com.ibm.db2z11.doc.perf/src/tpc/db2z_enablingaccelerators.dita) If you set QUERY_ACCELERATION to ALL, then all queries use IDAA and you do not have to do any additional configuration. If you are using another setting, some queries might not use IDAA. In this case, you can enable IDAA in IBM Cognos Business Intelligence and IBM SPSS Modeler. For more information, see Configuring IBM Capacity Management Analytics to use IBM DB2 Analytics AcceleratorTo use IBM DB2 Analytics Accelerator (IDAA) with IBM Capacity Management Analytics, you can add a DB2 open session command block to the data source connection in IBM Cognos Business Intelligence and enable IDAA in SPSS Modeler..Use IBM DB2 Analytics Accelerator (IDAA) with IBM Capacity Management AnalyticsTo use IBM DB2 Analytics Accelerator (IDAA) with IBM Capacity Management Analytics, you can either configure ZPARMs in your DB2 configuration or enable IDAA in IBM Cognos Business Intelligence and IBM SPSS Modeler.

Setting ZPARMs for IDAA in DB2 for z/OS

You can enable IBM® DB2® Analytics Accelerator (IDAA) acceleration for IBM Capacity Management Analytics by setting the appropriate ZPARMS in your DB2 configuration.

For more information, see the following topics in IBM Knowledge Center:

Setting ZPARMs for IBM DB2 Analytics Accelerator in DB2 11 for z/OS® (www.ibm.com/support/knowledgecenter/SS4LQ8_4.1.0/com.ibm.datatools.aqt.doc/installmanual/task/t_idaa_inst_zparms_db2_11.html)

Enabling DB2 to work with IBM DB2 Analytics Accelerator for z/OS (www.ibm.com/support/knowledgecenter/SSEPEK_11.0.0/com.ibm.db2z11.doc.perf/src/tpc/db2z_enablingaccelerators.dita)

If you set QUERY_ACCELERATION to ALL, then all queries use IDAA and you do not have to do any additional configuration.

If you are using another setting, some queries might not use IDAA. In this case, you can enable IDAA in IBM Cognos® Business Intelligence and IBM SPSS® Modeler. For more information, see Configuring IBM Capacity Management Analytics to use IBM DB2 Analytics Accelerator.

Parent topic: Use IBM DB2 Analytics Accelerator (IDAA) with IBM Capacity Management Analytics

Configuring IBM Capacity Management Analytics to use IBM DB2 Analytics AcceleratorTo use IBM DB2 Analytics Accelerator (IDAA) with IBM Capacity Management Analytics, you can add a DB2 open session command block to the data source connection in IBM Cognos Business Intelligence and enable IDAA in SPSS Modeler. Alternatively, you can use ZPARMs for IDAA. For more information, see Setting ZPARMs for IDAA in DB2 for z/OSYou can enable IBM DB2 Analytics Accelerator (IDAA) acceleration for IBM Capacity Management Analytics by setting the appropriate ZPARMS in your DB2 configuration..IDAADB2 Analytics AcceleratorEnable IDAA in IBM Cognos Business Intelligence:In IBM Cognos Connection, click LaunchIBM Cognos Administration.On the Configuration tab, select Data Source Connections.Click the data source name.Click Set properties Set properties icon.On the Connection tab, click Set for Open session commands.Enter the following text in the XML database commands box:<commandBlock> <commands> <sqlCommand> <sql>SET CURRENT QUERY ACCELERATION ALL</sql> </sqlCommand> </commands> </Block> It is recommended that you set the value to ALL, but you can set it to the value that is more appropriate for your environment. For more information, see the "Setting the query acceleration register from IBM Cognos BI" section in Optimizing DB2 Queries with IBM DB2 Analytics Accelerator for z/OS If this data source is shared among other Cognos applications, all queries would use IDAA. If data from those shared applications is not also loaded into IDAA, then those reports may not complete. If loading the data for the shared applications into IDAA is not appropriate for your environment, consider setting up a second data source.Click OK, and then click OK again.Enable IDAA in IBM SPSS Modeler:Catalog the IDAA data source. Edit the odbc.ini file to connect to the IDAA data source. Ensure that the data source name is the same as the database alias name that you used when you cataloged the database. For more information, see Configuring IBM SPSS Modeler Server with DB2 CLI/ODBC on Linux on System z operating systemsComplete this procedure to configure IBM SPSS Modeler Server with DB2 CLI/ODBC on Linux on System z operating systems..For more information, see Enabling Integration with IBM DB2 Analytics Accelerator for z/OS in IBM Knowledge Center (www.ibm.com/support/knowledgecenter/SS3RA7_17.0.0/clementine/dbmining_zdb2_enabling.html).Ensure that you modify the odbc-db2-accelerator-names.cfg file in the Enabling the integration of IBM® DB2 for z/OS® in IBM SPSS Modeler task (www.ibm.com/support/knowledgecenter/SS3RA7_17.0.0/clementine/dbmining_zdb2_enabling_helperapps.html).Restart SPSS Modeler.Use IBM DB2 Analytics Accelerator (IDAA) with IBM Capacity Management AnalyticsTo use IBM DB2 Analytics Accelerator (IDAA) with IBM Capacity Management Analytics, you can either configure ZPARMs in your DB2 configuration or enable IDAA in IBM Cognos Business Intelligence and IBM SPSS Modeler.

Configuring IBM Capacity Management Analytics to use IBM DB2 Analytics Accelerator

To use IBM® DB2® Analytics Accelerator (IDAA) with IBM Capacity Management Analytics, you can add a DB2 open session command block to the data source connection in IBM Cognos® Business Intelligence and enable IDAA in SPSS® Modeler.

Alternatively, you can use ZPARMs for IDAA. For more information, see Setting ZPARMs for IDAA in DB2 for z/OS.

Procedure

Enable IDAA in IBM Cognos Business Intelligence:

In IBM Cognos Connection, click Launch > IBM Cognos Administration.

On the Configuration tab, select Data Source Connections.

Click the data source name.

Click Set properties Set properties icon.

On the Connection tab, click Set for Open session commands.

Enter the following text in the XML database commands box:

<commandBlock>
  <commands>
    <sqlCommand>
      <sql>SET CURRENT QUERY ACCELERATION ALL</sql>
    </sqlCommand>
  </commands>
</Block>

It is recommended that you set the value to ALL, but you can set it to the value that is more appropriate for your environment. For more information, see the "Setting the query acceleration register from IBM Cognos BI" section in Optimizing DB2 Queries with IBM DB2 Analytics Accelerator for z/OS®

If this data source is shared among other Cognos applications, all queries would use IDAA. If data from those shared applications is not also loaded into IDAA, then those reports may not complete. If loading the data for the shared applications into IDAA is not appropriate for your environment, consider setting up a second data source.

Click OK, and then click OK again.

Enable IDAA in IBM SPSS Modeler:

Catalog the IDAA data source.

Edit the odbc.ini file to connect to the IDAA data source. Ensure that the data source name is the same as the database alias name that you used when you cataloged the database. For more information, see Configuring IBM SPSS Modeler Server with DB2 CLI/ODBC on Linux on System z operating systems.

For more information, see Enabling Integration with IBM DB2 Analytics Accelerator for z/OS in IBM Knowledge Center (www.ibm.com/support/knowledgecenter/SS3RA7_17.0.0/clementine/dbmining_zdb2_enabling.html).

Ensure that you modify the odbc-db2-accelerator-names.cfg file in the Enabling the integration of IBM® DB2 for z/OS® in IBM SPSS Modeler task (www.ibm.com/support/knowledgecenter/SS3RA7_17.0.0/clementine/dbmining_zdb2_enabling_helperapps.html).

Restart SPSS Modeler.

Parent topic: Use IBM DB2 Analytics Accelerator (IDAA) with IBM Capacity Management Analytics

Verify that IDAA is being used in IBM Capacity Management AnalyticsYou can verify that the query or stream was run with IDAA by using IBM Data Studio or IBM z/OS System Display and Search Facility (SDSF) on the LPAR where the database resides.Verify by using IBM Data Studio You can verify that a query or stream was run with IDAA from IBM Data Studio. From the Administration Explorer, expand the data source, and select Accelerators, and then click Manage. The executed queries or streams are displayed in the Query Monitoring pane.Verify by using SDSF If you use SDSF, you can run the following command to verify whether a Cognos query or an SPSS Modeler stream was run with IDAA: -DatabaseName DISPLAY THD(*) ACCEL(*) For a Cognos query, the expected result would be something like: DSNV401I -DatabaseName DISPLAY THREAD REPORT FOLLOWS - DSNV402I -DatabaseName ACTIVE THREADS - 697 NAME ST A REQ ID AUTHID PLAN ASID TOKEN SERVER AC * 23 db2jcc_appli COGADM DISTSERV 0071 4185 V437-WORKSTATION=name USERID=cogadm APPLICATION NAME=db2jcc_application V442-CRTKN=::IP_address.36684.CFD341BE1A54 V445-G91E8B0B.OF4C.CFD341BE1A54=4185 ACCESSING DATA FOR ::IP_address V444-G91E8B0B.OF4C.CFD341BE1A54=4185 ACCESSING DATA AT ZGRYPHO2-::IP_address..1400 DISPLAY ACTIVE REPORT COMPLETE For an SPSS stream, the expected result would be something like: -DatabaseName DISPLAY THD(*) ACCEL(*) DSNV401I -DatabaseName DISPLAY THREAD REPORT FOLLOWS - DSNV402I -DatabaseName ACTIVE THREADS - 606 NAME ST A REQ ID AUTHID PLAN ASID TOKEN SERVER RA * 230 modelerserve COGADM DISTSERV 0071 10446 V437-WORKSTATION=ADMINIB-7ILJPF1 USERID=cogadm APPLICATION NAME=modelerserver.exe V442-CRTKN=IP_address.61028.151111053635 V445-G9778346.EE64.151111053635=10446 ACCESSING DATA FOR ::IP_address V444-G9778346.EE64.151111053635=10446 ACCESSING DATA AT ZGRYPHO2-::IP_address..1400 DISPLAY ACTIVE REPORT COMPLETE DSN9022I -DatabaseName DSNVDT '-DISPLAY THD' NORMAL COMPLETIONUse IBM DB2 Analytics Accelerator (IDAA) with IBM Capacity Management AnalyticsTo use IBM DB2 Analytics Accelerator (IDAA) with IBM Capacity Management Analytics, you can either configure ZPARMs in your DB2 configuration or enable IDAA in IBM Cognos Business Intelligence and IBM SPSS Modeler.

Verify that IDAA is being used in IBM Capacity Management Analytics

You can verify that the query or stream was run with IDAA by using IBM® Data Studio or IBM z™/OS System Display and Search Facility (SDSF) on the LPAR where the database resides.

Verify by using IBM Data Studio

You can verify that a query or stream was run with IDAA from IBM Data Studio.

From the Administration Explorer, expand the data source, and select Accelerators, and then click Manage. The executed queries or streams are displayed in the Query Monitoring pane.

Verify by using SDSF

If you use SDSF, you can run the following command to verify whether a Cognos® query or an SPSS® Modeler stream was run with IDAA: -DatabaseName DISPLAY THD(*) ACCEL(*)

For a Cognos query, the expected result would be something like:

DSNV401I -DatabaseName DISPLAY THREAD REPORT FOLLOWS -
DSNV402I -DatabaseName ACTIVE THREADS - 697
NAME ST A REQ ID AUTHID PLAN ASID TOKEN
SERVER AC * 23 db2jcc_appli COGADM DISTSERV 0071 4185
V437-WORKSTATION=name
USERID=cogadm
APPLICATION NAME=db2jcc_application
V442-CRTKN=::IP_address.36684.CFD341BE1A54
V445-G91E8B0B.OF4C.CFD341BE1A54=4185 ACCESSING DATA FOR ::IP_address
V444-G91E8B0B.OF4C.CFD341BE1A54=4185 ACCESSING DATA AT ZGRYPHO2-::IP_address..1400
DISPLAY ACTIVE REPORT COMPLETE

For an SPSS stream, the expected result would be something like:

-DatabaseName DISPLAY THD(*) ACCEL(*)
DSNV401I  -DatabaseName DISPLAY THREAD REPORT FOLLOWS -
DSNV402I  -DatabaseName ACTIVE THREADS - 606
NAME     ST A   REQ ID           AUTHID   PLAN     ASID TOKEN
SERVER   RA *   230 modelerserve COGADM   DISTSERV 0071 10446
V437-WORKSTATION=ADMINIB-7ILJPF1
USERID=cogadm
APPLICATION NAME=modelerserver.exe
V442-CRTKN=IP_address.61028.151111053635
V445-G9778346.EE64.151111053635=10446 ACCESSING DATA FOR ::IP_address
V444-G9778346.EE64.151111053635=10446 ACCESSING DATA AT ZGRYPHO2-::IP_address..1400
DISPLAY ACTIVE REPORT COMPLETE
DSN9022I  -DatabaseName DSNVDT '-DISPLAY THD' NORMAL COMPLETION

Parent topic: Use IBM DB2 Analytics Accelerator (IDAA) with IBM Capacity Management Analytics

Configuring IBM SPSS Modeler Server with DB2 CLI/ODBC on Linux on System z operating systemsComplete this procedure to configure IBM SPSS Modeler Server with DB2 CLI/ODBC on Linux on System z operating systems.Ensure that the following prerequisites are met before proceeding.Modeler Server is installed on Linux on System zSDAP 7.1 is installed on Linux on System zDB2 client is installed on Linux on System z and the db2inst user existsEdit odbc.ini in the SDAP installation directory to include the following lines:[ODBC Data Sources] <DataSourceName>=IBM DB2 CLI ODBC [<DataSourceName>] Driver=/home/db2inst1/sqllib/lib/libdb2o.so Description=IBM DB2 CLI/ODBC Driver DriverUnicodeType=1For example:[ODBC Data Sources] STLAB71=IBM DB2 CLI ODBC [STLAB71] Driver=/home/db2inst1/sqllib/lib/libdb2o.so Description=IBM DB2 CLI/ODBC Driver DriverUnicodeType=1 Ensure that the data source name (DataSourceName in the example) does not contain spaces.Edit odbc.sh in the SDAP installation directory so that the LD_LIBRARY_PATH variable appears as follows:if [ "$LD_LIBRARY_PATH" = "" ]; then LD_LIBRARY_PATH=/opt/IBM/SPSS/SDAP71/lib:/home/db2inst1/sqllib/lib else LD_LIBRARY_PATH=/opt/IBM/SPSS/SDAP71/lib:$LD_LIBRARY_PATH:/home/db2inst1/sqllib/libIn the db2inst/sqllib/cfg directory, create or edit db2cli.ini to include following lines. [<data source name>] Hostname=<hostname> Port=8040 Protocol=TCPIP DriverUnicodeType=1 Database=DB1RFor example:[STLAB71] Hostname=myserver.mydomain.com Port=8040 Protocol=TCPIP DriverUnicodeType=1 Database=DB1RThe data source name should be the same as the name specified in the SDAP odbc.ini file.Add the following line to modelersrv.sh in the SPSS Modeler Server installation directory: . /opt/IBM/SPSS/SDAP71/odbc.shIn the SPSS Modeler Server <install_dir>/bin directory, link libspssodbc.so with libspssodbc_datadirect_utf16.so. rm -fr libspssodbc.so ln -s libspssodbc_datadirect_utf16.so libspssodbc.soRestart SPSS Modeler Server.Optional: Manual configuration tasksThe tasks in this chapter are run automatically by the IBM Capacity Management Analytics Solution Kit installation. Use these tasks only if you want to perform any of the configuration tasks manually.

Configuring IBM SPSS Modeler Server with DB2 CLI/ODBC on Linux on System z operating systems

Complete this procedure to configure IBM® SPSS® Modeler Server with DB2® CLI/ODBC on Linux on System z® operating systems.

Before you begin

Ensure that the following prerequisites are met before proceeding.

Modeler Server is installed on Linux on System z

SDAP 7.1 is installed on Linux on System z

DB2 client is installed on Linux on System z and the db2inst user exists

Procedure

Edit odbc.ini in the SDAP installation directory to include the following lines:

[ODBC Data Sources]
<DataSourceName>=IBM DB2 CLI ODBC

[<DataSourceName>]
Driver=/home/db2inst1/sqllib/lib/libdb2o.so
Description=IBM DB2 CLI/ODBC Driver
DriverUnicodeType=1

For example:

[ODBC Data Sources]
STLAB71=IBM DB2 CLI ODBC

[STLAB71]
Driver=/home/db2inst1/sqllib/lib/libdb2o.so
Description=IBM DB2 CLI/ODBC Driver
DriverUnicodeType=1

Note: Ensure that the data source name (DataSourceName in the example) does not contain spaces.

Edit odbc.sh in the SDAP installation directory so that the LD_LIBRARY_PATH variable appears as follows:

if [ "$LD_LIBRARY_PATH" = "" ]; then
LD_LIBRARY_PATH=/opt/IBM/SPSS/SDAP71/lib:/home/db2inst1/sqllib/lib
else
LD_LIBRARY_PATH=/opt/IBM/SPSS/SDAP71/lib:$LD_LIBRARY_PATH:/home/db2inst1/sqllib/lib

In the db2inst/sqllib/cfg directory, create or edit db2cli.ini to include following lines.

[<data source name>]
Hostname=<hostname>
Port=8040
Protocol=TCPIP
DriverUnicodeType=1
Database=DB1R

For example:

[STLAB71]
Hostname=myserver.mydomain.com
Port=8040
Protocol=TCPIP
DriverUnicodeType=1
Database=DB1R

Note: The data source name should be the same as the name specified in the SDAP odbc.ini file.

Add the following line to modelersrv.sh in the SPSS Modeler Server installation directory:

. /opt/IBM/SPSS/SDAP71/odbc.sh

In the SPSS Modeler Server <install_dir>/bin directory, link libspssodbc.so with libspssodbc_datadirect_utf16.so.

rm -fr libspssodbc.so
ln -s libspssodbc_datadirect_utf16.so libspssodbc.so

Restart SPSS Modeler Server.

Parent topic: Optional: Manual configuration tasks

Import Capacity Management Analytics content to IBM Cognos Business IntelligenceView IBM Capacity Management Analytics reports in the IBM Cognos Business Intelligence portal. The reports are imported to the portal from a deployment zip file that is included in the Capacity Management Analytics installation.Optional: Manual configuration tasksThe tasks in this chapter are run automatically by the IBM Capacity Management Analytics Solution Kit installation. Use these tasks only if you want to perform any of the configuration tasks manually.Ensure that images for the reports are displayed in IBM Cognos BISome configuration is required to ensure that the IBM Capacity Management Analytics images appear in the IBM Cognos Business Intelligence reports.Importing the IBM Cognos report deployment fileImport the IBM Capacity Management Analytics report deployment file into IBM Cognos Business Intelligence by using IBM Cognos Administration.Creating data source connections to the Capacity Management Analytics data storeYou must create a data source connection to the Capacity Management Analytics data store to be able to run the IBM Capacity Management Analytics reports.

Import Capacity Management Analytics content to IBM Cognos Business Intelligence

View IBM® Capacity Management Analytics reports in the IBM Cognos® Business Intelligence portal. The reports are imported to the portal from a deployment zip file that is included in the Capacity Management Analytics installation.

Ensure that images for the reports are displayed in IBM Cognos BI
Some configuration is required to ensure that the IBM Capacity Management Analytics images appear in the IBM Cognos Business Intelligence reports.

Importing the IBM Cognos report deployment file
Import the IBM Capacity Management Analytics report deployment file into IBM Cognos Business Intelligence by using IBM Cognos Administration.

Creating data source connections to the Capacity Management Analytics data store
You must create a data source connection to the Capacity Management Analytics data store to be able to run the IBM Capacity Management Analytics reports.

Parent topic: Optional: Manual configuration tasks

Ensure that images for the reports are displayed in IBM Cognos BISome configuration is required to ensure that the IBM Capacity Management Analytics images appear in the IBM Cognos Business Intelligence reports.Import Capacity Management Analytics content to IBM Cognos Business IntelligenceView IBM Capacity Management Analytics reports in the IBM Cognos Business Intelligence portal. The reports are imported to the portal from a deployment zip file that is included in the Capacity Management Analytics installation.Enabling the reports images for WebSphere Application ServerIf you use IBM Cognos BI for z/OS or Linux on System z, enable the IBM Capacity Management Analytics report images by deploying the cma_images.ear file to the IBM WebSphere Application Server.Deploying the report images to WebSphere Liberty or Apache Tomcat application serverYou can manually deploy the IBM Capacity Management Analytics report images to the WebSphere Liberty Profile or Apache Tomcat application server instance where IBM Cognos Business Intelligence is running.Enabling the report images for IBM HTTP Server or Apache server for Linux on System zOn IBM HTTP Server or Apache server, you must add directives to your server configuration file to display the images for the IBM Capacity Management Analytics reports.Enabling the report images for Report StudioTo view the IBM Capacity Management Analytics images when you author reports in Report Studio, you must enable WebDAV in IBM HTTP Server or Apache server.

Ensure that images for the reports are displayed in IBM Cognos BI

Some configuration is required to ensure that the IBM® Capacity Management Analytics images appear in the IBM Cognos® Business Intelligence reports.

Enabling the reports images for WebSphere Application Server
If you use IBM Cognos BI for z/OS® or Linux on System z®, enable the IBM Capacity Management Analytics report images by deploying the cma_images.ear file to the IBM WebSphere® Application Server.

Deploying the report images to WebSphere Liberty or Apache Tomcat application server
You can manually deploy the IBM Capacity Management Analytics report images to the WebSphere Liberty Profile or Apache Tomcat application server instance where IBM Cognos Business Intelligence is running.

Enabling the report images for IBM HTTP Server or Apache server for Linux on System z
On IBM HTTP Server or Apache server, you must add directives to your server configuration file to display the images for the IBM Capacity Management Analytics reports.

Enabling the report images for Report Studio
To view the IBM Capacity Management Analytics images when you author reports in Report Studio, you must enable WebDAV in IBM HTTP Server or Apache server.

Parent topic: Import Capacity Management Analytics content to IBM Cognos Business Intelligence

Enabling the reports images for WebSphere Application ServerIf you use IBM Cognos BI for z/OS or Linux on System z, enable the IBM Capacity Management Analytics report images by deploying the cma_images.ear file to the IBM WebSphere Application Server.You must copy the cma_images.ear file to a location where you can deploy the images to WebSphere Application Server. The cma_images.ear file is installed by the Capacity Management Analytics installer, and is in the C:\Program Files (x86)\IBM\Capacity_Management_Analytics_2.1.0\Images directory on the computer where you installed Capacity Management Analytics. If you are upgrading, you must uninstall the existing CMA_Images application, and then complete the following steps.In a web browser, enter the URL for WebSphere Application Server Integrated Solutions Console. For example, http://host_name:port/ibm/console. Where host_name is the name of the computer where WebSphere Application Server is installed and port is the WebSphere Application Server HTTP transport port number.On the Welcome page, click Application, New Application.Click New Enterprise Application, Click Local file system, then browse to C:\Program Files (x86)\IBM\Capacity_Management_Analytics_2.1.0\Images\cma_images.ear. Click Next.Click Fast Path, then click Next.Enter CMA_Images in the Application name box, then click Next.Click Next on the Map modules to servers screen.Click Next on the Metadata for modules screen.Review the Summary screen, then click Finish.Click Save when notified that the CMA_Images application was successfully installed.Click Applications, Application Types, WebSphere.enterprise applications.Select the CMA_Images application, then click Start.Ensure that images for the reports are displayed in IBM Cognos BISome configuration is required to ensure that the IBM Capacity Management Analytics images appear in the IBM Cognos Business Intelligence reports.

Enabling the reports images for WebSphere Application Server

If you use IBM® Cognos® BI for z/OS® or Linux on System z®, enable the IBM Capacity Management Analytics report images by deploying the cma_images.ear file to the IBM WebSphere® Application Server.

Before you begin

You must copy the cma_images.ear file to a location where you can deploy the images to WebSphere Application Server. The cma_images.ear file is installed by the Capacity Management Analytics installer, and is in the C:\Program Files (x86)\IBM\Capacity_Management_Analytics_2.1.0\Images directory on the computer where you installed Capacity Management Analytics.

If you are upgrading, you must uninstall the existing CMA_Images application, and then complete the following steps.

Procedure

In a web browser, enter the URL for WebSphere Application Server Integrated Solutions Console. For example, http://host_name:port/ibm/console. Where host_name is the name of the computer where WebSphere Application Server is installed and port is the WebSphere Application Server HTTP transport port number.

On the Welcome page, click Application, New Application.

Click New Enterprise Application,

Click Local file system, then browse to C:\Program Files (x86)\IBM\Capacity_Management_Analytics_2.1.0\Images\cma_images.ear.

Click Next.

Click Fast Path, then click Next.

Enter CMA_Images in the Application name box, then click Next.

Click Next on the Map modules to servers screen.

Click Next on the Metadata for modules screen.

Review the Summary screen, then click Finish.

Click Save when notified that the CMA_Images application was successfully installed.

Click Applications, Application Types, WebSphere.enterprise applications.

Select the CMA_Images application, then click Start.

Parent topic: Ensure that images for the reports are displayed in IBM Cognos BI

Deploying the report images to WebSphere Liberty or Apache Tomcat application serverYou can manually deploy the IBM Capacity Management Analytics report images to the WebSphere Liberty Profile or Apache Tomcat application server instance where IBM Cognos Business Intelligence is running.Go to the CMA_ROOT\Images directory.For example, on a Microsoft Windows operating system, go to the C:\Program Files (x86)\IBM\Capacity_Management_Analytics_2.1.0\Images directory.Do one of the following steps:If you are using WebSphere Liberty, copy the cma.war file to the c10_location/wlpdropins directory.If you are using Apache Tomcat, copy the cma.war file to the c10_location/webapps directory.Restart the WebSphere Liberty Profile or Apache Tomcat server instance.Ensure that images for the reports are displayed in IBM Cognos BISome configuration is required to ensure that the IBM Capacity Management Analytics images appear in the IBM Cognos Business Intelligence reports.

Deploying the report images to WebSphere Liberty or Apache Tomcat application server

You can manually deploy the IBM® Capacity Management Analytics report images to the WebSphere® Liberty Profile or Apache Tomcat application server instance where IBM Cognos® Business Intelligence is running.

Procedure

Go to the CMA_ROOT\Images directory. For example, on a Microsoft Windows operating system, go to the C:\Program Files (x86)\IBM\Capacity_Management_Analytics_2.1.0\Images directory.

Do one of the following steps:

If you are using WebSphere Liberty, copy the cma.war file to the c10_location/wlpdropins directory.

If you are using Apache Tomcat, copy the cma.war file to the c10_location/webapps directory.

Restart the WebSphere Liberty Profile or Apache Tomcat server instance.

Parent topic: Ensure that images for the reports are displayed in IBM Cognos BI

Enabling the report images for IBM HTTP Server or Apache server for Linux on System zOn IBM HTTP Server or Apache server, you must add directives to your server configuration file to display the images for the IBM Capacity Management Analytics reports.Copy the report images to a location on your web server computer.The report images are installed by the Capacity Management Analytics installer, and are in the C:\Program Files (x86)\IBM\Capacity_Management_Analytics_2.1.0\Images directory on the computer where you installed Capacity Management Analytics. Copy the images to /opt/IBM/Capacity_Management_Analytics_2.1.0/Images on your Linux on System z operating system.In the webserver_location/conf directory, open the httpd.conf file in a text editor.Create an alias that is named cma that points to the directory where you have the Capacity Management Analytics report images.For example, add or modify the following information:Alias /cma "/opt/IBM/Capacity_Management_Analytics_2.1.0" <Directory "/opt/IBM/Capacity_Management_Analytics_2.1.0"> Options Indexes FollowSymLinks MultiViews AllowOverride None Order allow,deny Allow from all </Directory> Save the file.Restart your web server.The Capacity Management Analytics images are displayed when the user runs a report. Ensure that images for the reports are displayed in IBM Cognos BISome configuration is required to ensure that the IBM Capacity Management Analytics images appear in the IBM Cognos Business Intelligence reports.

Enabling the report images for IBM HTTP Server or Apache server for Linux on System z

On IBM® HTTP Server or Apache server, you must add directives to your server configuration file to display the images for the IBM Capacity Management Analytics reports.

Procedure

Copy the report images to a location on your web server computer.

The report images are installed by the Capacity Management Analytics installer, and are in the C:\Program Files (x86)\IBM\Capacity_Management_Analytics_2.1.0\Images directory on the computer where you installed Capacity Management Analytics.

Copy the images to /opt/IBM/Capacity_Management_Analytics_2.1.0/Images on your Linux on System z® operating system.

In the webserver_location/conf directory, open the httpd.conf file in a text editor.

Create an alias that is named cma that points to the directory where you have the Capacity Management Analytics report images. For example, add or modify the following information:

Alias /cma "/opt/IBM/Capacity_Management_Analytics_2.1.0"
<Directory "/opt/IBM/Capacity_Management_Analytics_2.1.0">
Options Indexes FollowSymLinks MultiViews
AllowOverride None
Order allow,deny
Allow from all
</Directory>

Save the file.

Restart your web server.

Results

The Capacity Management Analytics images are displayed when the user runs a report.

Parent topic: Ensure that images for the reports are displayed in IBM Cognos BI

Enabling the report images for Report StudioTo view the IBM Capacity Management Analytics images when you author reports in Report Studio, you must enable WebDAV in IBM HTTP Server or Apache server.Copy the report images to a location on your web server computer.The report images are installed by the Capacity Management Analytics installer, and are in the C:\Program Files (x86)\IBM\Capacity_Management_Analytics_2.1.0\Images directory on the computer where you installed Capacity Management Analytics. Copy the images to /opt/IBM/Capacity_Management_Analytics_2.1.0/Images on your Linux on System z operating system.In the webserver_location/conf directory, open the httpd.conf file in a text editor.Uncomment the directives that load the modules/mod_dav.so and modules/mod_dav_fs.so components.LoadModule dav_module modules/mod_dav.so LoadModule dav_fs_module modules/mod_dav_fs.soProvide a location for the DAVLockDB directive.For example,DAVLockDB "webserver_location/var/DavLock"Ensure that the directory exists.Set a time-out for WebDAV.For example, DAVMinTimeout 600Create an alias for the directory where your images are stored.For example, create an alias that is named cma that points to the directory where you have the Capacity Management Analytics report images. On Linux on System z, that directory might be /opt/IBM/Capacity_Management_Analytics_2.1.0/Images.Add or edit the Dav On information for the alias.For example, add or modify the following information:Alias /cma "/opt/IBM/Capacity_Management_Analytics_2.1.0" <Directory "/opt/IBM/Capacity_Management_Analytics_2.1.0"> Dav On Options Indexes FollowSymLinks MultiViews AllowOverride None Order allow,deny Allow from all </Directory>Save the file.Restart your web server.With WebDAV enabled, the Capacity Management Analytics images are displayed in Report Studio. Ensure that images for the reports are displayed in IBM Cognos BISome configuration is required to ensure that the IBM Capacity Management Analytics images appear in the IBM Cognos Business Intelligence reports.

Enabling the report images for Report Studio

To view the IBM® Capacity Management Analytics images when you author reports in Report Studio, you must enable WebDAV in IBM HTTP Server or Apache server.

Procedure

Copy the report images to a location on your web server computer.

The report images are installed by the Capacity Management Analytics installer, and are in the C:\Program Files (x86)\IBM\Capacity_Management_Analytics_2.1.0\Images directory on the computer where you installed Capacity Management Analytics.

Copy the images to /opt/IBM/Capacity_Management_Analytics_2.1.0/Images on your Linux on System z® operating system.

In the webserver_location/conf directory, open the httpd.conf file in a text editor.

Uncomment the directives that load the modules/mod_dav.so and modules/mod_dav_fs.so components.

LoadModule dav_module modules/mod_dav.so
LoadModule dav_fs_module modules/mod_dav_fs.so

Provide a location for the DAVLockDB directive.

For example,

DAVLockDB "webserver_location/var/DavLock"

Ensure that the directory exists.

Set a time-out for WebDAV.

For example,

DAVMinTimeout 600

Create an alias for the directory where your images are stored. For example, create an alias that is named cma that points to the directory where you have the Capacity Management Analytics report images. On Linux on System z, that directory might be /opt/IBM/Capacity_Management_Analytics_2.1.0/Images.

Add or edit the Dav On information for the alias.

For example, add or modify the following information:

Alias /cma "/opt/IBM/Capacity_Management_Analytics_2.1.0"
<Directory "/opt/IBM/Capacity_Management_Analytics_2.1.0">
Dav On
Options Indexes FollowSymLinks MultiViews
AllowOverride None
Order allow,deny
Allow from all
</Directory>

Save the file.

Restart your web server.

Results

With WebDAV enabled, the Capacity Management Analytics images are displayed in Report Studio.

Parent topic: Ensure that images for the reports are displayed in IBM Cognos BI

Importing the IBM Cognos report deployment fileImport the IBM Capacity Management Analytics report deployment file into IBM Cognos Business Intelligence by using IBM Cognos Administration.If you ran the installer on a Microsoft Windows operating system, ensure that you upload the contents of the C:\Program Files (x86)\IBM\Capacity_Management_Analytics_2.1.0 directory on the computer where you installed Capacity Management Analytics to the computer where the IBM Cognos Business Intelligence is installed. If you already have IBM Capacity Management Analytics installed and you have customized reports or created your own reports, you must back up your reports to another location before you delete the existing Solution Kit content. For more information about deleting the solution kit content, see Uninstalling the Capacity Management Analytics Solution Kit contentYou can delete the solution kit report content by deleting the IBM Capacity Management Analytics Solution Kit folder from the Public folders tab in IBM Cognos Connection, and then deleting the artifacts from IBM Cognos Administration..On the computer where the IBM Cognos Content Manager component is installed, copy the cma-2.1.zip file from the Capacity Management Analytics reports directory to the c10_location\deployment directory.If you are not using the default deployment directory, ensure that you copy the cma-2.1.zip file to the appropriate directory.Open a web browser.Enter the URL for the IBM Cognos Business Intelligence portal, and press Enter.For example, enterhttp://host_name/ibmcognos/Or, if you are using IBM Cognos BI on z/OS, enterhttp://host_name:port/p2pd/servlet/dispatch/extWhere host_name is the name of the computer where IBM Cognos BI is installed. For IBM Cognos BI on z/OS, port is the WebSphere Application Server HTTP transport port number.On the Welcome page, click Administer IBM Cognos content. Or click Launch > IBM Cognos Administration on IBM Cognos content.On the Configuration tab, click Content Administration.On the toolbar, click the New Import icon New Import icon.On the Select a deployment archive - New Import wizard, click Next.In the Deployment Archive pane, select cma-2.1 file, then click Next.On the Specify a name and description - New Import wizard, enter a Name, then click Next.In the Public folders, directory, and library content section, select IBM Capacity Management Analytics Solution Kit, and click Next.On the Specify the general options pane, select whether to include access permissions and references to external namespaces, and who owns the entries after they are imported, and click Next. The summary information is displayed. Review the summary information, and click Next.Click Save and run once, then click Finish.On the Run with options-deployment page, specify the time and date for the run, or select NOW if you want to run immediately. Click Upgrade all report specifications to the latest version. Click Do not assign new IDs during import, then click Run.To ensure that the workspaces work correctly, you must select Do not assign new IDs during import when you import the content.Review the run time, click View the details of this import after closing this dialog, then click OK.In the View run history details - deployment pane, click Refresh and check import messages in the View an import deployment record - deployment pane. A message such as CM-REQ-2300 Import is complete indicates that the import action is successful.Click Close.The deployed content is available in Public Folders.Click LaunchIBM Cognos Connection. The Launch icon is in the upper right of the outer frame.Import Capacity Management Analytics content to IBM Cognos Business IntelligenceView IBM Capacity Management Analytics reports in the IBM Cognos Business Intelligence portal. The reports are imported to the portal from a deployment zip file that is included in the Capacity Management Analytics installation.

Importing the IBM Cognos report deployment file

Import the IBM® Capacity Management Analytics report deployment file into IBM Cognos® Business Intelligence by using IBM Cognos Administration.

Before you begin

If you ran the installer on a Microsoft Windows operating system, ensure that you upload the contents of the C:\Program Files (x86)\IBM\Capacity_Management_Analytics_2.1.0 directory on the computer where you installed Capacity Management Analytics to the computer where the IBM Cognos Business Intelligence is installed.

Important: If you already have IBM Capacity Management Analytics installed and you have customized reports or created your own reports, you must back up your reports to another location before you delete the existing Solution Kit content.

For more information about deleting the solution kit content, see Uninstalling the Capacity Management Analytics Solution Kit content.

Procedure

On the computer where the IBM Cognos Content Manager component is installed, copy the cma-2.1.zip file from the Capacity Management Analytics reports directory to the c10_location\deployment directory.

Note: If you are not using the default deployment directory, ensure that you copy the cma-2.1.zip file to the appropriate directory.

Open a web browser.

Enter the URL for the IBM Cognos Business Intelligence portal, and press Enter.

For example, enter

http://host_name/ibmcognos/

Or, if you are using IBM Cognos BI on z/OS®, enter

http://host_name:port/p2pd/servlet/dispatch/ext

Where host_name is the name of the computer where IBM Cognos BI is installed. For IBM Cognos BI on z/OS, port is the WebSphere® Application Server HTTP transport port number.

On the Welcome page, click Administer IBM Cognos content. Or click Launch > IBM Cognos Administration on IBM Cognos content.

On the Configuration tab, click Content Administration.

On the toolbar, click the New Import icon New Import icon.

On the Select a deployment archive - New Import wizard, click Next.

In the Deployment Archive pane, select cma-2.1 file, then click Next.

On the Specify a name and description - New Import wizard, enter a Name, then click Next.

In the Public folders, directory, and library content section, select IBM Capacity Management Analytics Solution Kit, and click Next.

On the Specify the general options pane, select whether to include access permissions and references to external namespaces, and who owns the entries after they are imported, and click Next.

The summary information is displayed.

Review the summary information, and click Next.

Click Save and run once, then click Finish.

On the Run with options-deployment page, specify the time and date for the run, or select NOW if you want to run immediately.

Click Upgrade all report specifications to the latest version.

Click Do not assign new IDs during import, then click Run.

Important: To ensure that the workspaces work correctly, you must select Do not assign new IDs during import when you import the content.

Review the run time, click View the details of this import after closing this dialog, then click OK.

In the View run history details - deployment pane, click Refresh and check import messages in the View an import deployment record - deployment pane. A message such as CM-REQ-2300 Import is complete indicates that the import action is successful.

Click Close.

The deployed content is available in Public Folders.

Click Launch > IBM Cognos Connection. The Launch icon is in the upper right of the outer frame.

Parent topic: Import Capacity Management Analytics content to IBM Cognos Business Intelligence

Creating data source connections to the Capacity Management Analytics data store You must create a data source connection to the Capacity Management Analytics data store to be able to run the IBM Capacity Management Analytics reports.If you are performing a new installation of Capacity Management Analytics 2.1 or you are migrating from Capacity Management Analytics 1.2 Fix Pack 1 and the data configuration differs from the base installation (for example, a different schema name), then you must complete this procedure. If you are migrating from Capacity Management Analytics 1.2 Fix Pack 1 and the data configuration does not differ from the base installation, then you can skip this procedure.Open a web browser.Enter the URL for the IBM Cognos Business Intelligence portal, and press Enter.For example, enterhttp://host_name/ibmcognos/Or, if you are using IBM Cognos BI on z/OS, enterhttp://host_name:port/p2pd/servlet/dispatch/extWhere host_name is the name of the computer where IBM Cognos BI is installed. For IBM Cognos BI on z/OS, port is the WebSphere Application Server HTTP transport port number.On the Welcome page, click Administer IBM Cognos content. Or click LaunchIBM Cognos Administration.On the Configuration tab, and click Data Source Connections.On the toolbar, click New Data Source.In the Name box, type CMADW, and click Next.On the Specify the connection page, do the following steps:In Type, select IBM DB2.In Isolation level, select Specify a value, and choose Read uncommitted.Select Configure JDBC connection.Click Next.In the DB2 database name field, enter the name of your Capacity Management Analytics data store.Under Signons, select both the Password and Create a signon that the Everyone group can use check boxes, type the user ID and password for the data store, and then click Finish.For example, enter hcmadm for the user ID and hcmadm for the password. To test whether the parameters are correct, click Test the connection. On the Specify the IBM DB2 (JDBC) connection string panel, enter the name of the computer where your Capacity Management Analytics data store database is in the Server name box.In the Port number box, enter the port number for the database server.In the Database name box, enter the name of the database.To test whether the parameters are correct, click Test the connection.Click Finish.Import Capacity Management Analytics content to IBM Cognos Business IntelligenceView IBM Capacity Management Analytics reports in the IBM Cognos Business Intelligence portal. The reports are imported to the portal from a deployment zip file that is included in the Capacity Management Analytics installation.

Creating data source connections to the Capacity Management Analytics data store

You must create a data source connection to the Capacity Management Analytics data store to be able to run the IBM® Capacity Management Analytics reports.

About this task

If you are performing a new installation of Capacity Management Analytics 2.1 or you are migrating from Capacity Management Analytics 1.2 Fix Pack 1 and the data configuration differs from the base installation (for example, a different schema name), then you must complete this procedure.

If you are migrating from Capacity Management Analytics 1.2 Fix Pack 1 and the data configuration does not differ from the base installation, then you can skip this procedure.

Procedure

Open a web browser.

Enter the URL for the IBM Cognos® Business Intelligence portal, and press Enter.

For example, enter

http://host_name/ibmcognos/

Or, if you are using IBM Cognos BI on z/OS®, enter

http://host_name:port/p2pd/servlet/dispatch/ext

Where host_name is the name of the computer where IBM Cognos BI is installed. For IBM Cognos BI on z/OS, port is the WebSphere® Application Server HTTP transport port number.

On the Welcome page, click Administer IBM Cognos content. Or click Launch > IBM Cognos Administration.

On the Configuration tab, and click Data Source Connections.

On the toolbar, click New Data Source.

In the Name box, type CMADW, and click Next.

On the Specify the connection page, do the following steps:

In Type, select IBM DB2.

In Isolation level, select Specify a value, and choose Read uncommitted.

Select Configure JDBC connection.

Click Next.

In the DB2 database name field, enter the name of your Capacity Management Analytics data store.

Under Signons, select both the Password and Create a signon that the Everyone group can use check boxes, type the user ID and password for the data store, and then click Finish. For example, enter hcmadm for the user ID and hcmadm for the password.

Tip: To test whether the parameters are correct, click Test the connection.

On the Specify the IBM DB2 (JDBC) connection string panel, enter the name of the computer where your Capacity Management Analytics data store database is in the Server name box.

In the Port number box, enter the port number for the database server.

In the Database name box, enter the name of the database.

Tip: To test whether the parameters are correct, click Test the connection.

Click Finish.

Parent topic: Import Capacity Management Analytics content to IBM Cognos Business Intelligence

Calibrating streams and models for new hardware generationsIf you are using IBM Capacity Management Analytics with new generations of hardware, you must ensure that the Capacity Management Analytics assets (the streams and models) are calibrated correctly.To calibrate the assets, you must generate new models by rerunning all of the IBM SPSS predictive streams, such as the forecasting and anomaly detection streams.Special consideration must be taken if you are using Capacity Management Analytics on IBM z13 due to the multithreading capabilities within specialty processors.You must rerun the following MIPS forecasting streams: mvspm-lpar-hourly-forecast-timeseries.strmvspm-lpar-daily-forecast-timeseries.strmvspm-lpar-monthly-forecast-timeseries.strYou must rerun the following anomaly forecasting streams: anomaly_detect_building_c.stranomaly_detect_building_r.stranomaly_classification.stranomaly_detect_scoring.strOptional: Manual configuration tasksThe tasks in this chapter are run automatically by the IBM Capacity Management Analytics Solution Kit installation. Use these tasks only if you want to perform any of the configuration tasks manually.

Calibrating streams and models for new hardware generations

If you are using IBM® Capacity Management Analytics with new generations of hardware, you must ensure that the Capacity Management Analytics assets (the streams and models) are calibrated correctly.

To calibrate the assets, you must generate new models by rerunning all of the IBM SPSS® predictive streams, such as the forecasting and anomaly detection streams.

Special consideration must be taken if you are using Capacity Management Analytics on IBM z13 due to the multithreading capabilities within specialty processors.

You must rerun the following MIPS forecasting streams:

mvspm-lpar-hourly-forecast-timeseries.str

mvspm-lpar-daily-forecast-timeseries.str

mvspm-lpar-monthly-forecast-timeseries.str

You must rerun the following anomaly forecasting streams:

anomaly_detect_building_c.str

anomaly_detect_building_r.str

anomaly_classification.str

anomaly_detect_scoring.str

Parent topic: Optional: Manual configuration tasks

Stop and start solution software servicesIBM Capacity Management Analytics is an integrated solution that includes many products. If you must stop the services, you must do so in the correct order. The product services must also be started in the correct order.Stop solution servicesYou must stop the IBM Capacity Management Analytics node services in a specific order.Start solution servicesYou must start the IBM Capacity Management Analytics node services in a specific order.

Stop and start solution software services

IBM® Capacity Management Analytics is an integrated solution that includes many products. If you must stop the services, you must do so in the correct order. The product services must also be started in the correct order.

Stop solution services
You must stop the IBM Capacity Management Analytics node services in a specific order.

Start solution services
You must start the IBM Capacity Management Analytics node services in a specific order.

Stop solution servicesYou must stop the IBM Capacity Management Analytics node services in a specific order.Stop the node services in the following order:Cognos AnalyticsSPSS Modeler ServerDB2 Enterprise ServerStop and start solution software servicesIBM Capacity Management Analytics is an integrated solution that includes many products. If you must stop the services, you must do so in the correct order. The product services must also be started in the correct order.Stopping services on IBM Cognos AnalyticsTo stop solution services, you must begin with stopping services on IBM Cognos Analytics.Stopping services on the SPSS Modeler ServerYou must stop the IBM SPSS services on the IBM Capacity Management Analytics node computer.Stopping services on the IBM DB2 Enterprise ServerYou must stop the IBM DB2 instance on the IBM Capacity Management Analytics node computer.

Stop solution services

You must stop the IBM® Capacity Management Analytics node services in a specific order.

Stop the node services in the following order:

Cognos Analytics

SPSS Modeler Server

DB2 Enterprise Server

Stopping services on IBM Cognos Analytics
To stop solution services, you must begin with stopping services on IBM Cognos Analytics.

Stopping services on the SPSS Modeler Server
You must stop the IBM SPSS services on the IBM Capacity Management Analytics node computer.

Stopping services on the IBM DB2 Enterprise Server
You must stop the IBM DB2 instance on the IBM Capacity Management Analytics node computer.

Parent topic: Stop and start solution software services

Stopping services on IBM Cognos AnalyticsTo stop solution services, you must begin with stopping services on IBM Cognos Analytics.You must stop the IBM Cognos Business Intelligence services and IBM HTTP Server on the IBM Capacity Management Analytics node computer.On Linux operating systems, log in to the node computer as the administrator or as a user with sudo or root.Go to /opt/ibm/cognos/analytics/bin64 and run ./cogconfig.sh -stop.Stop solution servicesYou must stop the IBM Capacity Management Analytics node services in a specific order.

Stopping services on IBM Cognos Analytics

To stop solution services, you must begin with stopping services on IBM Cognos Analytics.

About this task

You must stop the IBM Cognos Business Intelligence services and IBM HTTP Server on the IBM® Capacity Management Analytics node computer.

Procedure

On Linux operating systems, log in to the node computer as the administrator or as a user with sudo or root.

Go to /opt/ibm/cognos/analytics/bin64 and run ./cogconfig.sh -stop.

Parent topic: Stop solution services

Stopping services on the SPSS Modeler ServerYou must stop the IBM SPSS services on the IBM Capacity Management Analytics node computer.On Linux operating systems, log in to the node computer as the administrator or as a user with sudo or root.Go to the IBM SPSS Modeler Server directory. For instance, go to /opt/ibm/SPSS/ModelerServer/17.1. If you manually installed and accepted the defaults, the path is /usr/ibm/SPSS/ModelerServer/17.1.Enter the following command: ./modelersrv.sh stop.To verify whether any services are still running, enter the following command: ./modelersrv.sh listTo stop any services that are still running, enter the following command: kill -9 'cat modelersrv.pid'. Stop solution servicesYou must stop the IBM Capacity Management Analytics node services in a specific order.

Stopping services on the SPSS Modeler Server

You must stop the IBM SPSS services on the IBM® Capacity Management Analytics node computer.

About this task

Procedure

On Linux operating systems, log in to the node computer as the administrator or as a user with sudo or root.

Go to the IBM SPSS Modeler Server directory. For instance, go to /opt/ibm/SPSS/ModelerServer/17.1. If you manually installed and accepted the defaults, the path is /usr/ibm/SPSS/ModelerServer/17.1.

Enter the following command: ./modelersrv.sh stop.

To verify whether any services are still running, enter the following command: ./modelersrv.sh list

To stop any services that are still running, enter the following command: kill -9 'cat modelersrv.pid'.

Parent topic: Stop solution services

Stopping services on the IBM DB2 Enterprise ServerYou must stop the IBM DB2 instance on the IBM Capacity Management Analytics node computer.On Linux operating systems, log in to the node computer as the administrator or as a user with sudo or root.In a terminal window, type the following command to change the DB2 instance owner: su - db2inst1Enter the following command to stop the DB2 administration server: db2stop.Stop solution servicesYou must stop the IBM Capacity Management Analytics node services in a specific order.

Stopping services on the IBM DB2 Enterprise Server

You must stop the IBM DB2 instance on the IBM® Capacity Management Analytics node computer.

Procedure

On Linux operating systems, log in to the node computer as the administrator or as a user with sudo or root.

In a terminal window, type the following command to change the DB2 instance owner: su - db2inst1

Enter the following command to stop the DB2 administration server: db2stop.

Parent topic: Stop solution services

Start solution servicesYou must start the IBM Capacity Management Analytics node services in a specific order.Start the node services in the following order:DB2 Enterprise ServerCognos AnalyticsSPSS Modeler ServerStop and start solution software servicesIBM Capacity Management Analytics is an integrated solution that includes many products. If you must stop the services, you must do so in the correct order. The product services must also be started in the correct order.Starting services on the IBM DB2 Enterprise ServerYou must start the IBM DB2 instance on the IBM Capacity Management Analytics node computer.Starting services on IBM Cognos AnalyticsYou must start the IBM Cognos Analytics services on the IBM Capacity Management Analytics node computer.Starting services on the IBM SPSS Modeler ServerYou must start the IBM SPSS services on the IBM Capacity Management Analytics node computer.

Start solution services

You must start the IBM® Capacity Management Analytics node services in a specific order.

Start the node services in the following order:

DB2 Enterprise Server

Cognos Analytics

SPSS Modeler Server

Starting services on the IBM DB2 Enterprise Server
You must start the IBM DB2 instance on the IBM Capacity Management Analytics node computer.

Starting services on IBM Cognos Analytics
You must start the IBM Cognos® Analytics services on the IBM Capacity Management Analytics node computer.

Starting services on the IBM SPSS Modeler Server
You must start the IBM SPSS services on the IBM Capacity Management Analytics node computer.

Parent topic: Stop and start solution software services

Starting services on the IBM DB2 Enterprise ServerYou must start the IBM DB2 instance on the IBM Capacity Management Analytics node computer.On Linux operating systems, log in to the node computer as the administrator or as a user with sudo or root.In a terminal window, type the following command to change the DB2 instance owner: su - db2inst1.Enter the following command to start the DB2 administration server: db2start.Start solution servicesYou must start the IBM Capacity Management Analytics node services in a specific order.

Starting services on the IBM DB2 Enterprise Server

You must start the IBM DB2 instance on the IBM® Capacity Management Analytics node computer.

Procedure

On Linux operating systems, log in to the node computer as the administrator or as a user with sudo or root.

In a terminal window, type the following command to change the DB2 instance owner: su - db2inst1.

Enter the following command to start the DB2 administration server: db2start.

Parent topic: Start solution services

Starting services on IBM Cognos Analytics You must start the IBM Cognos Analytics services on the IBM Capacity Management Analytics node computer.On Linux operating systems, or if you installed IBM Cognos Business Intelligence to WebSphere Application Server on Microsoft Windows operating systems, you start the IBM Cognos Business Intelligence services by starting the WebSphere Application Server profile where IBM Cognos Business Intelligence is running.On Linux operating systems, log in to the node computer as the administrator or as a user with sudo or root.Go to /opt/ibm/cognos/analytics/bin64 and run ./cogconfig.sh start.Start solution servicesYou must start the IBM Capacity Management Analytics node services in a specific order.

Starting services on IBM Cognos Analytics

You must start the IBM® Cognos® Analytics services on the IBM Capacity Management Analytics node computer.

About this task

On Linux operating systems, or if you installed IBM Cognos Business Intelligence to WebSphere® Application Server on Microsoft Windows operating systems, you start the IBM Cognos Business Intelligence services by starting the WebSphere Application Server profile where IBM Cognos Business Intelligence is running.

Procedure

On Linux operating systems, log in to the node computer as the administrator or as a user with sudo or root.

Go to /opt/ibm/cognos/analytics/bin64 and run ./cogconfig.sh start.

Parent topic: Start solution services

Starting services on the IBM SPSS Modeler ServerYou must start the IBM SPSS services on the IBM Capacity Management Analytics node computer.On Linux operating systems, log in to the node computer as the administrator or as a user with sudo or root.Go to the IBM SPSS Modeler Server directory. For example, go to /opt/ibm/SPSS/ModelerServer/17.1. If you manually installed and accepted the defaults, the path is /usr/ ibm /SPSS/ModelerServer/17.1.Enter the following command: ./modelersrv.sh start.Start solution servicesYou must start the IBM Capacity Management Analytics node services in a specific order.

Starting services on the IBM SPSS Modeler Server

You must start the IBM SPSS services on the IBM® Capacity Management Analytics node computer.

Procedure

On Linux operating systems, log in to the node computer as the administrator or as a user with sudo or root.

Go to the IBM SPSS Modeler Server directory. For example, go to /opt/ibm/SPSS/ModelerServer/17.1. If you manually installed and accepted the defaults, the path is /usr/ ibm /SPSS/ModelerServer/17.1.

Enter the following command: ./modelersrv.sh start.

Parent topic: Start solution services

Capacity Management Analytics and the IBM Sub-Capacity Reporting Tool (SCRT)The IBM Sub-Capacity Reporting Tool (SCRT) is a no-charge tool that IBM policy requires to be used to report license capacity for sub-capacity eligible products that run on the z/OS system. If you purchased Capacity Management Analytics on z/OS, which is a sub-capacity eligible product, you will be required to submit the SCRT reports to IBM.For information about SCRT, see the IBM System z Software Pricing web site.At startup, all Capacity Management Analytics on z/OS included products that run on z/OS must register themselves for sub-capacity pricing with the Capacity Management Analytics on z/OS product identifier (PID) and related information. System Management Facility (SMF) 89 records are written as part of the sub-capacity registration process. When the SCRT is run, the SMF 89 records are processed and IBM Capacity Management Analytics on z/OS is recognized as being installed and is eligible for sub-capacity pricing.Each included product provides the support to properly register as Capacity Management Analytics on z/OS by the following APARs:PM94322 for Cognos Business Intelligence for z/OSPM94316 for SPSS Modeler with Scoring Adapter for zEnterprisePM94238 for IBM Tivoli Decision Support for z/OSBefore you continue, you must install the products and apply the APARs as soon as they become available. Otherwise, the included product's own PIDs shows up in the SCRT report instead of IBM Capacity Management Analytics on z/OS. If that happens and the APARs are not yet available, you must add an Exclude control statement for it when you run SCRT. Registering IBM Cognos BI for z/OSYou must copy the IBM Capacity Management Analytics on z/OS registration file to you IBM Cognos Business Intelligence for z/OS installation.Registering IBM SPSS Modeler with Scoring Adapter for zEnterpriseYou must copy the IBM Capacity Management Analytics on z/OS registration file to your IBM SPSS Modeler with Scoring Adapter for zEnterprise installation.Registering IBM Tivoli Decision Support for z/OSYou must copy the IBM Capacity Management Analytics on z/OS registration file to your IBM Tivoli Decision Support for z/OS installation.Verifying that products are registered correctlyYou can use the JCL sample script HCMPROD to verify the registration of IBM Capacity Management Analytics on z/OS. The sample script is in the CMA_ROOT/scripts directory. On z/OS systems, the sample file is found in the samples directory in the SMP/E installation folder.

Capacity Management Analytics and the IBM Sub-Capacity Reporting Tool (SCRT)

The IBM® Sub-Capacity Reporting Tool (SCRT) is a no-charge tool that IBM policy requires to be used to report license capacity for sub-capacity eligible products that run on the z/OS® system. If you purchased Capacity Management Analytics on z/OS, which is a sub-capacity eligible product, you will be required to submit the SCRT reports to IBM.

For information about SCRT, see the IBM System z® Software Pricing web site.

At startup, all Capacity Management Analytics on z/OS included products that run on z/OS must register themselves for sub-capacity pricing with the Capacity Management Analytics on z/OS product identifier (PID) and related information. System Management Facility (SMF) 89 records are written as part of the sub-capacity registration process. When the SCRT is run, the SMF 89 records are processed and IBM Capacity Management Analytics on z/OS is recognized as being installed and is eligible for sub-capacity pricing.

Each included product provides the support to properly register as Capacity Management Analytics on z/OS by the following APARs:

PM94322 for Cognos® Business Intelligence for z/OS

PM94316 for SPSS® Modeler with Scoring Adapter for zEnterprise®

PM94238 for IBM Tivoli® Decision Support for z/OS

Before you continue, you must install the products and apply the APARs as soon as they become available. Otherwise, the included product's own PIDs shows up in the SCRT report instead of IBM Capacity Management Analytics on z/OS. If that happens and the APARs are not yet available, you must add an Exclude control statement for it when you run SCRT.

Registering IBM Cognos BI for z/OS
You must copy the IBM Capacity Management Analytics on z/OS registration file to you IBM Cognos Business Intelligence for z/OS installation.

Registering IBM SPSS Modeler with Scoring Adapter for zEnterprise
You must copy the IBM Capacity Management Analytics on z/OS registration file to your IBM SPSS Modeler with Scoring Adapter for zEnterprise installation.

Registering IBM Tivoli Decision Support for z/OS
You must copy the IBM Capacity Management Analytics on z/OS registration file to your IBM Tivoli Decision Support for z/OS installation.

Verifying that products are registered correctly
You can use the JCL sample script HCMPROD to verify the registration of IBM Capacity Management Analytics on z/OS. The sample script is in the CMA_ROOT/scripts directory. On z/OS systems, the sample file is found in the samples directory in the SMP/E installation folder.

Registering IBM Cognos BI for z/OSYou must copy the IBM Capacity Management Analytics on z/OS registration file to you IBM Cognos Business Intelligence for z/OS installation.The registration information file is provided with the IBM Capacity Management Analytics Solution Kit. The file is in the CMA_ROOT\configuration directory.On the computer where you installed the Capacity Management Analytics Solution Kit, go to the CMA_ROOT\configuration directory.Use FTP or another means to copy CMApid.registrar as a binary file to the z/OS system where IBM Cognos BI is running.Copying CMApid.registrar as a binary file avoids conversion to EBCDIC.Do one of the following steps:If IBM Cognos is running on WebSphere Application Server for z/OS, copy CMApid.registrar to the WAS_instance/AppServer/properties/version directory.If IBM Cognos is running on Tomcat, copy CMApid.registrar to the c10_location/configuration directory.Set the environment variable CGC_SCRT_REGISTRATION_FILE to be the fully qualified path and file name of the registration file for the user ID in which IBM Cognos is running. This is typically done in the .profile for that user.For example: export CGC_SCRT_REGISTRATION_FILE=$WAS_instance/AppServer/properties/version/CMApid.registrarIf IBM Cognos is running on WebSphere Application Server, you must also set the environment variable in the administrative console in Application Serversserver_nameProcess DefinitionEnvironment Entries.Restart your IBM Cognos BI services.Capacity Management Analytics and the IBM Sub-Capacity Reporting Tool (SCRT)The IBM Sub-Capacity Reporting Tool (SCRT) is a no-charge tool that IBM policy requires to be used to report license capacity for sub-capacity eligible products that run on the z/OS system. If you purchased Capacity Management Analytics on z/OS, which is a sub-capacity eligible product, you will be required to submit the SCRT reports to IBM.

Registering IBM Cognos BI for z/OS

You must copy the IBM® Capacity Management Analytics on z/OS® registration file to you IBM Cognos® Business Intelligence for z/OS installation.

The registration information file is provided with the IBM Capacity Management Analytics Solution Kit. The file is in the CMA_ROOT\configuration directory.

Procedure

On the computer where you installed the Capacity Management Analytics Solution Kit, go to the CMA_ROOT\configuration directory.

Use FTP or another means to copy CMApid.registrar as a binary file to the z/OS system where IBM Cognos BI is running.

Important: Copying CMApid.registrar as a binary file avoids conversion to EBCDIC.

Do one of the following steps:

If IBM Cognos is running on WebSphere® Application Server for z/OS, copy CMApid.registrar to the WAS_instance/AppServer/properties/version directory.

If IBM Cognos is running on Tomcat, copy CMApid.registrar to the c10_location/configuration directory.

Set the environment variable CGC_SCRT_REGISTRATION_FILE to be the fully qualified path and file name of the registration file for the user ID in which IBM Cognos is running. This is typically done in the .profile for that user. For example: export CGC_SCRT_REGISTRATION_FILE=$WAS_instance/AppServer/properties/version/CMApid.registrar

If IBM Cognos is running on WebSphere Application Server, you must also set the environment variable in the administrative console in Application Servers > server_name > Process Definition > Environment Entries.

Restart your IBM Cognos BI services.

Parent topic: Capacity Management Analytics and the IBM Sub-Capacity Reporting Tool (SCRT)

Registering IBM SPSS Modeler with Scoring Adapter for zEnterpriseYou must copy the IBM Capacity Management Analytics on z/OS registration file to your IBM SPSS Modeler with Scoring Adapter for zEnterprise installation.The registration information file is provided with the IBM Capacity Management Analytics Solution Kit. The file is in the CMA_ROOT\configuration directory.On the computer where you installed the Capacity Management Analytics Solution Kit, go to the CMA_ROOT\configuration directory.Use FTP or another means to copy CMApid.registrar as a binary file to the z/OS system where IBM SPSS Modeler with Scoring Adapter for zEnterprise is running.Copying CMApid.registrar as a binary file avoids conversion to EBCDIC.Copy CMApid.registrar to the directory where the scoring adapter is installed. The directory must also be defined in the LIBPATH environment variable in the IBM DB2 User Defined Function for the scoring adapter.The default path is /usr/lpp/spss/cfscoring_16.0.0.Rename CMApid.registrar to pid.registrar.Restart the Workload Manager Application Environment in which the scoring adapter is running by using a QUIESCE and START command.Capacity Management Analytics and the IBM Sub-Capacity Reporting Tool (SCRT)The IBM Sub-Capacity Reporting Tool (SCRT) is a no-charge tool that IBM policy requires to be used to report license capacity for sub-capacity eligible products that run on the z/OS system. If you purchased Capacity Management Analytics on z/OS, which is a sub-capacity eligible product, you will be required to submit the SCRT reports to IBM.

Registering IBM SPSS Modeler with Scoring Adapter for zEnterprise

You must copy the IBM® Capacity Management Analytics on z/OS® registration file to your IBM SPSS® Modeler with Scoring Adapter for zEnterprise® installation.

The registration information file is provided with the IBM Capacity Management Analytics Solution Kit. The file is in the CMA_ROOT\configuration directory.

Procedure

On the computer where you installed the Capacity Management Analytics Solution Kit, go to the CMA_ROOT\configuration directory.

Use FTP or another means to copy CMApid.registrar as a binary file to the z/OS system where IBM SPSS Modeler with Scoring Adapter for zEnterprise is running.

Important: Copying CMApid.registrar as a binary file avoids conversion to EBCDIC.

Copy CMApid.registrar to the directory where the scoring adapter is installed. The directory must also be defined in the LIBPATH environment variable in the IBM DB2® User Defined Function for the scoring adapter.

The default path is /usr/lpp/spss/cfscoring_16.0.0.

Rename CMApid.registrar to pid.registrar.

Restart the Workload Manager Application Environment in which the scoring adapter is running by using a QUIESCE and START command.

Parent topic: Capacity Management Analytics and the IBM Sub-Capacity Reporting Tool (SCRT)

Registering IBM Tivoli Decision Support for z/OSYou must copy the IBM Capacity Management Analytics on z/OS registration file to your IBM Tivoli Decision Support for z/OS installation.The registration information file is provided with the Capacity Management Analytics Solution Kit. The file is in the CMA_ROOT\configuration directory.A sample JCL job named DRLJCCMA is provided with Tivoli Decision Support for z/OS in APAR PM94238. The sample creates the registration load module for Tivoli Decision Support for z/OS from the Capacity Management Analytics supplied file containing the registration information.On the computer where you installed the Capacity Management Analytics Solution Kit, go to the CMA_ROOT\configuration directory.Use FTP or another means to copy CMApid.registrar to the z/OS system where IBM Tivoli Decision Support for z/OS is running.CMApid.registrar must be copied as EBCDIC for IBM Tivoli Decision Support for z/OS.Copy CMApid.registrar to a data set of your choice.This should be a z/OS sequential data set or PDS member with RECFM=FB and LRECL=80.Edit the JCL in DRLJCCMA in the SDRLCNTL data set.Details are provided in the comments in the JCL header. Ensure that the DRLPIN DD name of the READ step refers to the EBCDIC copy of CMApid.registrar.Run the JCL.The JCL reads the IBM Capacity Management Analytics on z/OS registration file and builds a registration load module in the Tivoli Decision Support for z/OS load library SDRLLOAD.Run Tivoli Decision Support for z/OS.Capacity Management Analytics and the IBM Sub-Capacity Reporting Tool (SCRT)The IBM Sub-Capacity Reporting Tool (SCRT) is a no-charge tool that IBM policy requires to be used to report license capacity for sub-capacity eligible products that run on the z/OS system. If you purchased Capacity Management Analytics on z/OS, which is a sub-capacity eligible product, you will be required to submit the SCRT reports to IBM.

Registering IBM Tivoli Decision Support for z/OS

You must copy the IBM® Capacity Management Analytics on z/OS® registration file to your IBM Tivoli® Decision Support for z/OS installation.

The registration information file is provided with the Capacity Management Analytics Solution Kit. The file is in the CMA_ROOT\configuration directory.

A sample JCL job named DRLJCCMA is provided with Tivoli Decision Support for z/OS in APAR PM94238. The sample creates the registration load module for Tivoli Decision Support for z/OS from the Capacity Management Analytics supplied file containing the registration information.

Procedure

On the computer where you installed the Capacity Management Analytics Solution Kit, go to the CMA_ROOT\configuration directory.

Use FTP or another means to copy CMApid.registrar to the z/OS system where IBM Tivoli Decision Support for z/OS is running.

Important: CMApid.registrar must be copied as EBCDIC for IBM Tivoli Decision Support for z/OS.

Copy CMApid.registrar to a data set of your choice.

This should be a z/OS sequential data set or PDS member with RECFM=FB and LRECL=80.

Edit the JCL in DRLJCCMA in the SDRLCNTL data set.

Details are provided in the comments in the JCL header. Ensure that the DRLPIN DD name of the READ step refers to the EBCDIC copy of CMApid.registrar.

Run the JCL.

The JCL reads the IBM Capacity Management Analytics on z/OS registration file and builds a registration load module in the Tivoli Decision Support for z/OS load library SDRLLOAD.

Run Tivoli Decision Support for z/OS.

Parent topic: Capacity Management Analytics and the IBM Sub-Capacity Reporting Tool (SCRT)

Verifying that products are registered correctlyYou can use the JCL sample script HCMPROD to verify the registration of IBM Capacity Management Analytics on z/OS. The sample script is in the CMA_ROOT/scripts directory. On z/OS systems, the sample file is found in the samples directory in the SMP/E installation folder.HCMPRODsample filesHCMPRODVerify that IBM Capacity Management Analytics components are running. You can verify that the registration was applied correctly only if the products are running.If you installed on a Microsoft Windows or a Linux on System z operating system, upload the HCMPROD script to the z/OS machine, and copy into one PDS dataset with RECFM=FB and LRECL=80. Change #SMFDS# to the SMF record dataset. Submit this job and check the SYSPRINT of the joblog in the SDSF panel.The results show IBM CMA with each product or feature. Totals 445 0 46 290 -Product Product Product Product Product Owner Number Name Version Function --------- -------- ---------------- -------- -------- IBM CORP 5694-A01 z/OS 01.13.00 z/OS IBM CORP 5694-A01 z/OS 01.13.00 JES2 IBM CORP 5694-A01 z/OS . . RACF IBM CORP 5694-A01 z/OS . . RMF IBM CORP 5694-A01 z/OS . . Security Server IBM CORP 5694-A01 z/OS . . SDSF IBM CORP 5694-A01 z/OS . . TCP/IP BASE IBM CORP 5645-001 z/OS 01.13.00 TSO/E IBM CORP 5655-Y26 Cognos BI 10.2.1 NOTUSAGE COGNOS IBM CORP 5655-Y26 Cognos BI 10.2.1 NOTUSAGE USINGWAS IBM CORP 5698-AA6 IBM CMA 2.1 NOTUSAGE COGNOS IBM CORP 5698-AA6 IBM CMA 2.1 NOTUSAGE USINGWAS IBM CORP 5655-W65 WAS FOR Z/OS NOTUSAGE WAS Z/OSCapacity Management Analytics and the IBM Sub-Capacity Reporting Tool (SCRT)The IBM Sub-Capacity Reporting Tool (SCRT) is a no-charge tool that IBM policy requires to be used to report license capacity for sub-capacity eligible products that run on the z/OS system. If you purchased Capacity Management Analytics on z/OS, which is a sub-capacity eligible product, you will be required to submit the SCRT reports to IBM.

Verifying that products are registered correctly

You can use the JCL sample script HCMPROD to verify the registration of IBM® Capacity Management Analytics on z/OS®. The sample script is in the CMA_ROOT/scripts directory. On z/OS systems, the sample file is found in the samples directory in the SMP/E installation folder.

Before you begin

Verify that IBM Capacity Management Analytics components are running. You can verify that the registration was applied correctly only if the products are running.

Procedure

If you installed on a Microsoft Windows or a Linux on System z® operating system, upload the HCMPROD script to the z/OS machine, and copy into one PDS dataset with RECFM=FB and LRECL=80.

Change #SMFDS# to the SMF record dataset.

Submit this job and check the SYSPRINT of the joblog in the SDSF panel.

The results show IBM CMA with each product or feature.

 Totals                               445       0    46     290
-Product       Product     Product             Product     Product
 Owner         Number      Name                Version     Function
 ---------     --------    ----------------    --------    --------
 IBM CORP      5694-A01    z/OS                01.13.00    z/OS
 IBM CORP      5694-A01    z/OS                01.13.00    JES2
 IBM CORP      5694-A01    z/OS                .  .        RACF
 IBM CORP      5694-A01    z/OS                .  .        RMF
 IBM CORP      5694-A01    z/OS                .  .        Security Server
 IBM CORP      5694-A01    z/OS                .  .        SDSF
 IBM CORP      5694-A01    z/OS                .  .        TCP/IP BASE
 IBM CORP      5645-001    z/OS                01.13.00    TSO/E
 IBM CORP      5655-Y26    Cognos BI 10.2.1    NOTUSAGE    COGNOS
 IBM CORP      5655-Y26    Cognos BI 10.2.1    NOTUSAGE    USINGWAS
 IBM CORP      5698-AA6    IBM CMA 2.1         NOTUSAGE    COGNOS
 IBM CORP      5698-AA6    IBM CMA 2.1         NOTUSAGE    USINGWAS
 IBM CORP      5655-W65    WAS FOR Z/OS        NOTUSAGE    WAS Z/OS

Parent topic: Capacity Management Analytics and the IBM Sub-Capacity Reporting Tool (SCRT)

Security considerations for IBM Capacity Management AnalyticsThis section provides information about the users and roles that are used in IBM Capacity Management Analytics and about password usage in configuration and script files.usersrolessecurityusers and rolesUsers and rolesMultiple roles are required to install, configure, and use IBM Capacity Management Analytics. This section provides a brief introduction to these roles or users.Password handling in IBM Capacity Management AnalyticsIBM Capacity Management Analytics requires multiple user passwords during installation and daily usage. These passwords include an IBM Cognos Business Intelligence account password, a WebSphere Application Server administration password, a database administration password, a database user password, and an SPSS Modeler Server password.

Security considerations for IBM Capacity Management Analytics

This section provides information about the users and roles that are used in IBM® Capacity Management Analytics and about password usage in configuration and script files.

Users and roles
Multiple roles are required to install, configure, and use IBM Capacity Management Analytics. This section provides a brief introduction to these roles or users.

Password handling in IBM Capacity Management Analytics
IBM Capacity Management Analytics requires multiple user passwords during installation and daily usage. These passwords include an IBM Cognos® Business Intelligence account password, a WebSphere® Application Server administration password, a database administration password, a database user password, and an SPSS® Modeler Server password.

Users and rolesMultiple roles are required to install, configure, and use IBM Capacity Management Analytics. This section provides a brief introduction to these roles or users.usersrolessecurityusers and rolesTivoli Decision Support for z/OS database user To initialize the IBM DB2 database when you install Tivoli Decision Support for z/OS, you must be a user that has DB2 SYSADM authority or a user that has the authority to create plans, storage groups, and databases, and who has access to the DB2 catalog. For more information about the security and permission requirements, see the Tivoli Decision Support for z/OS documentation (www.ibm.com/support/knowledgecenter/SSH53X_1.8.2/com.ibm.tivoli.dszos.doc.1.8.2/Admin/asecure.html). For more information about initializing the database, see the Tivoli Decision Support for z/OS documentation (www.ibm.com/support/knowledgecenter/SSH53X_1.8.2/com.ibm.tivoli.dszos.doc.1.8.2/Admin/aininor.html).Capacity Management Analytics database user The Capacity Management Analytics data in the database is protected by DB2. Administrators and users must be granted DB2 privileges to be able to access the data. The Capacity Management Analytics database administrator should have DB2 SYSADM authority (or be a user with the authority to create plans, storage groups, and databases, and have the authority to grant create tablespace, table, view, UDF, storage procedure, and index privileges to other users. The Capacity Management Analytics database user should have createdba, createalias, bindadd, accessctrl, and dataaccess privileges. The user also needs packadm on all collections. The user should have full access to the HCM database (the Capacity Management Analytics database) and DRL database (the Tivoli Decision Support for z/OS database).SPSS Modeler Server user IBM SPSS Server normal need to be installed/start/stop by root user, but SPSS Modeler does provide no-root start/stop functions. For more information, see the SPSS Modeler documentation (www.ibm.com/support/knowledgecenter/SS3RA7_17.0.0/clementine/server/adminguidesource/non_root_unix_container.dita). When you use SPSS Modeler Server with IBM Capacity Management Analytics, the user must have read and write access to the folder, data files, temporary files, and log files that are used in the Capacity Management Analytics streams.IBM Cognos Business Intelligence user For more information about user account information for IBM Cognos Business Intelligence for z/OS, see the User accounts for the installation and running the product in IBM Knowledge Center (www.ibm.com/support/knowledgecenter/SSEP7J_10.2.2/com.ibm.swg.ba.cognos.c8bizos_ig.10.2.2.doc/c_cbizos_prepcrteusrgrp.html). For more information about user account information for IBM Cognos Business Intelligence on distributed platforms, see Configure a User Account or Network Service Account for IBM Cognos Business Intelligence in IBM Knowledge Center (www.ibm.com/support/knowledgecenter/SSEP7J_10.2.2/com.ibm.swg.ba.cognos.inst_cr_winux.10.2.2.doc/c_configureauseraccountforcognos8.html%23ConfigureAUserAccountForCognos8).Security considerations for IBM Capacity Management AnalyticsThis section provides information about the users and roles that are used in IBM Capacity Management Analytics and about password usage in configuration and script files.

Users and roles

Multiple roles are required to install, configure, and use IBM® Capacity Management Analytics. This section provides a brief introduction to these roles or users.

Tivoli Decision Support for z/OS database user

To initialize the IBM DB2® database when you install Tivoli® Decision Support for z/OS®, you must be a user that has DB2 SYSADM authority or a user that has the authority to create plans, storage groups, and databases, and who has access to the DB2 catalog.

For more information about the security and permission requirements, see the Tivoli Decision Support for z/OS documentation (www.ibm.com/support/knowledgecenter/SSH53X_1.8.2/com.ibm.tivoli.dszos.doc.1.8.2/Admin/asecure.html).

For more information about initializing the database, see the Tivoli Decision Support for z/OS documentation (www.ibm.com/support/knowledgecenter/SSH53X_1.8.2/com.ibm.tivoli.dszos.doc.1.8.2/Admin/aininor.html).

Capacity Management Analytics database user

The Capacity Management Analytics data in the database is protected by DB2. Administrators and users must be granted DB2 privileges to be able to access the data.

The Capacity Management Analytics database administrator should have DB2 SYSADM authority (or be a user with the authority to create plans, storage groups, and databases, and have the authority to grant create tablespace, table, view, UDF, storage procedure, and index privileges to other users.

The Capacity Management Analytics database user should have createdba, createalias, bindadd, accessctrl, and dataaccess privileges. The user also needs packadm on all collections. The user should have full access to the HCM database (the Capacity Management Analytics database) and DRL database (the Tivoli Decision Support for z/OS database).

SPSS Modeler Server user

IBM SPSS® Server normal need to be installed/start/stop by root user, but SPSS Modeler does provide no-root start/stop functions.

For more information, see the SPSS Modeler documentation (www.ibm.com/support/knowledgecenter/SS3RA7_17.0.0/clementine/server/adminguidesource/non_root_unix_container.dita).

When you use SPSS Modeler Server with IBM Capacity Management Analytics, the user must have read and write access to the folder, data files, temporary files, and log files that are used in the Capacity Management Analytics streams.

IBM Cognos Business Intelligence user

For more information about user account information for IBM Cognos® Business Intelligence for z/OS, see the User accounts for the installation and running the product in IBM Knowledge Center (www.ibm.com/support/knowledgecenter/SSEP7J_10.2.2/com.ibm.swg.ba.cognos.c8bizos_ig.10.2.2.doc/c_cbizos_prepcrteusrgrp.html).

For more information about user account information for IBM Cognos Business Intelligence on distributed platforms, see Configure a User Account or Network Service Account for IBM Cognos Business Intelligence in IBM Knowledge Center (www.ibm.com/support/knowledgecenter/SSEP7J_10.2.2/com.ibm.swg.ba.cognos.inst_cr_winux.10.2.2.doc/c_configureauseraccountforcognos8.html%23ConfigureAUserAccountForCognos8).

Parent topic: Security considerations for IBM Capacity Management Analytics

Password handling in IBM Capacity Management AnalyticsIBM Capacity Management Analytics requires multiple user passwords during installation and daily usage. These passwords include an IBM Cognos Business Intelligence account password, a WebSphere Application Server administration password, a database administration password, a database user password, and an SPSS Modeler Server password.passwordsDuring the installation of IBM Capacity Management Analytics During the IBM Capacity Management Analytics installation, you are asked to enter all required passwords. After the installation, the passwords are encrypted and stored in the CMA_ROOT/bin/setenv.sh file. You can change the passwords by editing the setenv.sh file. If you must change passwords in the setenv.sh file, you must ensure that the passwords are encrypted. All passwords are encrypted by using the AES 256 encryption algorithm. You can use cmaencrypt.jar to encrypt a password. For example, you can use the following command to encrypt a password: java –jar cmaencrypt.jar yourpassword. Standard Java does not support 256-bit encryption. You must replace the security policy file in your Java lib directory before you use cmaencrypt.jar.While configuring IBM Capacity Management Analytics When you configure IBM Capacity Management Analytics, the script files use the password from the setenv.sh file and the passwords are decrypted when you are using them. The decryption program does not expose the password to the user. Also, all Capacity Management Analytics SQL and configuration step execution is initialized by a Java program to prevent passwords from being exposed during execution.While using IBM Capacity Management Analytics When you run the Capacity Management Analytics streams, the SPSS Modeler Server password is read from the setenv.sh file. The password is decrypted in a Java program and used to connect to SPSS Modeler Server. The SPSS Modeler streams require input stream parameters, including database passwords. SPSS Modeler provides a password encoding function in SPSS Modeler Client. You can use the encoding function to encode your database password and then enter the encrypted password in the parameter.txt file. For more information about encoding passwords in SPSS Modeler, see the SPSS Modeler documentation (www.ibm.com/support/knowledgecenter/SS3RA7_17.0.0/clementine/epassword_tool.dita)Security considerations for IBM Capacity Management AnalyticsThis section provides information about the users and roles that are used in IBM Capacity Management Analytics and about password usage in configuration and script files.

Password handling in IBM Capacity Management Analytics

IBM® Capacity Management Analytics requires multiple user passwords during installation and daily usage. These passwords include an IBM Cognos® Business Intelligence account password, a WebSphere® Application Server administration password, a database administration password, a database user password, and an SPSS® Modeler Server password.

During the installation of IBM Capacity Management Analytics

During the IBM Capacity Management Analytics installation, you are asked to enter all required passwords. After the installation, the passwords are encrypted and stored in the CMA_ROOT/bin/setenv.sh file. You can change the passwords by editing the setenv.sh file.

Note: If you must change passwords in the setenv.sh file, you must ensure that the passwords are encrypted.

All passwords are encrypted by using the AES 256 encryption algorithm. You can use cmaencrypt.jar to encrypt a password. For example, you can use the following command to encrypt a password: java –jar cmaencrypt.jar yourpassword.

Standard Java™ does not support 256-bit encryption. You must replace the security policy file in your Java lib directory before you use cmaencrypt.jar.

While configuring IBM Capacity Management Analytics

When you configure IBM Capacity Management Analytics, the script files use the password from the setenv.sh file and the passwords are decrypted when you are using them. The decryption program does not expose the password to the user. Also, all Capacity Management Analytics SQL and configuration step execution is initialized by a Java program to prevent passwords from being exposed during execution.

While using IBM Capacity Management Analytics

When you run the Capacity Management Analytics streams, the SPSS Modeler Server password is read from the setenv.sh file. The password is decrypted in a Java program and used to connect to SPSS Modeler Server.

The SPSS Modeler streams require input stream parameters, including database passwords. SPSS Modeler provides a password encoding function in SPSS Modeler Client. You can use the encoding function to encode your database password and then enter the encrypted password in the parameter.txt file.

For more information about encoding passwords in SPSS Modeler, see the SPSS Modeler documentation (www.ibm.com/support/knowledgecenter/SS3RA7_17.0.0/clementine/epassword_tool.dita)

Parent topic: Security considerations for IBM Capacity Management Analytics

Error, warning, and installation messagesError, warning, and installation messages are displayed for IBM Capacity Management Analytics.Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.Reporting error and warning messagesError or warning messages are displayed in reports and workspaces. In reports, the message summary is displayed in the right top of the subtitle bar. In workspaces, the message summary is displayed at the end of the global prompts area on the left. The message summary is in red text. You can click the message to show the details.Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.Installation error, information, and warning messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

Error, warning, and installation messages

Error, warning, and installation messages are displayed for IBM® Capacity Management Analytics.

Modeling error and warning messages
Running the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

Reporting error and warning messages
Error or warning messages are displayed in reports and workspaces. In reports, the message summary is displayed in the right top of the subtitle bar. In workspaces, the message summary is displayed at the end of the global prompts area on the left. The message summary is in red text. You can click the message to show the details.

Optimization error and warning messages
The following error or warnings may occur for the LPAR weight optimization.

Installation error, information, and warning messages
When installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.Log file names are formed by the stream name and a time stamp. For example, a prod_bill_msu_optimization.str log would be named prod_bill_msu_optimization_3629024907.log.If a stream failed to run, error or warning messages are written to the log file.Error, warning, and installation messagesError, warning, and installation messages are displayed for IBM Capacity Management Analytics.CMA-ME001UID and/or PWD for database is incorrect.CMA-ME002No database connection has been selected.CMA-ME003CMDW (CMA database connection) is incorrect.CMA-ME004Excel input file type is incorrect.CMA-ME005<transaction> transaction doesn't have enough data for model building.CMA-ME006<transaction> transaction has exceed the data limitation for model building, which is SIZE. Please sample your data before you build the model.CMA-ME007<transaction> transaction incorrect data, which is response time is less than CPU second. Please check your input data.CMA-ME008Do not have permission to read/write to <file> or this file path is incorrect.CMA-ME009No <parameter> specified!CMA-ME010CMA Schema or TDSz Schema is incorrect, or related table did not exist in database.CMA-ME011Product ID <product> on LPAR <lpar> and CPC Series NO <cpc_series_no> can't fit a model, please inspect your data.CMA-ME012The storage of certain fields or the number of total fields does not comply to the expected value as defined in the source node.CMA-ME013The total capacity of the LPARs is not enough. No optimization suggestion could be given and the optimization-related reports could not be generated!CMA-ME014No capacity in each LPARs before arrangement, please check your data or configuration.CMA-ME015No suitable LPAR in the data!CMA-ME016No product needs to be optimized!CMA-ME017The product <product> is cared but not suitable for any LPAR.CMA-ME018Invalid condition meet, optimization stopped.CMA-ME019Do not have permission to read/write to the path or path is incorrect. Log <file> cannot be created!CMA-ME020CPU and response scoring results didn't have intersection data for classify results.CMA-ME021Didn't find any CPU and response scoring results in database, please run CPU and response scoring before execute classification stream.CMA-ME022Didn't find any response scoring results in database, please run response scoring before execute classification stream.CMA-ME023Didn't find any cpu scoring results in Database, please run cpu scoring before execute classification stream.CMA-ME024Can not access the database because password for database is expired.CMA-ME025Unidentified error.CMA-ME026<transaction> transaction doesn't have enough data for model scoring.CMA-ME027Wrong value for USERSELECTION specified! Input either 'define' or 'weight'.CMA-ME028User does not have enough privilege. Please access the database with high privilege account.CMA-ME029Wrong value for <parameter> specified! Input either 'true' or 'false'.CMA-ME030start_date is incorrectCMA-ME031end_date is incorrectCMA-ME032Record record is not valid, following case may meet both report class and job name filled with *, both of them have specified value, capture ratio is greater than 1.CMA-ME033<job_name> has duplicate records.CMA-ME034Report class <class_name> has duplicate records.CMA-ME035Server <Server> has duplicate records.CMA-ME036Record<record> is invalid, no record existed in TDSz tables.CMA-ME037Application app_name for environment environment function function on CPC Series NO cpc_series_no MVS_SYSTEM_ID mvs_system_id cannot fit a model. Please check your data.CMA-ME038Application app_name for environment environment function function on CPC Series NO cpc_series_no MVS_SYSTEM_ID mvs_system_id is missing values. Please check your data.CMA-ME039Parameter HOUR_START and HOUR_END must be in range [0, 23], HOUR_START must not larger than HOUR_END.CMA-ME040DATE_START must be earlier than DATE_END.CMA-ME041Wrong PEAKRANK input format. Valid format is 'level1,level2,...,leveln' and 0<level<'<hour>'.CMA-ME042Wildcard character in Job_name <JOB_NAME> is incorrect, please check input application mapping file.CMA-ME043No needed data in the table <Schema.TableName> from date_start to date_end.CMA-ME044No application data to process.CMA-ME045No Transaction specified in the anomaly input file.CMA-ME046The start date specified in the file is greater than the end date.CMA-ME047TIME_START should be earlier than TIME_END.CMA-ME048The score ratio is greater than 1. The score ratio should be greater than 0 but less than or equal to 1.CMA-ME049There were no tree-based scoring results in the database. Run the tree-based scoring before you execute the classification system.CMA-MW001Product ID <product> on LPAR <lpar> and CPC Series NO <cpc_series_no> has been obsoleted from <date>, it will be skipped in forecast sequence.CMA-MW002Product ID <product> on LPAR <lpar> and CPC Series NO <cpc_series_no> on <weekday> contained null value and does not have 24 hours data in one day, it cannot be forecasted.CMA-MW003Product ID <product> on LPAR <lpar> and CPC Series NO <cpc_series_no> on <weekday> does not have 24 hours data in one day, it cannot be forecasted.CMA-MW004Product ID <product> on LPAR <lpar> and CPC Series NO <cpc_series_no> on <weekday> contained a null value, it cannot be forecasted.CMA-MW005Product ID <product> on LPAR <lpar> and CPC Series NO <cpc_series_no> contained bad data, stream automatically fix it and forecast.CMA-MW006The record count of LPAR <lpar> on processor type <processor type> and CPC Series NO <CPU series no> is less than the limitation of the algorithm, do not forecast.CMA-MW007LPAR <lpar> on processor type <processor type> and CPC Series NO <cpc_series_no> have incorrect scoring result, the predict can't be processed.CMA-MW008Product ID <product> cannot be put in LPAR <lpar> due to no information, forbidden in setting, or have no overlapped duration, ignored!CMA-MW009The total capacity of the LPAR <lpar> is increased <xxx> percent to host the remaining of the Product ID <product>.CMA-MW010Do not have permission to save models to model path or model path is incorrect.CMA-MW011LPAR <lpar> is invalid and ignored!CMA-MW012The total capacity of the LPARs is not enough for Product ID <product>, the optimization is now trying to increase the capacity of certain LPAR.CMA-MW013Failed to reading cma_message.csv, detailed CMA information cannot be provided.CMA-MW014Job xxx and service xxx have intersections.CMA-MW015The stream used actual history data from <DATE_MIN> to <DATE_MAX>.CMA-MW016The input start date <input_data_start> is earlier than the actual observed start date <observ_date_start>. The later date was used.CMA-MW017The input end date <input_data_end> is later than the actual observed end date <observ_date_end>. The later date was used.CMA-MW018The selected period contains incorrect capture ratio for the job. The capture ratio must be less than or equal to 1.CMA-MW019The selected period contains incorrect capture ratio for the report class. The capture ratio must be less than or equal to 1.CMA-MW020The mapping table does not contain any records and no data is exported to the mapping table. Run appl_lob_z.str to export the data.

Modeling error and warning messages

Running the IBM® Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

Log file names are formed by the stream name and a time stamp. For example, a prod_bill_msu_optimization.str log would be named prod_bill_msu_optimization_3629024907.log.

If a stream failed to run, error or warning messages are written to the log file.

CMA-ME001
UID and/or PWD for database is incorrect.

CMA-ME002
No database connection has been selected.

CMA-ME003
CMDW (CMA database connection) is incorrect.

CMA-ME004
Excel input file type is incorrect.

CMA-ME005
<transaction> transaction doesn't have enough data for model building.

CMA-ME006
<transaction> transaction has exceed the data limitation for model building, which is SIZE. Please sample your data before you build the model.

CMA-ME007
<transaction> transaction incorrect data, which is response time is less than CPU second. Please check your input data.

CMA-ME008
Do not have permission to read/write to <file> or this file path is incorrect.

CMA-ME009
No <parameter> specified!

CMA-ME010
CMA Schema or TDSz Schema is incorrect, or related table did not exist in database.

CMA-ME011
Product ID <product> on LPAR <lpar> and CPC Series NO <cpc_series_no> can't fit a model, please inspect your data.

CMA-ME012
The storage of certain fields or the number of total fields does not comply to the expected value as defined in the source node.

CMA-ME013
The total capacity of the LPARs is not enough. No optimization suggestion could be given and the optimization-related reports could not be generated!

CMA-ME014
No capacity in each LPARs before arrangement, please check your data or configuration.

CMA-ME015
No suitable LPAR in the data!

CMA-ME016
No product needs to be optimized!

CMA-ME017
The product <product> is cared but not suitable for any LPAR.

CMA-ME018
Invalid condition meet, optimization stopped.

CMA-ME019
Do not have permission to read/write to the path or path is incorrect. Log <file> cannot be created!

CMA-ME020
CPU and response scoring results didn't have intersection data for classify results.

CMA-ME021
Didn't find any CPU and response scoring results in database, please run CPU and response scoring before execute classification stream.

CMA-ME022
Didn't find any response scoring results in database, please run response scoring before execute classification stream.

CMA-ME023
Didn't find any cpu scoring results in Database, please run cpu scoring before execute classification stream.

CMA-ME024
Can not access the database because password for database is expired.

CMA-ME025
Unidentified error.

CMA-ME026
<transaction> transaction doesn't have enough data for model scoring.

CMA-ME027
Wrong value for USERSELECTION specified! Input either 'define' or 'weight'.

CMA-ME028
User does not have enough privilege. Please access the database with high privilege account.

CMA-ME029
Wrong value for <parameter> specified! Input either 'true' or 'false'.

CMA-ME030
start_date is incorrect

CMA-ME031
end_date is incorrect

CMA-ME032
Record record is not valid, following case may meet both report class and job name filled with *, both of them have specified value, capture ratio is greater than 1.

CMA-ME033
<job_name> has duplicate records.

CMA-ME034
Report class <class_name> has duplicate records.

CMA-ME035
Server <Server> has duplicate records.

CMA-ME036
Record<record> is invalid, no record existed in TDSz tables.

CMA-ME037
Application app_name for environment environment function function on CPC Series NO cpc_series_no MVS_SYSTEM_ID mvs_system_id cannot fit a model. Please check your data.

CMA-ME038
Application app_name for environment environment function function on CPC Series NO cpc_series_no MVS_SYSTEM_ID mvs_system_id is missing values. Please check your data.

CMA-ME039
Parameter HOUR_START and HOUR_END must be in range [0, 23], HOUR_START must not larger than HOUR_END.

CMA-ME040
DATE_START must be earlier than DATE_END.

CMA-ME041
Wrong PEAKRANK input format. Valid format is 'level1,level2,...,leveln' and 0<level<'<hour>'.

CMA-ME042
Wildcard character in Job_name <JOB_NAME> is incorrect, please check input application mapping file.

CMA-ME043
No needed data in the table <Schema.TableName> from date_start to date_end.

CMA-ME044
No application data to process.

CMA-ME045
No Transaction specified in the anomaly input file.

CMA-ME046
The start date specified in the file is greater than the end date.

CMA-ME047
TIME_START should be earlier than TIME_END.

CMA-ME048
The score ratio is greater than 1. The score ratio should be greater than 0 but less than or equal to 1.

CMA-ME049
There were no tree-based scoring results in the database. Run the tree-based scoring before you execute the classification system.

CMA-MW001
Product ID <product> on LPAR <lpar> and CPC Series NO <cpc_series_no> has been obsoleted from <date>, it will be skipped in forecast sequence.

CMA-MW002
Product ID <product> on LPAR <lpar> and CPC Series NO <cpc_series_no> on <weekday> contained null value and does not have 24 hours data in one day, it cannot be forecasted.

CMA-MW003
Product ID <product> on LPAR <lpar> and CPC Series NO <cpc_series_no> on <weekday> does not have 24 hours data in one day, it cannot be forecasted.

CMA-MW004
Product ID <product> on LPAR <lpar> and CPC Series NO <cpc_series_no> on <weekday> contained a null value, it cannot be forecasted.

CMA-MW005
Product ID <product> on LPAR <lpar> and CPC Series NO <cpc_series_no> contained bad data, stream automatically fix it and forecast.

CMA-MW006
The record count of LPAR <lpar> on processor type <processor type> and CPC Series NO <CPU series no> is less than the limitation of the algorithm, do not forecast.

CMA-MW007
LPAR <lpar> on processor type <processor type> and CPC Series NO <cpc_series_no> have incorrect scoring result, the predict can't be processed.

CMA-MW008
Product ID <product> cannot be put in LPAR <lpar> due to no information, forbidden in setting, or have no overlapped duration, ignored!

CMA-MW009
The total capacity of the LPAR <lpar> is increased <xxx> percent to host the remaining of the Product ID <product>.

CMA-MW010
Do not have permission to save models to model path or model path is incorrect.

CMA-MW011
LPAR <lpar> is invalid and ignored!

CMA-MW012
The total capacity of the LPARs is not enough for Product ID <product>, the optimization is now trying to increase the capacity of certain LPAR.

CMA-MW013
Failed to reading cma_message.csv, detailed CMA information cannot be provided.

CMA-MW014
Job xxx and service xxx have intersections.

CMA-MW015
The stream used actual history data from <DATE_MIN> to <DATE_MAX>.

CMA-MW016
The input start date <input_data_start> is earlier than the actual observed start date <observ_date_start>. The later date was used.

CMA-MW017
The input end date <input_data_end> is later than the actual observed end date <observ_date_end>. The later date was used.

CMA-MW018
The selected period contains incorrect capture ratio for the job. The capture ratio must be less than or equal to 1.

CMA-MW019
The selected period contains incorrect capture ratio for the report class. The capture ratio must be less than or equal to 1.

CMA-MW020
The mapping table does not contain any records and no data is exported to the mapping table. Run appl_lob_z.str to export the data.

Parent topic: Error, warning, and installation messages

CMA-ME001UID and/or PWD for database is incorrect.Explanation The account name for parameter UID or the password for parameter PWD is invalid. The execution of the current stream has stopped and no valid result is produced.User Response Check and make sure the account name or the password is valid, then rerun the stream.Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-ME001

UID and/or PWD for database is incorrect.

Explanation

The account name for parameter UID or the password for parameter PWD is invalid.

The execution of the current stream has stopped and no valid result is produced.

User Response

Check and make sure the account name or the password is valid, then rerun the stream.

Parent topic: Modeling error and warning messages

CMA-ME002No database connection has been selected. Explanation No database connection is provided in the data source node in filed “data source.” This can happen when you run a single branch of a stream manually and the branch has a data source node. User Response Ensure that there is a non-empty and valid data connection in the data source node in filed “data source.” Then, rerun the stream branch. Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-ME002

No database connection has been selected.

Explanation

No database connection is provided in the data source node in filed “data source.” This can happen when you run a single branch of a stream manually and the branch has a data source node.

User Response

Ensure that there is a non-empty and valid data connection in the data source node in filed “data source.” Then, rerun the stream branch.

Parent topic: Modeling error and warning messages

CMA-ME003CMDW (CMA database connection) is incorrect.Explanation The value for the parameter CMDW refers to an invalid database connection.User Response Ensure the value for the CMDW parameter is a valid database connection and that it exists on the computer on which the stream is running. Then, rerun the stream.Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-ME003

CMDW (CMA database connection) is incorrect.

Explanation

The value for the parameter CMDW refers to an invalid database connection.

User Response

Ensure the value for the CMDW parameter is a valid database connection and that it exists on the computer on which the stream is running. Then, rerun the stream.

Parent topic: Modeling error and warning messages

CMA-ME004Excel input file type is incorrect.Explanation Capacity Management Analytics uses Microsoft Excel 2003 formatting. The input file that you specified might be another format.User Response Convert your file to Microsoft Excel 2003 format.Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-ME004

Excel input file type is incorrect.

Explanation

Capacity Management Analytics uses Microsoft Excel 2003 formatting. The input file that you specified might be another format.

User Response

Convert your file to Microsoft Excel 2003 format.

Parent topic: Modeling error and warning messages

CMA-ME005<transaction> transaction doesn't have enough data for model building.Explanation The data recorded in the database for the specified type of <transaction> is not enough, and the anomaly model cannot be built.User Response Change model building date in the transaction.csv file to ensure that the records for the specified <transaction> in the DRL.CICS_T_TRAN_T table are greater than 40.Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-ME005

<transaction> transaction doesn't have enough data for model building.

Explanation

The data recorded in the database for the specified type of <transaction> is not enough, and the anomaly model cannot be built.

User Response

Change model building date in the transaction.csv file to ensure that the records for the specified <transaction> in the DRL.CICS_T_TRAN_T table are greater than 40.

Parent topic: Modeling error and warning messages

CMA-ME006<transaction> transaction has exceed the data limitation for model building, which is SIZE. Please sample your data before you build the model.Explanation The specified <transaction> contained too much data for model building. IBM SPSS Modeler has limits to the amount of data used in model building process. The limitation can be found in the Modeler documentation (http://www.ibm.com/support/knowledgecenter/SS3RA7_16.0.0/com.ibm.spss.modeler.help/clementine/server/adminguidesource/admin_ram.htm).User Response Change model building date in the date_info.csv file to ensure that the input data records are less than the limitation. Alternatively, you can sample the input data to reduce the data records.Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-ME006

<transaction> transaction has exceed the data limitation for model building, which is SIZE. Please sample your data before you build the model.

Explanation

The specified <transaction> contained too much data for model building. IBM® SPSS® Modeler has limits to the amount of data used in model building process. The limitation can be found in the Modeler documentation (http://www.ibm.com/support/knowledgecenter/SS3RA7_16.0.0/com.ibm.spss.modeler.help/clementine/server/adminguidesource/admin_ram.htm).

User Response

Change model building date in the date_info.csv file to ensure that the input data records are less than the limitation. Alternatively, you can sample the input data to reduce the data records.

Parent topic: Modeling error and warning messages

CMA-ME007<transaction> transaction incorrect data, which is response time is less than CPU second. Please check your input data.Explanation The specified <transaction> contained invalid records that indicate that the transaction response time is less than a CPU second.User Response Check the input data for the specified <transaction> and correct the records so that the response time is greater than a CPU second.Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-ME007

<transaction> transaction incorrect data, which is response time is less than CPU second. Please check your input data.

Explanation

The specified <transaction> contained invalid records that indicate that the transaction response time is less than a CPU second.

User Response

Check the input data for the specified <transaction> and correct the records so that the response time is greater than a CPU second.

Parent topic: Modeling error and warning messages

CMA-ME008Do not have permission to read/write to <file> or this file path is incorrect.Explanation The file path you specified in stream parameter is incorrect or Capacity Management Analytics does not have the correct permission to read or write to the file.User Response Ensure the path and permissions are correct to read or write to the <file>.Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-ME008

Do not have permission to read/write to <file> or this file path is incorrect.

Explanation

The file path you specified in stream parameter is incorrect or Capacity Management Analytics does not have the correct permission to read or write to the file.

User Response

Ensure the path and permissions are correct to read or write to the <file>.

Parent topic: Modeling error and warning messages

CMA-ME009No <parameter> specified! Explanation You have not specified the parameter <parameter>. User Response Specify the value for <parameter> in parameter.txt or in Modeler client. Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-ME009

No <parameter> specified!

Explanation

You have not specified the parameter <parameter>.

User Response

Specify the value for <parameter> in parameter.txt or in Modeler client.

Parent topic: Modeling error and warning messages

CMA-ME010CMA Schema or TDSz Schema is incorrect, or related table did not exist in database.Explanation The parameter you specified for CMASCHEMA or TDSz is incorrect or the related table does not exist in database.User Response Modify the parameter you specified for CMASCHEMA or TDSz and ensure the related table exists in the database.Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-ME010

CMA Schema or TDSz Schema is incorrect, or related table did not exist in database.

Explanation

The parameter you specified for CMASCHEMA or TDSz is incorrect or the related table does not exist in database.

User Response

Modify the parameter you specified for CMASCHEMA or TDSz and ensure the related table exists in the database.

Parent topic: Modeling error and warning messages

CMA-ME011Product ID <product> on LPAR <lpar> and CPC Series NO <cpc_series_no> can't fit a model, please inspect your data. Explanation The modeler could not fit a model in time series for Product ID <product> on LPAR <lpar> and CPC Series NO <cpc_series_no>. User Response Inspect your data of Product ID <product> on LPAR <lpar> and CPC Series NO <cpc_series_no> to ensure that it contains valid data or has enough data for building a model. Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-ME011

Product ID <product> on LPAR <lpar> and CPC Series NO <cpc_series_no> can't fit a model, please inspect your data.

Explanation

The modeler could not fit a model in time series for Product ID <product> on LPAR <lpar> and CPC Series NO <cpc_series_no>.

User Response

Inspect your data of Product ID <product> on LPAR <lpar> and CPC Series NO <cpc_series_no> to ensure that it contains valid data or has enough data for building a model.

Parent topic: Modeling error and warning messages

CMA-ME012The storage of certain fields or the number of total fields does not comply to the expected value as defined in the source node.Explanation The storage of certain fields has changed or certain fields have been removed or added to the inputted data source for the current stream. The execution of the current stream was stopped and no valid result is produced.User Response Get the source node from the node indicated in the log where the stream interruption occurred. Check whether the data definition (The storage of certain fields or the number of total fields) in the data source has changed. If so, try to restore it or use the original data source to rerun the stream.Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-ME012

The storage of certain fields or the number of total fields does not comply to the expected value as defined in the source node.

Explanation

The storage of certain fields has changed or certain fields have been removed or added to the inputted data source for the current stream.

The execution of the current stream was stopped and no valid result is produced.

User Response

Get the source node from the node indicated in the log where the stream interruption occurred. Check whether the data definition (The storage of certain fields or the number of total fields) in the data source has changed. If so, try to restore it or use the original data source to rerun the stream.

Parent topic: Modeling error and warning messages

CMA-ME013The total capacity of the LPARs is not enough. No optimization suggestion could be given and the optimization-related reports could not be generated!Explanation There are still some products that are left and they cannot be arranged because each LPAR reached its maximum capacity. The execution of the optimization stream was stopped and no optimization result was produced.User Response The defined capacity of some LPARs should be increased if USERSELECTION=define to give enough space to do the optimization, and, at the same time, the weight of these LPARs should also be increased if USERSELECTION=weight. Also, you can try to free the products if it is not necessary to be reserved in a certain LPAR in the setting file. If these changes are not allowed or they do not increase the capacity, then the optimization cannot be conducted for these products against these LPARs. Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-ME013

The total capacity of the LPARs is not enough. No optimization suggestion could be given and the optimization-related reports could not be generated!

Explanation

There are still some products that are left and they cannot be arranged because each LPAR reached its maximum capacity.

The execution of the optimization stream was stopped and no optimization result was produced.

User Response

The defined capacity of some LPARs should be increased if USERSELECTION=define to give enough space to do the optimization, and, at the same time, the weight of these LPARs should also be increased if USERSELECTION=weight. Also, you can try to free the products if it is not necessary to be reserved in a certain LPAR in the setting file.

If these changes are not allowed or they do not increase the capacity, then the optimization cannot be conducted for these products against these LPARs.

Parent topic: Modeling error and warning messages

CMA-ME014No capacity in each LPARs before arrangement, please check your data or configuration.Explanation Each LPAR has a heavy load because the N89 program, ZOS program, or the system overhead has a large MSU load that is already beyond the capacity of each LPAR in the data. The execution of the optimization stream was stopped and no optimization result is produced.User Response Ensure that the MSU of the LPARs is valid. Then try to increase the capacity for each LPAR by checking the setting of define capacity and the weight setting of each LPAR. It should not be too low. In this case, the define capacity of each LPAR should be increased to provide enough space for the optimization if USERSELECTION=define, and, at the same time, the weight of these LPARs should also be increased if USERSELECTION=weight.Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-ME014

No capacity in each LPARs before arrangement, please check your data or configuration.

Explanation

Each LPAR has a heavy load because the N89 program, ZOS program, or the system overhead has a large MSU load that is already beyond the capacity of each LPAR in the data.

The execution of the optimization stream was stopped and no optimization result is produced.

User Response

Ensure that the MSU of the LPARs is valid. Then try to increase the capacity for each LPAR by checking the setting of define capacity and the weight setting of each LPAR. It should not be too low. In this case, the define capacity of each LPAR should be increased to provide enough space for the optimization if USERSELECTION=define, and, at the same time, the weight of these LPARs should also be increased if USERSELECTION=weight.

Parent topic: Modeling error and warning messages

CMA-ME015No suitable LPAR in the data!Explanation The data inputted to CMA is invalid and contains no LPAR information. The execution of the optimization stream stopped and no optimization suggestion is given.User Response Check and make sure the data is valid.Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-ME015

No suitable LPAR in the data!

Explanation

The data inputted to CMA is invalid and contains no LPAR information.

The execution of the optimization stream stopped and no optimization suggestion is given.

User Response

Check and make sure the data is valid.

Parent topic: Modeling error and warning messages

CMA-ME016No product needs to be optimized!Explanation The data during optimized period contains null value for field PROD_ID or each of the optimizing product in field PROD_ID have zero MSU in the history data. So the data is invalid or has nothing to be optimized. The optimizing products refer to those that are waiting to be optimized. The execution of the optimization stream has stopped and no optimization suggestion is given.User Response Check why the optimizing product has zero MSU. The possible reasons include the machine was halted recently in a long term, data inputted to CMA is invalid, or abnormal execution results in CMA forecast processing. Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-ME016

No product needs to be optimized!

Explanation

The data during optimized period contains null value for field PROD_ID or each of the optimizing product in field PROD_ID have zero MSU in the history data. So the data is invalid or has nothing to be optimized. The optimizing products refer to those that are waiting to be optimized.

The execution of the optimization stream has stopped and no optimization suggestion is given.

User Response

Check why the optimizing product has zero MSU. The possible reasons include the machine was halted recently in a long term, data inputted to CMA is invalid, or abnormal execution results in CMA forecast processing.

Parent topic: Modeling error and warning messages

CMA-ME017The product <product> is cared but not suitable for any LPAR.Explanation A product can be specified as a cared product which has priority and is arranged first. If it is not suitable for any of the LPARs, a dead loop may occur when the stream is run. To avoid this, the stream will detect this and interrupt. The execution of the optimization stream stopped and no optimization suggestion is given.User Response Check if the capacity of all the LPARs are normal and check if <product> has an valid amount of MSU.Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-ME017

The product <product> is cared but not suitable for any LPAR.

Explanation

A product can be specified as a cared product which has priority and is arranged first. If it is not suitable for any of the LPARs, a dead loop may occur when the stream is run. To avoid this, the stream will detect this and interrupt.

The execution of the optimization stream stopped and no optimization suggestion is given.

User Response

Check if the capacity of all the LPARs are normal and check if <product> has an valid amount of MSU.

Parent topic: Modeling error and warning messages

CMA-ME018Invalid condition meet, optimization stopped.Explanation The optimization stream encountered an unknown problem and cannot continue to output the result. The execution of the optimization stream stopped and no optimization suggestion is given.User Response Do not delete any logs or temporary files. Contact customer support to resolve the issue.Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-ME018

Invalid condition meet, optimization stopped.

Explanation

The optimization stream encountered an unknown problem and cannot continue to output the result.

The execution of the optimization stream stopped and no optimization suggestion is given.

User Response

Do not delete any logs or temporary files. Contact customer support to resolve the issue.

Parent topic: Modeling error and warning messages

CMA-ME019Do not have permission to read/write to the path or path is incorrect. Log <file> cannot be created!Explanation The log path you specified for LOGPATH is incorrect or we do not have permission to write to it.User Response Ensure that the LOGPATH parameter you specified in parameter.txt or Modeler client is correct, and ensure we can write a log file to it.Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-ME019

Do not have permission to read/write to the path or path is incorrect. Log <file> cannot be created!

Explanation

The log path you specified for LOGPATH is incorrect or we do not have permission to write to it.

User Response

Ensure that the LOGPATH parameter you specified in parameter.txt or Modeler client is correct, and ensure we can write a log file to it.

Parent topic: Modeling error and warning messages

CMA-ME020CPU and response scoring results didn't have intersection data for classify results.Explanation The same transaction does not have both CPU sec and response time scoring results in the database. This is needed for classification stream.User Response The scoring stream needs to be rerun based on both CPU sec model and response time for the same transaction.Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-ME020

CPU and response scoring results didn't have intersection data for classify results.

Explanation

The same transaction does not have both CPU sec and response time scoring results in the database. This is needed for classification stream.

User Response

The scoring stream needs to be rerun based on both CPU sec model and response time for the same transaction.

Parent topic: Modeling error and warning messages

CMA-ME021Didn't find any CPU and response scoring results in database, please run CPU and response scoring before execute classification stream.Explanation No scoring results for anomaly detection in database. User Response The scoring stream needs to be rerun based on both CPU sec model and response time model for the same transaction. Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-ME021

Didn't find any CPU and response scoring results in database, please run CPU and response scoring before execute classification stream.

Explanation

No scoring results for anomaly detection in database.

User Response

The scoring stream needs to be rerun based on both CPU sec model and response time model for the same transaction.

Parent topic: Modeling error and warning messages

CMA-ME022Didn't find any response scoring results in database, please run response scoring before execute classification stream.Explanation Response time scoring results for anomaly detection didn't exist in database.User Response The scoring stream needs to be rerun based on response time model for the same transaction. Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-ME022

Didn't find any response scoring results in database, please run response scoring before execute classification stream.

Explanation

Response time scoring results for anomaly detection didn't exist in database.

User Response

The scoring stream needs to be rerun based on response time model for the same transaction.

Parent topic: Modeling error and warning messages

CMA-ME023Didn't find any cpu scoring results in Database, please run cpu scoring before execute classification stream.Explanation CPU second scoring results for anomaly detection did not exist in the database. User Response The scoring stream need to be rerun based on CPU second model for the same transaction.Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-ME023

Didn't find any cpu scoring results in Database, please run cpu scoring before execute classification stream.

Explanation

CPU second scoring results for anomaly detection did not exist in the database.

User Response

The scoring stream need to be rerun based on CPU second model for the same transaction.

Parent topic: Modeling error and warning messages

CMA-ME024Can not access the database because password for database is expired. Explanation The password you specified in parameter PWD for database is expired. We cannot access the database. User Response Modify your password for the database, and then specify the new password for parameter PWD. Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-ME024

Can not access the database because password for database is expired.

Explanation

The password you specified in parameter PWD for database is expired. We cannot access the database.

User Response

Modify your password for the database, and then specify the new password for parameter PWD.

Parent topic: Modeling error and warning messages

CMA-ME025Unidentified error.Explanation We cannot identify the error and will show you the Modeler error.User Response Review the Modeler exception and take the appropriate measures.Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-ME025

Unidentified error.

Explanation

We cannot identify the error and will show you the Modeler error.

User Response

Review the Modeler exception and take the appropriate measures.

Parent topic: Modeling error and warning messages

CMA-ME026<transaction> transaction doesn't have enough data for model scoring.Explanation The specified <transaction> does not have any scoring input data.User Response Change the model scoring date in the date_info.csv file to ensure that the specified <transaction> has records.Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-ME026

<transaction> transaction doesn't have enough data for model scoring.

Explanation

The specified <transaction> does not have any scoring input data.

User Response

Change the model scoring date in the date_info.csv file to ensure that the specified <transaction> has records.

Parent topic: Modeling error and warning messages

CMA-ME027Wrong value for USERSELECTION specified! Input either 'define' or 'weight'.Explanation The value inputted for the USERSELECTION parameter for the optimization stream is invalid. It should be either 'define' or 'weight'. The execution of the optimization stream stopped and no optimization suggestion is given.User Response Correct the value for the USERSELECTION parameter, and re-run the stream.Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-ME027

Wrong value for USERSELECTION specified! Input either 'define' or 'weight'.

Explanation

The value inputted for the USERSELECTION parameter for the optimization stream is invalid. It should be either 'define' or 'weight'.

The execution of the optimization stream stopped and no optimization suggestion is given.

User Response

Correct the value for the USERSELECTION parameter, and re-run the stream.

Parent topic: Modeling error and warning messages

CMA-ME028User does not have enough privilege. Please access the database with high privilege account.Explanation The database user specified in the Modeler stream parameters does not have enough privilege to operate the table. User Response Use another database user with higher privilege or grant specified privilege to the user used in stream.Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-ME028

User does not have enough privilege. Please access the database with high privilege account.

Explanation

The database user specified in the Modeler stream parameters does not have enough privilege to operate the table.

User Response

Use another database user with higher privilege or grant specified privilege to the user used in stream.

Parent topic: Modeling error and warning messages

CMA-ME029Wrong value for <parameter> specified! Input either 'true' or 'false'.Explanation <parameter> input value is incorrect.User Response Check the parameter.txt file or parameter input in the Modeler stream and ensure the input value for <parameter> is true or false.Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-ME029

Wrong value for <parameter> specified! Input either 'true' or 'false'.

Explanation

<parameter> input value is incorrect.

User Response

Check the parameter.txt file or parameter input in the Modeler stream and ensure the input value for <parameter> is true or false.

Parent topic: Modeling error and warning messages

CMA-ME030start_date is incorrectExplanation Parameter start_date is incorrect.User Response Check the parameter.txt file or parameter input in the Modeler stream and ensure the input value for start_date is in the correct format.Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-ME030

start_date is incorrect

Explanation

Parameter start_date is incorrect.

User Response

Check the parameter.txt file or parameter input in the Modeler stream and ensure the input value for start_date is in the correct format.

Parent topic: Modeling error and warning messages

CMA-ME031end_date is incorrectExplanation Parameter end_date is incorrect.User Response Check the parameter.txt file or parameter input in the Modeler stream and ensure the input value for end_date is in the correct format.Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-ME031

end_date is incorrect

Explanation

Parameter end_date is incorrect.

User Response

Check the parameter.txt file or parameter input in the Modeler stream and ensure the input value for end_date is in the correct format.

Parent topic: Modeling error and warning messages

CMA-ME032Record record is not valid, following case may meet both report class and job name filled with *, both of them have specified value, capture ratio is greater than 1. Explanation Parameter end_date is incorrect. User Response Check the parameter.txt file or parameter input in the Modeler stream and ensure the input value for end_date is in the correct format. Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-ME032

Record record is not valid, following case may meet both report class and job name filled with *, both of them have specified value, capture ratio is greater than 1.

Explanation

Parameter end_date is incorrect.

User Response

Check the parameter.txt file or parameter input in the Modeler stream and ensure the input value for end_date is in the correct format.

Parent topic: Modeling error and warning messages

CMA-ME033<job_name> has duplicate records. Explanation Parameter <job_name> has duplicate records. User Response Check your data and rerun the stream. Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-ME033

<job_name> has duplicate records.

Explanation

Parameter <job_name> has duplicate records.

User Response

Check your data and rerun the stream.

Parent topic: Modeling error and warning messages

CMA-ME034Report class <class_name> has duplicate records. Explanation Parameter <class_name> has duplicate records. User Response Check your data and rerun the stream. Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-ME034

Report class <class_name> has duplicate records.

Explanation

Parameter <class_name> has duplicate records.

User Response

Check your data and rerun the stream.

Parent topic: Modeling error and warning messages

CMA-ME035Server <Server> has duplicate records. Explanation Parameter <Server> has duplicate records. User Response Check your data and rerun the stream. Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-ME035

Server <Server> has duplicate records.

Explanation

Parameter <Server> has duplicate records.

User Response

Check your data and rerun the stream.

Parent topic: Modeling error and warning messages

CMA-ME036Record<record> is invalid, no record existed in TDSz tables. Explanation There is no record that exists in the TDSz tables, so the record is invalid. User Response Check your data and ensure the record exists in the TDSz tables. Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-ME036

Record<record> is invalid, no record existed in TDSz tables.

Explanation

There is no record that exists in the TDSz tables, so the record is invalid.

User Response

Check your data and ensure the record exists in the TDSz tables.

Parent topic: Modeling error and warning messages

CMA-ME037Application app_name for environment environment function function on CPC Series NO cpc_series_no MVS_SYSTEM_ID mvs_system_id cannot fit a model. Please check your data.Explanation The SPSS Statistics Expert Modeler could not fit a model.User Response Check your data for application app_name for environment environment function function on CPC Series NO cpc_series_no MVS_SYSTEM_ID mvs_system_id to ensure that it contains valid data or that it has enough data to build a model.Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-ME037

Application app_name for environment environment function function on CPC Series NO cpc_series_no MVS_SYSTEM_ID mvs_system_id cannot fit a model. Please check your data.

Explanation

The SPSS® Statistics Expert Modeler could not fit a model.

User Response

Check your data for application app_name for environment environment function function on CPC Series NO cpc_series_no MVS_SYSTEM_ID mvs_system_id to ensure that it contains valid data or that it has enough data to build a model.

Parent topic: Modeling error and warning messages

CMA-ME038Application app_name for environment environment function function on CPC Series NO cpc_series_no MVS_SYSTEM_ID mvs_system_id is missing values. Please check your data.Explanation IBM SPSS Modeler could not fit the model in time series for application app_name for environment environment function function on CPC Series NO cpc_series_no MVS_SYSTEM_ID mvs_system_id due to missing values.User Response Check your data for application app_name for environment environment function function on CPC Series NO cpc_series_no MVS_SYSTEM_ID mvs_system_id to ensure that it has enough data to build a model.Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-ME038

Application app_name for environment environment function function on CPC Series NO cpc_series_no MVS_SYSTEM_ID mvs_system_id is missing values. Please check your data.

Explanation

IBM® SPSS® Modeler could not fit the model in time series for application app_name for environment environment function function on CPC Series NO cpc_series_no MVS_SYSTEM_ID mvs_system_id due to missing values.

User Response

Check your data for application app_name for environment environment function function on CPC Series NO cpc_series_no MVS_SYSTEM_ID mvs_system_id to ensure that it has enough data to build a model.

Parent topic: Modeling error and warning messages

CMA-ME039Parameter HOUR_START and HOUR_END must be in range [0, 23], HOUR_START must not larger than HOUR_END.Explanation The values that you specified for parameter HOUR_START and HOUR_END must be an integer and between 0 and 23. HOUR_START must not larger than HOUR_END.User Response Check your data for the values that you specified for the HOUR_START and HOUR_END parameters.Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-ME039

Parameter HOUR_START and HOUR_END must be in range [0, 23], HOUR_START must not larger than HOUR_END.

Explanation

The values that you specified for parameter HOUR_START and HOUR_END must be an integer and between 0 and 23. HOUR_START must not larger than HOUR_END.

User Response

Check your data for the values that you specified for the HOUR_START and HOUR_END parameters.

Parent topic: Modeling error and warning messages

CMA-ME040DATE_START must be earlier than DATE_END.Explanation The value that you specified for the DATE_START parameter must be earlier than the value that you specified for the DATE_END parameter.User Response Check your data to ensure that the DATE_START value is earlier than the DATE_END value.Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-ME040

DATE_START must be earlier than DATE_END.

Explanation

The value that you specified for the DATE_START parameter must be earlier than the value that you specified for the DATE_END parameter.

User Response

Check your data to ensure that the DATE_START value is earlier than the DATE_END value.

Parent topic: Modeling error and warning messages

CMA-ME041Wrong PEAKRANK input format. Valid format is 'level1,level2,...,leveln' and 0<level<'<hour>'.Explanation The input parameter PEAKRANK has an incorrect value. The value must be formatted as 'level1,level2,...,leveln' or it is not in the range of 0<level<<hour>, where <hour> is automatically calculated from the HOUR_START and HOUR_END parameters.User Response Check your data to ensure that the PEAKRANK value is in the correct format.Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-ME041

Wrong PEAKRANK input format. Valid format is 'level1,level2,...,leveln' and 0<level<'<hour>'.

Explanation

The input parameter PEAKRANK has an incorrect value. The value must be formatted as 'level1,level2,...,leveln' or it is not in the range of 0<level<<hour>, where <hour> is automatically calculated from the HOUR_START and HOUR_END parameters.

User Response

Check your data to ensure that the PEAKRANK value is in the correct format.

Parent topic: Modeling error and warning messages

CMA-ME042Wildcard character in Job_name <JOB_NAME> is incorrect, please check input application mapping file. Explanation A wildcard character was found in <JOB_NAME> and is incorrect. Full values must be entered. User Response Check the input application file and ensure that you enter the full value. Then, rerun the stream. Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-ME042

Wildcard character in Job_name <JOB_NAME> is incorrect, please check input application mapping file.

Explanation

A wildcard character was found in <JOB_NAME> and is incorrect. Full values must be entered.

User Response

Check the input application file and ensure that you enter the full value. Then, rerun the stream.

Parent topic: Modeling error and warning messages

CMA-ME043No needed data in the table <Schema.TableName> from date_start to date_end.Explanation The process failed because no data was not found in the <Schema.TableName> table that is within the time range from data_start to data_end.User Response Check your data to ensure that the date range is correct or that the data in the <Schema.TableName> table is correct.Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-ME043

No needed data in the table <Schema.TableName> from date_start to date_end.

Explanation

The process failed because no data was not found in the <Schema.TableName> table that is within the time range from data_start to data_end.

User Response

Check your data to ensure that the date range is correct or that the data in the <Schema.TableName> table is correct.

Parent topic: Modeling error and warning messages

CMA-ME044No application data to process.Explanation The input data is invalid or empty.User Response Check to ensure that you have valid input data.Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-ME044

No application data to process.

Explanation

The input data is invalid or empty.

User Response

Check to ensure that you have valid input data.

Parent topic: Modeling error and warning messages

CMA-ME045No Transaction specified in the anomaly input file. Explanation No transaction has been specified in the anomaly input file. User Response Specify a transaction to be examined in the transaction.csv file to ensure that your model can be built. Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-ME045

No Transaction specified in the anomaly input file.

Explanation

No transaction has been specified in the anomaly input file.

User Response

Specify a transaction to be examined in the transaction.csv file to ensure that your model can be built.

Parent topic: Modeling error and warning messages

CMA-ME046The start date specified in the file is greater than the end date. Explanation The value that you specified for the DATE_START must be earlier than the DATE_END parameter. User Response Check your data to ensure that the DATE_START value is earlier than the DATE_END value. Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-ME046

The start date specified in the file is greater than the end date.

Explanation

The value that you specified for the DATE_START must be earlier than the DATE_END parameter.

User Response

Check your data to ensure that the DATE_START value is earlier than the DATE_END value.

Parent topic: Modeling error and warning messages

CMA-ME047TIME_START should be earlier than TIME_END. Explanation The value that you specified for the TIME_START must be earlier than TIME_END. User Response Check your data to ensure that the TIME_START value is earlier than the TIME_END value. Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-ME047

TIME_START should be earlier than TIME_END.

Explanation

The value that you specified for the TIME_START must be earlier than TIME_END.

User Response

Check your data to ensure that the TIME_START value is earlier than the TIME_END value.

Parent topic: Modeling error and warning messages

CMA-ME048The score ratio is greater than 1. The score ratio should be greater than 0 but less than or equal to 1. Explanation The SCORE_RATIO value that defines the ratio that the tree-based anomaly model uses to judge whether the transaction is abnormal or not is greater than 1. The default value is 0.5, with a recommended range of this parameter value of [0.5,0.6]. User Response Edit the value of the parameter SCORE_RATIO and ensure it does not exceed 1. Use the recommended values. Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-ME048

The score ratio is greater than 1. The score ratio should be greater than 0 but less than or equal to 1.

Explanation

The SCORE_RATIO value that defines the ratio that the tree-based anomaly model uses to judge whether the transaction is abnormal or not is greater than 1. The default value is 0.5, with a recommended range of this parameter value of [0.5,0.6].

User Response

Edit the value of the parameter SCORE_RATIO and ensure it does not exceed 1. Use the recommended values.

Parent topic: Modeling error and warning messages

CMA-ME049There were no tree-based scoring results in the database. Run the tree-based scoring before you execute the classification system. Explanation The anomaly_classification.str can be run only after all other scoring streams have run. The HCM anomaly table should contain CPU, response, and tree scoring results for the same transaction. User Response Run all scoring streams, including the tree-based scoring stream, then run the classification system. Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-ME049

There were no tree-based scoring results in the database. Run the tree-based scoring before you execute the classification system.

Explanation

The anomaly_classification.str can be run only after all other scoring streams have run. The HCM anomaly table should contain CPU, response, and tree scoring results for the same transaction.

User Response

Run all scoring streams, including the tree-based scoring stream, then run the classification system.

Parent topic: Modeling error and warning messages

CMA-MW001Product ID <product> on LPAR <lpar> and CPC Series NO <cpc_series_no> has been obsoleted from <date>, it will be skipped in forecast sequence. Explanation If the latest date in Product ID <product> on LPAR <lpar> and CPC Series NO <cpc_series_no> (DA) is earlier than the latest date of all the product (DB), and DB minus DA is greater than or equal to ODD (obsolete of days which client specified in stream), then it will be skipped in forecast sequence. User Response If you want the product to be included in the forecast sequence, specify a larger number for the parameter ODD or modify the records in the database so that DB minus DA is less than ODD. Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-MW001

Product ID <product> on LPAR <lpar> and CPC Series NO <cpc_series_no> has been obsoleted from <date>, it will be skipped in forecast sequence.

Explanation

If the latest date in Product ID <product> on LPAR <lpar> and CPC Series NO <cpc_series_no> (DA) is earlier than the latest date of all the product (DB), and DB minus DA is greater than or equal to ODD (obsolete of days which client specified in stream), then it will be skipped in forecast sequence.

User Response

If you want the product to be included in the forecast sequence, specify a larger number for the parameter ODD or modify the records in the database so that DB minus DA is less than ODD.

Parent topic: Modeling error and warning messages

CMA-MW002Product ID <product> on LPAR <lpar> and CPC Series NO <cpc_series_no> on <weekday> contained null value and does not have 24 hours data in one day, it cannot be forecasted. Explanation If the product in one LPAR of one CPC contains only null values in any day of the week and does not have 24 hours of data in any day of the week, it cannot be forecasted. User Response Fill in the null values and enter the missing values in the database to correct this warning. Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-MW002

Product ID <product> on LPAR <lpar> and CPC Series NO <cpc_series_no> on <weekday> contained null value and does not have 24 hours data in one day, it cannot be forecasted.

Explanation

If the product in one LPAR of one CPC contains only null values in any day of the week and does not have 24 hours of data in any day of the week, it cannot be forecasted.

User Response

Fill in the null values and enter the missing values in the database to correct this warning.

Parent topic: Modeling error and warning messages

CMA-MW003Product ID <product> on LPAR <lpar> and CPC Series NO <cpc_series_no> on <weekday> does not have 24 hours data in one day, it cannot be forecasted. Explanation If the product in one LPAR of one CPC does not have 24 hours of data in any day of the week, it cannot be forecasted. User Response Enter the missing values, according to your SMF data, in the database to correct this warning. Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-MW003

Product ID <product> on LPAR <lpar> and CPC Series NO <cpc_series_no> on <weekday> does not have 24 hours data in one day, it cannot be forecasted.

Explanation

If the product in one LPAR of one CPC does not have 24 hours of data in any day of the week, it cannot be forecasted.

User Response

Enter the missing values, according to your SMF data, in the database to correct this warning.

Parent topic: Modeling error and warning messages

CMA-MW004Product ID <product> on LPAR <lpar> and CPC Series NO <cpc_series_no> on <weekday> contained a null value, it cannot be forecasted. Explanation If the product in one LPAR of one CPC contains only null values in any day of week, it cannot be forecasted. User Response Enter the missing values in the database to correct this warning. Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-MW004

Product ID <product> on LPAR <lpar> and CPC Series NO <cpc_series_no> on <weekday> contained a null value, it cannot be forecasted.

Explanation

If the product in one LPAR of one CPC contains only null values in any day of week, it cannot be forecasted.

User Response

Enter the missing values in the database to correct this warning.

Parent topic: Modeling error and warning messages

CMA-MW005Product ID <product> on LPAR <lpar> and CPC Series NO <cpc_series_no> contained bad data, stream automatically fix it and forecast. Explanation Product ID <product> on LPAR <lpar> and CPC Series NO<cpc_series_no> contained data which PROD_MSU is not less than PROC_CAPACITY_MSU, Stream will replace the data with last normal data in the same hour of the same week of day for the specified product. User Response Correct the data in database for the specified product so that the PROD_MSU is always less than PROC_CAPACITY_MSU. Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-MW005

Product ID <product> on LPAR <lpar> and CPC Series NO <cpc_series_no> contained bad data, stream automatically fix it and forecast.

Explanation

Product ID <product> on LPAR <lpar> and CPC Series NO<cpc_series_no> contained data which PROD_MSU is not less than PROC_CAPACITY_MSU, Stream will replace the data with last normal data in the same hour of the same week of day for the specified product.

User Response

Correct the data in database for the specified product so that the PROD_MSU is always less than PROC_CAPACITY_MSU.

Parent topic: Modeling error and warning messages

CMA-MW006The record count of LPAR <lpar> on processor type <processor type> and CPC Series NO <CPU series no> is less than the limitation of the algorithm, do not forecast. Explanation Historical specified combination is less than algorithm requirement. User Response Add enough historical data for this combination. Hourly forecast required at least 721 records, daily forecast required at least 61 records, monthly forecast required at least 4 records. Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-MW006

The record count of LPAR <lpar> on processor type <processor type> and CPC Series NO <CPU series no> is less than the limitation of the algorithm, do not forecast.

Explanation

Historical specified combination is less than algorithm requirement.

User Response

Add enough historical data for this combination. Hourly forecast required at least 721 records, daily forecast required at least 61 records, monthly forecast required at least 4 records.

Parent topic: Modeling error and warning messages

CMA-MW007LPAR <lpar> on processor type <processor type> and CPC Series NO <cpc_series_no> have incorrect scoring result, the predict can't be processed. Explanation Scoring results for specified combination is incorrect, and forecasted result will not be ignored. User Response Check the historical data for this specified combination. If the variance of historical data is too big, then this combination can not be handled by the algorithm. Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-MW007

LPAR <lpar> on processor type <processor type> and CPC Series NO <cpc_series_no> have incorrect scoring result, the predict can't be processed.

Explanation

Scoring results for specified combination is incorrect, and forecasted result will not be ignored.

User Response

Check the historical data for this specified combination. If the variance of historical data is too big, then this combination can not be handled by the algorithm.

Parent topic: Modeling error and warning messages

CMA-MW008Product ID <product> cannot be put in LPAR <lpar> due to no information, forbidden in setting, or have no overlapped duration, ignored! Explanation The product referred by <product> cannot be put in LPAR referred by <lpar> during the optimization arrangement. The execution of the optimization stream will not stop. User Response There is no user response. The message indicates that some products were not placed in LPARs. Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-MW008

Product ID <product> cannot be put in LPAR <lpar> due to no information, forbidden in setting, or have no overlapped duration, ignored!

Explanation

The product referred by <product> cannot be put in LPAR referred by <lpar> during the optimization arrangement.

The execution of the optimization stream will not stop.

User Response

There is no user response. The message indicates that some products were not placed in LPARs.

Parent topic: Modeling error and warning messages

CMA-MW009The total capacity of the LPAR <lpar> is increased <xxx> percent to host the remaining of the Product ID <product>. Explanation This warning will follow after CMA-MW012. This warning indicates that some products are left and there is no LPAR capacity for them to be arranged into. But the optimization algorithm finds a suitable LPAR referred by <lpar> to increased its capacity (the increasing amount is indicated by <xxx> percent) and hosted the product referred by <product>. The capacity is increased means that the actual overload for this LPAR after the optimization may be over the configured capacity, but the optimization stream will not change any configuration for the capacity of this LPAR. The increasing amount is the expected maximum amount above that configured capacity. The capacity is permitted to be increased to the limitation that not above the NOMINAL CAPACITY of the LPAR. The reason that some products are left to be arranged is because the total capacity of all the LPARs is not enough to host all the products. The execution of the optimization stream will continue, it may have another attempt to increase other LPARs for other products which cannot be arranged. In that case, there will be multiple CMA-MW009 warnings. User Response There is no user response. Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-MW009

The total capacity of the LPAR <lpar> is increased <xxx> percent to host the remaining of the Product ID <product>.

Explanation

This warning will follow after CMA-MW012.

This warning indicates that some products are left and there is no LPAR capacity for them to be arranged into. But the optimization algorithm finds a suitable LPAR referred by <lpar> to increased its capacity (the increasing amount is indicated by <xxx> percent) and hosted the product referred by <product>. The capacity is increased means that the actual overload for this LPAR after the optimization may be over the configured capacity, but the optimization stream will not change any configuration for the capacity of this LPAR. The increasing amount is the expected maximum amount above that configured capacity. The capacity is permitted to be increased to the limitation that not above the NOMINAL CAPACITY of the LPAR. The reason that some products are left to be arranged is because the total capacity of all the LPARs is not enough to host all the products.

The execution of the optimization stream will continue, it may have another attempt to increase other LPARs for other products which cannot be arranged. In that case, there will be multiple CMA-MW009 warnings.

User Response

There is no user response.

Parent topic: Modeling error and warning messages

CMA-MW010Do not have permission to save models to model path or model path is incorrect. Explanation The model path you specified in stream parameter is incorrect or we do not have the permission to save model to the path. User Response Modify the OUTPUTMODELPATH parameter you specified in parameter.txt or Modeler client to a valid path. Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-MW010

Do not have permission to save models to model path or model path is incorrect.

Explanation

The model path you specified in stream parameter is incorrect or we do not have the permission to save model to the path.

User Response

Modify the OUTPUTMODELPATH parameter you specified in parameter.txt or Modeler client to a valid path.

Parent topic: Modeling error and warning messages

CMA-MW011LPAR <lpar> is invalid and ignored! Explanation LPAR referred by <lpar> is not suitable for all products to be arranged in it, the stream will continue to run, but this LPAR will be ignored for the succeeding processing. User Response There is no user response. Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-MW011

LPAR <lpar> is invalid and ignored!

Explanation

LPAR referred by <lpar> is not suitable for all products to be arranged in it, the stream will continue to run, but this LPAR will be ignored for the succeeding processing.

User Response

There is no user response.

Parent topic: Modeling error and warning messages

CMA-MW012The total capacity of the LPARs is not enough for Product ID <product>, the optimization is now trying to increase the capacity of certain LPAR. Explanation This warning indicates that some products are left and there is no LPAR capacity for them to be arranged into. The optimization algorithm is trying to locate a suitable LPAR to host the <product> product by increasing the capacity of the LPAR. User Response There is no user response. Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-MW012

The total capacity of the LPARs is not enough for Product ID <product>, the optimization is now trying to increase the capacity of certain LPAR.

Explanation

This warning indicates that some products are left and there is no LPAR capacity for them to be arranged into. The optimization algorithm is trying to locate a suitable LPAR to host the <product> product by increasing the capacity of the LPAR.

User Response

There is no user response.

Parent topic: Modeling error and warning messages

CMA-MW013Failed to reading cma_message.csv, detailed CMA information cannot be provided. Explanation The Modeler stream cannot read cma_message.csv so some of CMA error cannot be shown. User Response On Windows operating systems, ensure that you have read and write permissions for $CMA_HOME/Streams/data folder. On Unix or Linux operating systems, ensure that the Streams folder is copied to the correct location and $CMA_HOME environment variable is set correctly. Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-MW013

Failed to reading cma_message.csv, detailed CMA information cannot be provided.

Explanation

The Modeler stream cannot read cma_message.csv so some of CMA error cannot be shown.

User Response

On Windows operating systems, ensure that you have read and write permissions for $CMA_HOME/Streams/data folder. On Unix or Linux operating systems, ensure that the Streams folder is copied to the correct location and $CMA_HOME environment variable is set correctly.

Parent topic: Modeling error and warning messages

CMA-MW014Job xxx and service xxx have intersections. Explanation Job xxx belongs to service class xxx. Both are mentioned in the input file appl_lob_z.csv, causing double counting in the calculation. User Response Remove either the job or the class in the input file appl_lob_z.csv. Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-MW014

Job xxx and service xxx have intersections.

Explanation

Job xxx belongs to service class xxx. Both are mentioned in the input file appl_lob_z.csv, causing double counting in the calculation.

User Response

Remove either the job or the class in the input file appl_lob_z.csv.

Parent topic: Modeling error and warning messages

CMA-MW015The stream used actual history data from <DATE_MIN> to <DATE_MAX>. Explanation If the values that you specified for the DATE_START and DATE_END parameters are out of range between the actual history start date and the actual history end date. The stream forecast seconds using the actual history start date or actual history end date and a warning message is displayed to show the data range that was used in the stream. User Response There is no user response. Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-MW015

The stream used actual history data from <DATE_MIN> to <DATE_MAX>.

Explanation

If the values that you specified for the DATE_START and DATE_END parameters are out of range between the actual history start date and the actual history end date. The stream forecast seconds using the actual history start date or actual history end date and a warning message is displayed to show the data range that was used in the stream.

User Response

There is no user response.

Parent topic: Modeling error and warning messages

CMA-MW016The input start date <input_data_start> is earlier than the actual observed start date <observ_date_start>. The later date was used. Explanation The date in the DATE_START parameter is earlier than the actual observed start date. The stream used the actual observed start date instead. User Response There is no user response. Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-MW016

The input start date <input_data_start> is earlier than the actual observed start date <observ_date_start>. The later date was used.

Explanation

The date in the DATE_START parameter is earlier than the actual observed start date. The stream used the actual observed start date instead.

User Response

There is no user response.

Parent topic: Modeling error and warning messages

CMA-MW017The input end date <input_data_end> is later than the actual observed end date <observ_date_end>. The later date was used. Explanation The date in the DATE_END parameter is later than the actual observed end date. The stream used the actual observed end date instead. User Response There is no user response. Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-MW017

The input end date <input_data_end> is later than the actual observed end date <observ_date_end>. The later date was used.

Explanation

The date in the DATE_END parameter is later than the actual observed end date. The stream used the actual observed end date instead.

User Response

There is no user response.

Parent topic: Modeling error and warning messages

CMA-MW018The selected period contains incorrect capture ratio for the job. The capture ratio must be less than or equal to 1. Explanation The specified period contains an incorrect capture ratio for the job. User Response Change the selected period so the capture ratio remains below or at a value of 1. Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-MW018

The selected period contains incorrect capture ratio for the job. The capture ratio must be less than or equal to 1.

Explanation

The specified period contains an incorrect capture ratio for the job.

User Response

Change the selected period so the capture ratio remains below or at a value of 1.

Parent topic: Modeling error and warning messages

CMA-MW019The selected period contains incorrect capture ratio for the report class. The capture ratio must be less than or equal to 1. Explanation The specified period contains an incorrect capture ratio for the report class. User Response Change the selected period so the capture ratio remains below or at a value of 1. Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-MW019

The selected period contains incorrect capture ratio for the report class. The capture ratio must be less than or equal to 1.

Explanation

The specified period contains an incorrect capture ratio for the report class.

User Response

Change the selected period so the capture ratio remains below or at a value of 1.

Parent topic: Modeling error and warning messages

CMA-MW020The mapping table does not contain any records and no data is exported to the mapping table. Run appl_lob_z.str to export the data. Explanation The mapping table contains no records and no data is exported to the mapping table. User Response Run appl_lob_z.str to export the data. Modeling error and warning messagesRunning the IBM Capacity Management Analytics anomaly streams, SCA streams, and ETL streams generate stream logs in the log folder. The log folder is specified by the LOGPATH parameter.

CMA-MW020

The mapping table does not contain any records and no data is exported to the mapping table. Run appl_lob_z.str to export the data.

Explanation

The mapping table contains no records and no data is exported to the mapping table.

User Response

Run appl_lob_z.str to export the data.

Parent topic: Modeling error and warning messages

Reporting error and warning messagesError or warning messages are displayed in reports and workspaces. In reports, the message summary is displayed in the right top of the subtitle bar. In workspaces, the message summary is displayed at the end of the global prompts area on the left. The message summary is in red text. You can click the message to show the details.Error, warning, and installation messagesError, warning, and installation messages are displayed for IBM Capacity Management Analytics.CMA-RW001Partial Interval Band Warning.CMA-RW002Scenario Synchronization Warning.

Reporting error and warning messages

Error or warning messages are displayed in reports and workspaces. In reports, the message summary is displayed in the right top of the subtitle bar. In workspaces, the message summary is displayed at the end of the global prompts area on the left. The message summary is in red text. You can click the message to show the details.

CMA-RW001
Partial Interval Band Warning.

CMA-RW002
Scenario Synchronization Warning.

Parent topic: Error, warning, and installation messages

CMA-RW001Partial Interval Band Warning. Explanation This warning message is displayed because partial interval band is detected for your selection on date/time range and interval band value. Please consider one of the following suggestions: Change date/time range to be a multiple of the interval band.Change interval band selection to a smaller value. Condition to display When you select a partial interval band with your date/rime range and your interval band choices. Reporting error and warning messagesError or warning messages are displayed in reports and workspaces. In reports, the message summary is displayed in the right top of the subtitle bar. In workspaces, the message summary is displayed at the end of the global prompts area on the left. The message summary is in red text. You can click the message to show the details.

CMA-RW001

Partial Interval Band Warning.

Explanation

This warning message is displayed because partial interval band is detected for your selection on date/time range and interval band value. Please consider one of the following suggestions:

Change date/time range to be a multiple of the interval band.

Change interval band selection to a smaller value.

Condition to display

When you select a partial interval band with your date/rime range and your interval band choices.

Parent topic: Reporting error and warning messages

CMA-RW002Scenario Synchronization Warning. Explanation The current optimization result is based on the forecast result running at <TIMESTAMP>. There are new updates. Please run optimization again. Condition to display This warning message is for the Software Cost Analytics feature, and is issued when optimization records are later than forecast records. Reporting error and warning messagesError or warning messages are displayed in reports and workspaces. In reports, the message summary is displayed in the right top of the subtitle bar. In workspaces, the message summary is displayed at the end of the global prompts area on the left. The message summary is in red text. You can click the message to show the details.

CMA-RW002

Scenario Synchronization Warning.

Explanation

The current optimization result is based on the forecast result running at <TIMESTAMP>. There are new updates. Please run optimization again.

Condition to display

This warning message is for the Software Cost Analytics feature, and is issued when optimization records are later than forecast records.

Parent topic: Reporting error and warning messages

Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.Error, warning, and installation messagesError, warning, and installation messages are displayed for IBM Capacity Management Analytics.CMA-OE000The message file cma_messages.csv is not found in the default location.CMA-OE001<lparName> does not have records for every hour.CMA-OE002<lparName> does not have SMF72 records for any hour.CMA-OE003CPU_SERIAL_NO is missing in the parameter file.CMA-OE004Invalid HOUR_START in the parameter file.CMA-OE005HOUR_START is missing in the parameter file.CMA-OE006Invalid HOUR_END in the parameter file.CMA-OE007HOUR_START should be a lesser value than HOUR_END in the parameter file.CMA-OE008HOUR_END is missing in the parameter file.CMA-OE009Invalid PROCESSOR_TYPE in the parameter file.CMA-OE010PROCESSOR_TYPE is missing in the parameter file.CMA-OE011No input date is available or all of the dates specified are invalid in the parameter file.CMA-OE012The decimal format of the LOCALE: <locale> is <decimalFormat>. The -OPTIM_DELIMITER:<delimiter> is given as a parameter. This can result in incorrect values for the decimal format numbers from the input csv files.CMA-OE013CPU_SERIAL_NO in the parameter file cannot be found in the table <cmaSchema>.MVSPM_LPAR.CMA-OE014PROCESSOR_TYPE in the parameter file cannot be found in the table <cmaSchema>.MVSPM_LPAR.CMA-OE015TIME_RESOLUTION is not consistent between the tables MVSPM_LPAR and MVSPM_WORKLOAD_H.CMA-OE016The TARGET_WORKLOAD_IMPORTANCE_LEVEL of PROCESSOR_TYPE IFL is not null in the LPAR parameter file.CMA-OE017<lparName> does not have active logical processors for every interval.CMA-OE018Invalid FILE_NAME_MODEL_PARA in the parameter file.CMA-OE019Invalid FILE_NAME_MODEL_PARA_PER_LPAR in the parameter file.CMA-OE020Invalid FILE_NAME_DATE_LIST in the parameter file.CMA-OE021Invalid LPAR during the date and time range.CMA-OW000The folder for storing optimization model LP files does not exist.CMA-OW001Duplicate records may be loaded in the table for LPAR.CMA-OW002Invalid LPAR_PRIORITY of LPAR.CMA-OW003Invalid FIXED_WEIGHT of LPAR.CMA-OW004The TARGET_WORKLOAD_PERCENT of LPAR is missing in the parameter file.CMA-OW005Invalid TARGET_WORKLOAD_PERCENT of LPAR in the parameter file.CMA-OW006<lparName> in the LPAR parameter file cannot be found in the table <cmaSchema>.MVSPM_LPAR.CMA-OW007FILE_NAME_MODEL_PARA is missing from the parameter file.CMA-OW008FILE_NAME_PARA_PER_LPAR is missing from the parameter file.CMA-OW009FILE_NAME_DATE_LIST is missing from the parameter file.CMA-OW010EXECUTION_NAME is missing from the parameter file.CMA-OW011Invalid value in the model parameter file.CMA-OW012Missing SUB_OBJECTIVE_WEIGHT value in the model parameter file.CMA-OW013 Missing SUB_OBJECTIVE_NAME in the model parameter file.CMA-OW015Invalid format for DATE in the date list parameter file.CMA-OW016The given double value does not match the decimal format of the locale.CMA-OW017Records already exist in the Optimization Metadata table.CMA-OW018Records already exist in the LPAR Weight Optimization Parameters table.CMA-OW019The captured seconds of the target workload calculated from the <TDSzSchema> table is larger than the LPAR busy seconds calculated from the <cmaSchema> table.CMA-OW020MEASURED_SEC does not match between records in the <TDSzSchema>.MVSPM_WORKLOAD_H table and the <cmaSchema>.MVSPM_LPAR table.CMA-OW021Records already exist in the LPAR Weight Optimization table.CMA-OW022Records already exist in the LPAR Weight Optimization Detail table.CMA-OW023Invalid TARGET_WORKLOAD_IMPORTANCE_LEVEL of LPAR in the parameter file.CMA-OW024Optimized weight for LPAR is less than 0.1%.CMA-OW025<lparName> is missing in the LPAR parameter file.CMA-OW026<lparName> does not have any active logical processors for any interval.

Optimization error and warning messages

The following error or warnings may occur for the LPAR weight optimization.

CMA-OE000
The message file cma_messages.csv is not found in the default location.

CMA-OE001
<lparName> does not have records for every hour.

CMA-OE002
<lparName> does not have SMF72 records for any hour.

CMA-OE003
CPU_SERIAL_NO is missing in the parameter file.

CMA-OE004
Invalid HOUR_START in the parameter file.

CMA-OE005
HOUR_START is missing in the parameter file.

CMA-OE006
Invalid HOUR_END in the parameter file.

CMA-OE007
HOUR_START should be a lesser value than HOUR_END in the parameter file.

CMA-OE008
HOUR_END is missing in the parameter file.

CMA-OE009
Invalid PROCESSOR_TYPE in the parameter file.

CMA-OE010
PROCESSOR_TYPE is missing in the parameter file.

CMA-OE011
No input date is available or all of the dates specified are invalid in the parameter file.

CMA-OE012
The decimal format of the LOCALE: <locale> is <decimalFormat>. The -OPTIM_DELIMITER:<delimiter> is given as a parameter. This can result in incorrect values for the decimal format numbers from the input csv files.

CMA-OE013
CPU_SERIAL_NO in the parameter file cannot be found in the table <cmaSchema>.MVSPM_LPAR.

CMA-OE014
PROCESSOR_TYPE in the parameter file cannot be found in the table <cmaSchema>.MVSPM_LPAR.

CMA-OE015
TIME_RESOLUTION is not consistent between the tables MVSPM_LPAR and MVSPM_WORKLOAD_H.

CMA-OE016
The TARGET_WORKLOAD_IMPORTANCE_LEVEL of PROCESSOR_TYPE IFL is not null in the LPAR parameter file.

CMA-OE017
<lparName> does not have active logical processors for every interval.

CMA-OE018
Invalid FILE_NAME_MODEL_PARA in the parameter file.

CMA-OE019
Invalid FILE_NAME_MODEL_PARA_PER_LPAR in the parameter file.

CMA-OE020
Invalid FILE_NAME_DATE_LIST in the parameter file.

CMA-OE021
Invalid LPAR during the date and time range.

CMA-OW000
The folder for storing optimization model LP files does not exist.

CMA-OW001
Duplicate records may be loaded in the table for LPAR.

CMA-OW002
Invalid LPAR_PRIORITY of LPAR.

CMA-OW003
Invalid FIXED_WEIGHT of LPAR.

CMA-OW004
The TARGET_WORKLOAD_PERCENT of LPAR is missing in the parameter file.

CMA-OW005
Invalid TARGET_WORKLOAD_PERCENT of LPAR in the parameter file.

CMA-OW006
<lparName> in the LPAR parameter file cannot be found in the table <cmaSchema>.MVSPM_LPAR.

CMA-OW007
FILE_NAME_MODEL_PARA is missing from the parameter file.

CMA-OW008
FILE_NAME_PARA_PER_LPAR is missing from the parameter file.

CMA-OW009
FILE_NAME_DATE_LIST is missing from the parameter file.

CMA-OW010
EXECUTION_NAME is missing from the parameter file.

CMA-OW011
Invalid value in the model parameter file.

CMA-OW012
Missing SUB_OBJECTIVE_WEIGHT value in the model parameter file.

CMA-OW013
Missing SUB_OBJECTIVE_NAME in the model parameter file.

CMA-OW015
Invalid format for DATE in the date list parameter file.

CMA-OW016
The given double value does not match the decimal format of the locale.

CMA-OW017
Records already exist in the Optimization Metadata table.

CMA-OW018
Records already exist in the LPAR Weight Optimization Parameters table.

CMA-OW019
The captured seconds of the target workload calculated from the <TDSzSchema> table is larger than the LPAR busy seconds calculated from the <cmaSchema> table.

CMA-OW020
MEASURED_SEC does not match between records in the <TDSzSchema>.MVSPM_WORKLOAD_H table and the <cmaSchema>.MVSPM_LPAR table.

CMA-OW021
Records already exist in the LPAR Weight Optimization table.

CMA-OW022
Records already exist in the LPAR Weight Optimization Detail table.

CMA-OW023
Invalid TARGET_WORKLOAD_IMPORTANCE_LEVEL of LPAR in the parameter file.

CMA-OW024
Optimized weight for LPAR is less than 0.1%.

CMA-OW025
<lparName> is missing in the LPAR parameter file.

CMA-OW026
<lparName> does not have any active logical processors for any interval.

Parent topic: Error, warning, and installation messages

CMA-OE000The message file cma_messages.csv is not found in the default location. Explanation This message is displayed when the file that contains error and warning messages is not found in the default location CMA_HOME/optimizations/data. User Response Ensure the file cma_messages.csv is under the default location CMA_HOME/optimizations/data and rerun the optimization. Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.

CMA-OE000

The message file cma_messages.csv is not found in the default location.

Explanation

This message is displayed when the file that contains error and warning messages is not found in the default location CMA_HOME/optimizations/data.

User Response

Ensure the file cma_messages.csv is under the default location CMA_HOME/optimizations/data and rerun the optimization.

Parent topic: Optimization error and warning messages

CMA-OE001<lparName> does not have records for every hour. Explanation This message is displayed because the LPAR does not have records for each hour in the time range for the dates indicated. User Response Ensure the chosen dates and time ranges that have a consistent and representative set of LPAR records as input and rerun the optimization. Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.

CMA-OE001

<lparName> does not have records for every hour.

Explanation

This message is displayed because the LPAR does not have records for each hour in the time range for the dates indicated.

User Response

Ensure the chosen dates and time ranges that have a consistent and representative set of LPAR records as input and rerun the optimization.

Parent topic: Optimization error and warning messages

CMA-OE002<lparName> does not have SMF72 records for any hour. Explanation This message is displayed because the LPAR <lparName> does not have SMF72 records for any hour in the LPAR parameter file <fileNameParaPerLpar>. User Response To resolve this issue, set the TARGET_WORKLOAD_IMPORTANCE_LEVEL as null and rerun the optimization. SMF70 data will be used for the LPAR. Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.

CMA-OE002

<lparName> does not have SMF72 records for any hour.

Explanation

This message is displayed because the LPAR <lparName> does not have SMF72 records for any hour in the LPAR parameter file <fileNameParaPerLpar>.

User Response

To resolve this issue, set the TARGET_WORKLOAD_IMPORTANCE_LEVEL as null and rerun the optimization. SMF70 data will be used for the LPAR.

Parent topic: Optimization error and warning messages

CMA-OE003CPU_SERIAL_NO is missing in the parameter file. Explanation CPU_SERIAL_NO is missing in the parameter file <fileNameParameter>. User Response To resolve this issue, enter the item and value in the file and rerun the optimization. Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.

CMA-OE003

CPU_SERIAL_NO is missing in the parameter file.

Explanation

CPU_SERIAL_NO is missing in the parameter file <fileNameParameter>.

User Response

To resolve this issue, enter the item and value in the file and rerun the optimization.

Parent topic: Optimization error and warning messages

CMA-OE004Invalid HOUR_START in the parameter file. Explanation This message is displayed because there is an invalid HOUR_START: <hourStart> in the parameter file <fileNameParameter>. HOUR_START is expected to be an integer between 0 and 23. User Response To resolve this error, correct the value in the parameter file and rerun the optimization. Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.

CMA-OE004

Invalid HOUR_START in the parameter file.

Explanation

This message is displayed because there is an invalid HOUR_START: <hourStart> in the parameter file <fileNameParameter>. HOUR_START is expected to be an integer between 0 and 23.

User Response

To resolve this error, correct the value in the parameter file and rerun the optimization.

Parent topic: Optimization error and warning messages

CMA-OE005HOUR_START is missing in the parameter file. Explanation This message is displayed when HOUR_START is missing in the parameter file <fileNameParameter>. User Response To resolve this error, enter the item and value in the parameter file and rerun the optimization. Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.

CMA-OE005

HOUR_START is missing in the parameter file.

Explanation

This message is displayed when HOUR_START is missing in the parameter file <fileNameParameter>.

User Response

To resolve this error, enter the item and value in the parameter file and rerun the optimization.

Parent topic: Optimization error and warning messages

CMA-OE006Invalid HOUR_END in the parameter file. Explanation This message is displayed when there is an invalid HOUR_END: <hourEnd> in the parameter file <fileNameParameter>. HOUR_END is expected to be an integer between 0 and 23 and should be greater than HOUR_START. User Response To resolve this error, correct the value in the parameter file and rerun the optimization. Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.

CMA-OE006

Invalid HOUR_END in the parameter file.

Explanation

This message is displayed when there is an invalid HOUR_END: <hourEnd> in the parameter file <fileNameParameter>. HOUR_END is expected to be an integer between 0 and 23 and should be greater than HOUR_START.

User Response

To resolve this error, correct the value in the parameter file and rerun the optimization.

Parent topic: Optimization error and warning messages

CMA-OE007HOUR_START should be a lesser value than HOUR_END in the parameter file. Explanation This message is displayed when HOUR_START is a higher value than HOUR_END in the parameter file <fileNameParameter>. HOUR_START should always be a lesser value than HOUR_END. User Response To resolve this error, correct the value in the parameter file and rerun the optimization. Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.

CMA-OE007

HOUR_START should be a lesser value than HOUR_END in the parameter file.

Explanation

This message is displayed when HOUR_START is a higher value than HOUR_END in the parameter file <fileNameParameter>. HOUR_START should always be a lesser value than HOUR_END.

User Response

To resolve this error, correct the value in the parameter file and rerun the optimization.

Parent topic: Optimization error and warning messages

CMA-OE008HOUR_END is missing in the parameter file. Explanation This message is displayed when HOUR_END is missing in the parameter file <fileNameParameter>. User Response To resolve this error, enter the item and value in the parameter file and rerun the optimization. Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.

CMA-OE008

HOUR_END is missing in the parameter file.

Explanation

This message is displayed when HOUR_END is missing in the parameter file <fileNameParameter>.

User Response

To resolve this error, enter the item and value in the parameter file and rerun the optimization.

Parent topic: Optimization error and warning messages

CMA-OE009Invalid PROCESSOR_TYPE in the parameter file. Explanation This message is displayed when the PROCESSOR_TYPE is invalid in the parameter file <fileNameParameter>. The value can be CP, zIIP, zAAP, or IFL. User Response To resolve this error, correct the value in the parameter file and rerun the optimization. Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.

CMA-OE009

Invalid PROCESSOR_TYPE in the parameter file.

Explanation

This message is displayed when the PROCESSOR_TYPE is invalid in the parameter file <fileNameParameter>. The value can be CP, zIIP, zAAP, or IFL.

User Response

To resolve this error, correct the value in the parameter file and rerun the optimization.

Parent topic: Optimization error and warning messages

CMA-OE010PROCESSOR_TYPE is missing in the parameter file. Explanation This message is displayed when the PROCESSOR_TYPE is missing in the parameter file <fileNameParameter>. The value can be CP, zIIP, zAAP, or IFL. User Response To resolve this error, enter the item and value in the parameter file and rerun the optimization. Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.

CMA-OE010

PROCESSOR_TYPE is missing in the parameter file.

Explanation

This message is displayed when the PROCESSOR_TYPE is missing in the parameter file <fileNameParameter>. The value can be CP, zIIP, zAAP, or IFL.

User Response

To resolve this error, enter the item and value in the parameter file and rerun the optimization.

Parent topic: Optimization error and warning messages

CMA-OE011No input date is available or all of the dates specified are invalid in the parameter file. Explanation This message means that either no input date is available, or all of the dates specified in the date list parameter file <fileNameDateList> are invalid. User Response To resolve this error, enter valid dates in the date list parameter file and rerun the optimization. Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.

CMA-OE011

No input date is available or all of the dates specified are invalid in the parameter file.

Explanation

This message means that either no input date is available, or all of the dates specified in the date list parameter file <fileNameDateList> are invalid.

User Response

To resolve this error, enter valid dates in the date list parameter file and rerun the optimization.

Parent topic: Optimization error and warning messages

CMA-OE012The decimal format of the LOCALE: <locale> is <decimalFormat>. The -OPTIM_DELIMITER:<delimiter> is given as a parameter. This can result in incorrect values for the decimal format numbers from the input csv files. Explanation This message means that the given parameter -OPTIM_DELIMITER and the decimal format of the locale are the same. This can lead to interpreting incorrect values for the decimal format numbers. User Response To resolve this issue, use the correct delimiter that matches with the LOCALE as parameter and rerun the optimization. Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.

CMA-OE012

The decimal format of the LOCALE: <locale> is <decimalFormat>. The -OPTIM_DELIMITER:<delimiter> is given as a parameter. This can result in incorrect values for the decimal format numbers from the input csv files.

Explanation

This message means that the given parameter -OPTIM_DELIMITER and the decimal format of the locale are the same. This can lead to interpreting incorrect values for the decimal format numbers.

User Response

To resolve this issue, use the correct delimiter that matches with the LOCALE as parameter and rerun the optimization.

Parent topic: Optimization error and warning messages

CMA-OE013CPU_SERIAL_NO in the parameter file cannot be found in the table <cmaSchema>.MVSPM_LPAR. Explanation This message means that the CPU_SERIAL_NO in the parameter file <fileNameParameter> cannot be found in the SMF70 table. User Response To resolve this issue, correct the value in the parameter file and rerun the optimization. Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.

CMA-OE013

CPU_SERIAL_NO in the parameter file cannot be found in the table <cmaSchema>.MVSPM_LPAR.

Explanation

This message means that the CPU_SERIAL_NO in the parameter file <fileNameParameter> cannot be found in the SMF70 table.

User Response

To resolve this issue, correct the value in the parameter file and rerun the optimization.

Parent topic: Optimization error and warning messages

CMA-OE014PROCESSOR_TYPE in the parameter file cannot be found in the table <cmaSchema>.MVSPM_LPAR. Explanation This message means that the PROCESSOR_TYPE in the parameter file <fileNameParameter> cannot be found in the SMF70 table. User Response To resolve this issue, correct the value in the parameter file and rerun the optimization. Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.

CMA-OE014

PROCESSOR_TYPE in the parameter file cannot be found in the table <cmaSchema>.MVSPM_LPAR.

Explanation

This message means that the PROCESSOR_TYPE in the parameter file <fileNameParameter> cannot be found in the SMF70 table.

User Response

To resolve this issue, correct the value in the parameter file and rerun the optimization.

Parent topic: Optimization error and warning messages

CMA-OE015TIME_RESOLUTION is not consistent between the tables MVSPM_LPAR and MVSPM_WORKLOAD_H. Explanation This message is displayed when TIME_RESOLUTION in the parameter file <fileNameParameter> is not consistent between the tables <cmaSchema>.MVSPM_LPAR and <cmaSchema>.MVSPM_WORKLOAD_H. User Response To resolve this error, use only <cmaSchema>.MVSPM_LPAR records by setting TARGET_WORKLOAD_IMP_LEVEL as null in the LPAR parameter file <fileameParaPerLpar>, or use dates that have matching TIME_RESOLUTION as input for the optimization, or reload the tables by using the same TIME_RESOLUTION and rerun the optimization. Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.

CMA-OE015

TIME_RESOLUTION is not consistent between the tables MVSPM_LPAR and MVSPM_WORKLOAD_H.

Explanation

This message is displayed when TIME_RESOLUTION in the parameter file <fileNameParameter> is not consistent between the tables <cmaSchema>.MVSPM_LPAR and <cmaSchema>.MVSPM_WORKLOAD_H.

User Response

To resolve this error, use only <cmaSchema>.MVSPM_LPAR records by setting TARGET_WORKLOAD_IMP_LEVEL as null in the LPAR parameter file <fileameParaPerLpar>, or use dates that have matching TIME_RESOLUTION as input for the optimization, or reload the tables by using the same TIME_RESOLUTION and rerun the optimization.

Parent topic: Optimization error and warning messages

CMA-OE016The TARGET_WORKLOAD_IMPORTANCE_LEVEL of PROCESSOR_TYPE IFL is not null in the LPAR parameter file. Explanation This message is displayed when the TARGET_WORKLOAD_IMPORTANCE_LEVEL of PROCESSOR_TYPE IFL is not null in the LPAR parameter file <fileNameParaPerLpar>. The value is expected to be null. User Response To resolve this error, set TARGET_WORKLOAD_IMPORTANCE_LEVEL to null in the LPAR parameter file and rerun the optimization. Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.

CMA-OE016

The TARGET_WORKLOAD_IMPORTANCE_LEVEL of PROCESSOR_TYPE IFL is not null in the LPAR parameter file.

Explanation

This message is displayed when the TARGET_WORKLOAD_IMPORTANCE_LEVEL of PROCESSOR_TYPE IFL is not null in the LPAR parameter file <fileNameParaPerLpar>. The value is expected to be null.

User Response

To resolve this error, set TARGET_WORKLOAD_IMPORTANCE_LEVEL to null in the LPAR parameter file and rerun the optimization.

Parent topic: Optimization error and warning messages

CMA-OE017<lparName> does not have active logical processors for every interval. Explanation This message is displayed because the LPAR <lparName> is inactive in the table <cmaSchema>.MVSPM_LPAR. User Response To resolve this error, use dates and a time range that have a consistent and representative set of active LPARs as input and rerun the optimization Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.

CMA-OE017

<lparName> does not have active logical processors for every interval.

Explanation

This message is displayed because the LPAR <lparName> is inactive in the table <cmaSchema>.MVSPM_LPAR.

User Response

To resolve this error, use dates and a time range that have a consistent and representative set of active LPARs as input and rerun the optimization

Parent topic: Optimization error and warning messages

CMA-OE018Invalid FILE_NAME_MODEL_PARA in the parameter file. Explanation This message is displayed when the file FILE_NAME_MODEL_PARA specified in the parameter file <fileNameParameter> does not exist. User Response To resolve this error, provide the absolute path of the file in FILE_NAME_MODEL_PARA or if no value is provided, the <default> location CMA_HOME/optimizations/data and the file name lwo_parameters_model.csv will be used. Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.

CMA-OE018

Invalid FILE_NAME_MODEL_PARA in the parameter file.

Explanation

This message is displayed when the file FILE_NAME_MODEL_PARA specified in the parameter file <fileNameParameter> does not exist.

User Response

To resolve this error, provide the absolute path of the file in FILE_NAME_MODEL_PARA or if no value is provided, the <default> location CMA_HOME/optimizations/data and the file name lwo_parameters_model.csv will be used.

Parent topic: Optimization error and warning messages

CMA-OE019Invalid FILE_NAME_MODEL_PARA_PER_LPAR in the parameter file. Explanation This message is displayed because the file FILE_NAME_MODEL_PARA_PER_LPAR specified in the parameter file <fileNameParameter> does not exist. User Response To resolve this error, provide the absolute path of the file in FILE_NAME_MODEL_PARA_PER_LPAR or if no value is provided, the <default> location CMA_HOME/optimizations/data and the file name lwo_parameters_per_lpar.csv will be used. Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.

CMA-OE019

Invalid FILE_NAME_MODEL_PARA_PER_LPAR in the parameter file.

Explanation

This message is displayed because the file FILE_NAME_MODEL_PARA_PER_LPAR specified in the parameter file <fileNameParameter> does not exist.

User Response

To resolve this error, provide the absolute path of the file in FILE_NAME_MODEL_PARA_PER_LPAR or if no value is provided, the <default> location CMA_HOME/optimizations/data and the file name lwo_parameters_per_lpar.csv will be used.

Parent topic: Optimization error and warning messages

CMA-OE020Invalid FILE_NAME_DATE_LIST in the parameter file. Explanation This message means that the file FILE_NAME_DATE_LIST in the parameter file <fileNameParameter> does not exist. User Response To resolve this error, provide the absolute path of the file in FILE_NAME_DATE_LIST or if no value is provided, the <default> location CMA_HOME/optimizations/data and file name lwo_parameters_date_list.csv will be used. Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.

CMA-OE020

Invalid FILE_NAME_DATE_LIST in the parameter file.

Explanation

This message means that the file FILE_NAME_DATE_LIST in the parameter file <fileNameParameter> does not exist.

User Response

To resolve this error, provide the absolute path of the file in FILE_NAME_DATE_LIST or if no value is provided, the <default> location CMA_HOME/optimizations/data and file name lwo_parameters_date_list.csv will be used.

Parent topic: Optimization error and warning messages

CMA-OE021Invalid LPAR during the date and time range. Explanation This message is displayed when there is no valid LPAR during the specified date and time range. User Response Enter a valid LPAR that is available during the specified date and time range and rerun the optimization. Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.

CMA-OE021

Invalid LPAR during the date and time range.

Explanation

This message is displayed when there is no valid LPAR during the specified date and time range.

User Response

Enter a valid LPAR that is available during the specified date and time range and rerun the optimization.

Parent topic: Optimization error and warning messages

CMA-OW000The folder for storing optimization model LP files does not exist. Explanation This message is displayed when the folder for storing optimization model LP files cannot be found. A new folder is created in <modelPath>. Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.

CMA-OW000

The folder for storing optimization model LP files does not exist.

Explanation

This message is displayed when the folder for storing optimization model LP files cannot be found. A new folder is created in <modelPath>.

Parent topic: Optimization error and warning messages

CMA-OW001Duplicate records may be loaded in the table for LPAR. Explanation This message is displayed when MEASURED_SEC is larger than the TIME_RESOLUTION in the table for the specified date and time intervals. As a result, duplicate records may be loaded in the table. To resolve this issue, double check your data. User Response Double check your data. Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.

CMA-OW001

Duplicate records may be loaded in the table for LPAR.

Explanation

This message is displayed when MEASURED_SEC is larger than the TIME_RESOLUTION in the table for the specified date and time intervals. As a result, duplicate records may be loaded in the table. To resolve this issue, double check your data.

User Response

Double check your data.

Parent topic: Optimization error and warning messages

CMA-OW002Invalid LPAR_PRIORITY of LPAR. Explanation This message is displayed when the LPAR_PRIORITY value is invalid. The value is expected to be a non-negative integer, with a default value of 1. User Response The warning can be ignored if the LPAR_PRIORITY for the LPAR is 1, or you must change the value in the LPAR parameter file. Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.

CMA-OW002

Invalid LPAR_PRIORITY of LPAR.

Explanation

This message is displayed when the LPAR_PRIORITY value is invalid. The value is expected to be a non-negative integer, with a default value of 1.

User Response

The warning can be ignored if the LPAR_PRIORITY for the LPAR is 1, or you must change the value in the LPAR parameter file.

Parent topic: Optimization error and warning messages

CMA-OW003Invalid FIXED_WEIGHT of LPAR. Explanation This message is displayed when the FIXED_WEIGHT value is invalid. The value should be between 0 and 1. The default value is null. User Response The warning can be ignored if the FIXED_WEIGHT for the LPAR is 1, or you must change the value in the LPAR parameter file. Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.

CMA-OW003

Invalid FIXED_WEIGHT of LPAR.

Explanation

This message is displayed when the FIXED_WEIGHT value is invalid. The value should be between 0 and 1. The default value is null.

User Response

The warning can be ignored if the FIXED_WEIGHT for the LPAR is 1, or you must change the value in the LPAR parameter file.

Parent topic: Optimization error and warning messages

CMA-OW004The TARGET_WORKLOAD_PERCENT of LPAR is missing in the parameter file. Explanation This message is displayed when the TARGET_WORKLOAD_PERCENT value is missing in the LPAR parameter file. The default value is 1. User Response The warning can be ignored if the TARGET_WORKLOAD_PERCENT for the LPAR is 1, or you must change the value in the LPAR parameter file. Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.

CMA-OW004

The TARGET_WORKLOAD_PERCENT of LPAR is missing in the parameter file.

Explanation

This message is displayed when the TARGET_WORKLOAD_PERCENT value is missing in the LPAR parameter file. The default value is 1.

User Response

The warning can be ignored if the TARGET_WORKLOAD_PERCENT for the LPAR is 1, or you must change the value in the LPAR parameter file.

Parent topic: Optimization error and warning messages

CMA-OW005Invalid TARGET_WORKLOAD_PERCENT of LPAR in the parameter file. Explanation This message is displayed when the TARGET_WORKLOAD_PERCENT value is invalid in the LPAR parameter file. The value should be between 0 and 1, with a default value of 1. User Response The warning can be ignored if the TARGET_WORKLOAD_PERCENT for the LPAR is 1, or you must change the value in the LPAR parameter file. Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.

CMA-OW005

Invalid TARGET_WORKLOAD_PERCENT of LPAR in the parameter file.

Explanation

This message is displayed when the TARGET_WORKLOAD_PERCENT value is invalid in the LPAR parameter file. The value should be between 0 and 1, with a default value of 1.

User Response

The warning can be ignored if the TARGET_WORKLOAD_PERCENT for the LPAR is 1, or you must change the value in the LPAR parameter file.

Parent topic: Optimization error and warning messages

CMA-OW006<lparName> in the LPAR parameter file cannot be found in the table <cmaSchema>.MVSPM_LPAR. Explanation This message is displayed because the LPAR <lparName> in the LPAR parameter file cannot be found in the table <cmaSchema>.MVSPM_LPAR for the given CPU_SERIAL_NO, PROCESSOR_TYPE, dates and time ranges. The LPAR is excluded. User Response Double check your data in the LPAR parameter file. Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.

CMA-OW006

<lparName> in the LPAR parameter file cannot be found in the table <cmaSchema>.MVSPM_LPAR.

Explanation

This message is displayed because the LPAR <lparName> in the LPAR parameter file cannot be found in the table <cmaSchema>.MVSPM_LPAR for the given CPU_SERIAL_NO, PROCESSOR_TYPE, dates and time ranges. The LPAR is excluded.

User Response

Double check your data in the LPAR parameter file.

Parent topic: Optimization error and warning messages

CMA-OW007FILE_NAME_MODEL_PARA is missing from the parameter file. Explanation This message is displayed when FILE_NAME_MODEL_PARA is missing from the parameter file. The default value <fileNameModelPara> is assigned. User Response To resolve this error, provide the absolute path of the file in FILE_NAME_DATE_PARA or if no value is provided, the <default> location CMA_HOME/optimizations/data and file name lwo_parameters_model.csv will be used. Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.

CMA-OW007

FILE_NAME_MODEL_PARA is missing from the parameter file.

Explanation

This message is displayed when FILE_NAME_MODEL_PARA is missing from the parameter file. The default value <fileNameModelPara> is assigned.

User Response

To resolve this error, provide the absolute path of the file in FILE_NAME_DATE_PARA or if no value is provided, the <default> location CMA_HOME/optimizations/data and file name lwo_parameters_model.csv will be used.

Parent topic: Optimization error and warning messages

CMA-OW008FILE_NAME_PARA_PER_LPAR is missing from the parameter file. Explanation This message is displayed when FILE_NAME_PARA_PER_LPAR is missing from the parameter file. The default value <fileNameParaPerLpar> is assigned. User Response To resolve this error, provide the absolute path of the file in FILE_NAME_PARA_PER_LPAR or if no value is provided, the <default> location CMA_HOME/optimizations/data and file name lwo_parameters_per_lpar.csv will be used. Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.

CMA-OW008

FILE_NAME_PARA_PER_LPAR is missing from the parameter file.

Explanation

This message is displayed when FILE_NAME_PARA_PER_LPAR is missing from the parameter file. The default value <fileNameParaPerLpar> is assigned.

User Response

To resolve this error, provide the absolute path of the file in FILE_NAME_PARA_PER_LPAR or if no value is provided, the <default> location CMA_HOME/optimizations/data and file name lwo_parameters_per_lpar.csv will be used.

Parent topic: Optimization error and warning messages

CMA-OW009FILE_NAME_DATE_LIST is missing from the parameter file. Explanation This message is displayed when FILE_NAME_DATE_LIST is missing from the parameter file. The default value <fileNameDateList> is assigned. User Response To resolve this error, provide the absolute path of the file in FILE_NAME_DATE_LIST or if no value is provided, the <default> location CMA_HOME/optimizations/data and file name lwo_parameters_date_list.csv will be used. Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.

CMA-OW009

FILE_NAME_DATE_LIST is missing from the parameter file.

Explanation

This message is displayed when FILE_NAME_DATE_LIST is missing from the parameter file. The default value <fileNameDateList> is assigned.

User Response

To resolve this error, provide the absolute path of the file in FILE_NAME_DATE_LIST or if no value is provided, the <default> location CMA_HOME/optimizations/data and file name lwo_parameters_date_list.csv will be used.

Parent topic: Optimization error and warning messages

CMA-OW010EXECUTION_NAME is missing from the parameter file. Explanation This message is displayed when EXECUTION_NAME is missing from the parameter file. The default value <executionName> is assigned. User Response The warning can be ignored if you want to use the EXECUTION_NAME by default, or enter the value in the parameter file. Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.

CMA-OW010

EXECUTION_NAME is missing from the parameter file.

Explanation

This message is displayed when EXECUTION_NAME is missing from the parameter file. The default value <executionName> is assigned.

User Response

The warning can be ignored if you want to use the EXECUTION_NAME by default, or enter the value in the parameter file.

Parent topic: Optimization error and warning messages

CMA-OW011Invalid value in the model parameter file. Explanation This message is displayed when the SUB_OBJECTIVE_WEIGHT <subObjectiveWeightValue> of the SUB_OBJECTIVE_NAME <subObjectiveName> is invalid in the model parameter file <fileNameModelPara>. The value is expected to be non-negative and a default value of 1 is assigned. User Response The warning can be ignored if the SUB_OBJECTIVE_WEIGHT for the SUB_OBJECTIVE_NAME is 1 or you must enter the correct value in the model parameter file. Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.

CMA-OW011

Invalid value in the model parameter file.

Explanation

This message is displayed when the SUB_OBJECTIVE_WEIGHT <subObjectiveWeightValue> of the SUB_OBJECTIVE_NAME <subObjectiveName> is invalid in the model parameter file <fileNameModelPara>. The value is expected to be non-negative and a default value of 1 is assigned.

User Response

The warning can be ignored if the SUB_OBJECTIVE_WEIGHT for the SUB_OBJECTIVE_NAME is 1 or you must enter the correct value in the model parameter file.

Parent topic: Optimization error and warning messages

CMA-OW012Missing SUB_OBJECTIVE_WEIGHT value in the model parameter file. Explanation This message is displayed when the SUB_OBJECTIVE_WEIGHT <subObjectiveWeightValue> of the SUB_OBJECTIVE_NAME <subObjectiveName> is missing in the model parameter file <fileNameModelPara>. The default value of 1 is assigned. User Response The warning can be ignored if the SUB_OBJECTIVE_WEIGHT for the SUB_OBJECTIVE_NAME is 1 or you must enter the correct value in the model parameter file. Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.

CMA-OW012

Missing SUB_OBJECTIVE_WEIGHT value in the model parameter file.

Explanation

This message is displayed when the SUB_OBJECTIVE_WEIGHT <subObjectiveWeightValue> of the SUB_OBJECTIVE_NAME <subObjectiveName> is missing in the model parameter file <fileNameModelPara>. The default value of 1 is assigned.

User Response

The warning can be ignored if the SUB_OBJECTIVE_WEIGHT for the SUB_OBJECTIVE_NAME is 1 or you must enter the correct value in the model parameter file.

Parent topic: Optimization error and warning messages

CMA-OW013 Missing SUB_OBJECTIVE_NAME in the model parameter file. Explanation This message is displayed when the SUB_OBJECTIVE_NAME is missing in the model parameter file <fileNameModelPara>. The default value of 1 is assigned to the missing objective. User Response The warning can be ignored if the SUB_OBJECTIVE_WEIGHT for the SUB_OBJECTIVE_NAME is 1 or you must enter the correct value in the model parameter file. Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.

CMA-OW013

Missing SUB_OBJECTIVE_NAME in the model parameter file.

Explanation

This message is displayed when the SUB_OBJECTIVE_NAME is missing in the model parameter file <fileNameModelPara>. The default value of 1 is assigned to the missing objective.

User Response

The warning can be ignored if the SUB_OBJECTIVE_WEIGHT for the SUB_OBJECTIVE_NAME is 1 or you must enter the correct value in the model parameter file.

Parent topic: Optimization error and warning messages

CMA-OW015Invalid format for DATE in the date list parameter file. Explanation This message is displayed when the format for DATE <date> in the date list parameter file <fileNameDateList> is invalid. The date format should be YYYY-MM-DD or the date format of the locale. The date will be excluded. User Response Check the file and the input value of the locale. Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.

CMA-OW015

Invalid format for DATE in the date list parameter file.

Explanation

This message is displayed when the format for DATE <date> in the date list parameter file <fileNameDateList> is invalid. The date format should be YYYY-MM-DD or the date format of the locale. The date will be excluded.

User Response

Check the file and the input value of the locale.

Parent topic: Optimization error and warning messages

CMA-OW016The given double value does not match the decimal format of the locale. Explanation This message occurs when the given double value in the file <filename> does not match the decimal format of the locale, which can lead to assigning the incorrect value. User Response Check the file and the input value of the locale. Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.

CMA-OW016

The given double value does not match the decimal format of the locale.

Explanation

This message occurs when the given double value in the file <filename> does not match the decimal format of the locale, which can lead to assigning the incorrect value.

User Response

Check the file and the input value of the locale.

Parent topic: Optimization error and warning messages

CMA-OW017Records already exist in the Optimization Metadata table. Explanation This message is displayed as follows: EXECUTION_NAME: <executionName> CPU_SERIAL_NO: <cpuSerialNo> AND PROCESSOR_TYPE: <processorType> already has records in the <cmaSchema> .OPTIMIZATION_METADATA table. The old records will be replaced with a new record. User Response Use a different execution name. Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.

CMA-OW017

Records already exist in the Optimization Metadata table.

Explanation

This message is displayed as follows: EXECUTION_NAME: <executionName> CPU_SERIAL_NO: <cpuSerialNo> AND PROCESSOR_TYPE: <processorType> already has records in the <cmaSchema> .OPTIMIZATION_METADATA table. The old records will be replaced with a new record.

User Response

Use a different execution name.

Parent topic: Optimization error and warning messages

CMA-OW018Records already exist in the LPAR Weight Optimization Parameters table. Explanation This message is displayed as follows: EXECUTION_NAME: <executionName> CPU_SERIAL_NO: <cpuSerialNo> AND PROCESSOR_TYPE: <processorType> already has records in the <cmaSchema> .LPAR_WEIGHT_OPTIMIZATION_PARAMETERS table. The old records will be replaced with a new record. User Response Use a different execution name. Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.

CMA-OW018

Records already exist in the LPAR Weight Optimization Parameters table.

Explanation

This message is displayed as follows: EXECUTION_NAME: <executionName> CPU_SERIAL_NO: <cpuSerialNo> AND PROCESSOR_TYPE: <processorType> already has records in the <cmaSchema> .LPAR_WEIGHT_OPTIMIZATION_PARAMETERS table. The old records will be replaced with a new record.

User Response

Use a different execution name.

Parent topic: Optimization error and warning messages

CMA-OW019The captured seconds of the target workload calculated from the <TDSzSchema> table is larger than the LPAR busy seconds calculated from the <cmaSchema> table. Explanation The captured seconds of the target workload calculated from the <TDSzSchema> table is larger than the LPAR busy seconds (total workload) calculated from the <cmaSchema> table. To resolve this issue, check the data in both tables. User Response To resolve this issue, check the data in both tables. Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.

CMA-OW019

The captured seconds of the target workload calculated from the <TDSzSchema> table is larger than the LPAR busy seconds calculated from the <cmaSchema> table.

Explanation

The captured seconds of the target workload calculated from the <TDSzSchema> table is larger than the LPAR busy seconds (total workload) calculated from the <cmaSchema> table. To resolve this issue, check the data in both tables.

User Response

To resolve this issue, check the data in both tables.

Parent topic: Optimization error and warning messages

CMA-OW020MEASURED_SEC does not match between records in the <TDSzSchema>.MVSPM_WORKLOAD_H table and the <cmaSchema>.MVSPM_LPAR table. Explanation MEASURED_SEC does not match between the records in the <TDSzSchema>.MVSPM_WORKLOAD_H table and the <cmaSchema>.MVSPM_LPAR table. To resolve this issue, check the data in both tables. User Response To resolve this issue, check the data in both tables. Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.

CMA-OW020

MEASURED_SEC does not match between records in the <TDSzSchema>.MVSPM_WORKLOAD_H table and the <cmaSchema>.MVSPM_LPAR table.

Explanation

MEASURED_SEC does not match between the records in the <TDSzSchema>.MVSPM_WORKLOAD_H table and the <cmaSchema>.MVSPM_LPAR table. To resolve this issue, check the data in both tables.

User Response

To resolve this issue, check the data in both tables.

Parent topic: Optimization error and warning messages

CMA-OW021Records already exist in the LPAR Weight Optimization table. Explanation This message is displayed as follows: EXECUTION_NAME: <executionName> CPU_SERIAL_NO: <cpuSerialNo> AND PROCESSOR_TYPE: <processorType> already has records in the <cmaSchema> .LPAR_WEIGHT_OPTIMIZATION table. The old records will be replaced with a new record. User Response Use a different execution name. Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.

CMA-OW021

Records already exist in the LPAR Weight Optimization table.

Explanation

This message is displayed as follows: EXECUTION_NAME: <executionName> CPU_SERIAL_NO: <cpuSerialNo> AND PROCESSOR_TYPE: <processorType> already has records in the <cmaSchema> .LPAR_WEIGHT_OPTIMIZATION table. The old records will be replaced with a new record.

User Response

Use a different execution name.

Parent topic: Optimization error and warning messages

CMA-OW022Records already exist in the LPAR Weight Optimization Detail table. Explanation This message is displayed as follows: EXECUTION_NAME: <executionName> CPU_SERIAL_NO: <cpuSerialNo> AND PROCESSOR_TYPE: <processorType> already has records in the <cmaSchema> .LPAR_WEIGHT_OPTIMIZATION_DETAIL table. The old records will be replaced with a new record. User Response Use a different execution name. Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.

CMA-OW022

Records already exist in the LPAR Weight Optimization Detail table.

Explanation

This message is displayed as follows: EXECUTION_NAME: <executionName> CPU_SERIAL_NO: <cpuSerialNo> AND PROCESSOR_TYPE: <processorType> already has records in the <cmaSchema> .LPAR_WEIGHT_OPTIMIZATION_DETAIL table. The old records will be replaced with a new record.

User Response

Use a different execution name.

Parent topic: Optimization error and warning messages

CMA-OW023Invalid TARGET_WORKLOAD_IMPORTANCE_LEVEL of LPAR in the parameter file. Explanation This message displays when there is an invalid TARGET_WORKLOAD_IMPORTANCE_LEVEL of LPAR <lparName> in the LPAR parameter file<fileNameParaPerLpar>. Ensure the value is set to null. User Response This warning can be ignored if the TARGET_WORKLOAD_IMPORTANCE_LEVEL for the LPAR is set to 1, or you can change the value in the LPAR parameter file. Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.

CMA-OW023

Invalid TARGET_WORKLOAD_IMPORTANCE_LEVEL of LPAR in the parameter file.

Explanation

This message displays when there is an invalid TARGET_WORKLOAD_IMPORTANCE_LEVEL of LPAR <lparName> in the LPAR parameter file<fileNameParaPerLpar>. Ensure the value is set to null.

User Response

This warning can be ignored if the TARGET_WORKLOAD_IMPORTANCE_LEVEL for the LPAR is set to 1, or you can change the value in the LPAR parameter file.

Parent topic: Optimization error and warning messages

CMA-OW024Optimized weight for LPAR is less than 0.1%. Explanation This message displays when the optimized weight for LPAR is less than 0.1%. Check the value and validate if any adjustment is required. User Response Check the data and input values. Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.

CMA-OW024

Optimized weight for LPAR is less than 0.1%.

Explanation

This message displays when the optimized weight for LPAR is less than 0.1%. Check the value and validate if any adjustment is required.

User Response

Check the data and input values.

Parent topic: Optimization error and warning messages

CMA-OW025<lparName> is missing in the LPAR parameter file. Explanation This message displays when <lparName> is missing in the LPAR parameter file <fileName>. The default setting is assigned to the LPAR. User Response If it is okay to assign default values to the LPAR, you can ignore this warning, or you can add the LPAR and parameters to the LPAR parameter file. Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.

CMA-OW025

<lparName> is missing in the LPAR parameter file.

Explanation

This message displays when <lparName> is missing in the LPAR parameter file <fileName>. The default setting is assigned to the LPAR.

User Response

If it is okay to assign default values to the LPAR, you can ignore this warning, or you can add the LPAR and parameters to the LPAR parameter file.

Parent topic: Optimization error and warning messages

CMA-OW026<lparName> does not have any active logical processors for any interval. Explanation This message displays when <lparName> does not have any active logical processors for any interval (LP_ONLINE_SEC is always 0). The LPAR will be excluded. User Response Check the data and input values. Optimization error and warning messagesThe following error or warnings may occur for the LPAR weight optimization.

CMA-OW026

<lparName> does not have any active logical processors for any interval.

Explanation

This message displays when <lparName> does not have any active logical processors for any interval (LP_ONLINE_SEC is always 0). The LPAR will be excluded.

User Response

Check the data and input values.

Parent topic: Optimization error and warning messages

Installation error, information, and warning messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.Error, warning, and installation messagesError, warning, and installation messages are displayed for IBM Capacity Management Analytics.Installation error messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.Installation information messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.Installation warning messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

Installation error, information, and warning messages

When installing IBM® Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

Installation error messages
When installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

Installation information messages
When installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

Installation warning messages
When installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

Parent topic: Error, warning, and installation messages

Installation error messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.Installation error, information, and warning messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.CMA-E-002Unable to load JSON file <jsonfile>.CMA-E-003Cannot create Python object <object>.CMA-E-004Unable to load parameter file <file>.CMA-E-005Cannot find file <file>.CMA-E-007Environment variable CMAHOME not set.CMA-E-008More than one file, <file>, was found.CMA-E-009Cannot create directory: <dir>.CMA-E-010There are no feature tarballs detected.CMA-E-011Environment variable CMAHOME: <dir> is not a directory.CMA-E-012Cannot find a support version of CPLEX Optimization Studio.CMA-E-013Version of Python is not supported.CMA-E-014Could not detect cmainstance-config.js. The CMAINSTANCE specified is not valid.CMA-E-016JSON file: <file> content is invalid.CMA-E-018No CMA features are installed. Cannot create Instance.CMA-E-019Feature: <feature> was not activated in CMAINSTANCE: <instance> because <feature> was not detected as installed.CMA-E-020Invalid CMAHOME detected at <dir>. You may need to recreate the CMAHOME directory.CMA-E-021Invalid function: <function>.CMA-E-022Untarring file: <file> failed.CMA-E-023Unable to load Python file: <file>.CMA-E-024Cognos Namespace was not specified. Set the Cognos Namespace before proceeding.CMA-E-025Environment variable <envar> not set.CMA-E-026Directory: <dir> is invalid or doesn't exist.CMA-E-027Failed to build the CMA images application war file.CMA-E-028Failed to execute Python command: <cmd>.CMA-E-029Failed to create directory(ies): <directory>.CMA-E-030External command: <cmd> failed with return code <retcode>.CMA-E-031Logger has not been defined.CMA-E-032Logger has not been defined.CMA-E-033Logger has not been defined.CMA-E-034HOST <host> is invalid: Name or service not known.CMA-E-035Unable to get group name of user <user>.CMA-E-040Parameter <parameter> doesn't exist or invalid.CMA-E-042Fail to <operation>.

Installation error messages

When installing IBM® Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-E-002
Unable to load JSON file <jsonfile>.

CMA-E-003
Cannot create Python object <object>.

CMA-E-004
Unable to load parameter file <file>.

CMA-E-005
Cannot find file <file>.

CMA-E-007
Environment variable CMAHOME not set.

CMA-E-008
More than one file, <file>, was found.

CMA-E-009
Cannot create directory: <dir>.

CMA-E-010
There are no feature tarballs detected.

CMA-E-011
Environment variable CMAHOME: <dir> is not a directory.

CMA-E-012
Cannot find a support version of CPLEX Optimization Studio.

CMA-E-013
Version of Python is not supported.

CMA-E-014
Could not detect cmainstance-config.js. The CMAINSTANCE specified is not valid.

CMA-E-016
JSON file: <file> content is invalid.

CMA-E-018
No CMA features are installed. Cannot create Instance.

CMA-E-019
Feature: <feature> was not activated in CMAINSTANCE: <instance> because <feature> was not detected as installed.

CMA-E-020
Invalid CMAHOME detected at <dir>. You may need to recreate the CMAHOME directory.

CMA-E-021
Invalid function: <function>.

CMA-E-022
Untarring file: <file> failed.

CMA-E-023
Unable to load Python file: <file>.

CMA-E-024
Cognos Namespace was not specified. Set the Cognos Namespace before proceeding.

CMA-E-025
Environment variable <envar> not set.

CMA-E-026
Directory: <dir> is invalid or doesn't exist.

CMA-E-027
Failed to build the CMA images application war file.

CMA-E-028
Failed to execute Python command: <cmd>.

CMA-E-029
Failed to create directory(ies): <directory>.

CMA-E-030
External command: <cmd> failed with return code <retcode>.

CMA-E-031
Logger has not been defined.

CMA-E-032
Logger has not been defined.

CMA-E-033
Logger has not been defined.

CMA-E-034
HOST <host> is invalid: Name or service not known.

CMA-E-035
Unable to get group name of user <user>.

CMA-E-040
Parameter <parameter> doesn't exist or invalid.

CMA-E-042
Fail to <operation>.

Parent topic: Installation error, information, and warning messages

CMA-E-002Unable to load JSON file <jsonfile>.Explanation Installation error: cannot load the JSON <jsonfile> file.User Response Check if the file exists and isn't corrupted and the format is correct.Installation error messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-E-002

Unable to load JSON file <jsonfile>.

Explanation

Installation error: cannot load the JSON <jsonfile> file.

User Response

Check if the file exists and isn't corrupted and the format is correct.

Parent topic: Installation error messages

CMA-E-003Cannot create Python object <object>.Explanation Installation error: cannot create Python object <object>.User Response Check if PYTHONPATH is set correctly in the user profile, and ensure the program file exists.Installation error messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-E-003

Cannot create Python object <object>.

Explanation

Installation error: cannot create Python object <object>.

User Response

Check if PYTHONPATH is set correctly in the user profile, and ensure the program file exists.

Parent topic: Installation error messages

CMA-E-004Unable to load parameter file <file>.Explanation Installation error: cannot load the parameter file <file>.User Response Check if the file exists and isn't corrupted and the format is correct.Installation error messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-E-004

Unable to load parameter file <file>.

Explanation

Installation error: cannot load the parameter file <file>.

User Response

Check if the file exists and isn't corrupted and the format is correct.

Parent topic: Installation error messages

CMA-E-005Cannot find file <file>.Explanation Installation error: cannot find the file <file>.User Response Check if the file exists and isn't corrupted and the format is correct.Installation error messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-E-005

Cannot find file <file>.

Explanation

Installation error: cannot find the file <file>.

User Response

Check if the file exists and isn't corrupted and the format is correct.

Parent topic: Installation error messages

CMA-E-007Environment variable CMAHOME not set.Explanation Installation error: the environment variable CMAHOME is not set.User Response Check the CMAHOME setup in the user profile.Installation error messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-E-007

Environment variable CMAHOME not set.

Explanation

Installation error: the environment variable CMAHOME is not set.

User Response

Check the CMAHOME setup in the user profile.

Parent topic: Installation error messages

CMA-E-008More than one file, <file>, was found.Explanation Installation error: more than one file, <file>, was found. Cannot differentiate the files.Installation error messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-E-008

More than one file, <file>, was found.

Explanation

Installation error: more than one file, <file>, was found. Cannot differentiate the files.

Parent topic: Installation error messages

CMA-E-009Cannot create directory: <dir>.Explanation Installation error: cannot create directory: <dir>.User Response Check if the parent folder exists and ensure the user has the correct permission.Installation error messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-E-009

Cannot create directory: <dir>.

Explanation

Installation error: cannot create directory: <dir>.

User Response

Check if the parent folder exists and ensure the user has the correct permission.

Parent topic: Installation error messages

CMA-E-010There are no feature tarballs detected.Explanation Installation error: there are no feature tarballs detected.User Response Check if the feature tarballs are in the correct directory: cmahome/zipfiles and also ensure that the user has the correct permission to the tarballs.Installation error messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-E-010

There are no feature tarballs detected.

Explanation

Installation error: there are no feature tarballs detected.

User Response

Check if the feature tarballs are in the correct directory: cmahome/zipfiles and also ensure that the user has the correct permission to the tarballs.

Parent topic: Installation error messages

CMA-E-011Environment variable CMAHOME: <dir> is not a directory.Explanation Installation error: environment variable CMAHOME: <dir> is not a directory.User Response Check if CMAHOME is set correctly in the profile and ensure the director exists.Installation error messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-E-011

Environment variable CMAHOME: <dir> is not a directory.

Explanation

Installation error: environment variable CMAHOME: <dir> is not a directory.

User Response

Check if CMAHOME is set correctly in the profile and ensure the director exists.

Parent topic: Installation error messages

CMA-E-012Cannot find a support version of CPLEX Optimization Studio.Explanation Installation error: cannot find a support version of CPLEX Optimization Studio.User Response Refer to the Solution Guide to ensure you have the correct version installed in the correct location.Installation error messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-E-012

Cannot find a support version of CPLEX Optimization Studio.

Explanation

Installation error: cannot find a support version of CPLEX Optimization Studio.

User Response

Refer to the Solution Guide to ensure you have the correct version installed in the correct location.

Parent topic: Installation error messages

CMA-E-013Version of Python is not supported.Explanation Installation error: version of Python is not supported.User Response Refer to the Solution Guide for a supported version of Python.Installation error messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-E-013

Version of Python is not supported.

Explanation

Installation error: version of Python is not supported.

User Response

Refer to the Solution Guide for a supported version of Python.

Parent topic: Installation error messages

CMA-E-014Could not detect cmainstance-config.js. The CMAINSTANCE specified is not valid.Explanation Installation error: could not detect cmainstance-config.js. The CMAINSTANCE specified is not valid.User Response Check the CMAINSTANCE setting in the user profile. Check if the <cmainstance> directory exists and ensure the user has the correct permission. Check if cmainstance-config.js exists in the <cmainstance>/configuration directory, that it isn't corrupted, and the file format is correct.Installation error messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-E-014

Could not detect cmainstance-config.js. The CMAINSTANCE specified is not valid.

Explanation

Installation error: could not detect cmainstance-config.js. The CMAINSTANCE specified is not valid.

User Response

Check the CMAINSTANCE setting in the user profile. Check if the <cmainstance> directory exists and ensure the user has the correct permission. Check if cmainstance-config.js exists in the <cmainstance>/configuration directory, that it isn't corrupted, and the file format is correct.

Parent topic: Installation error messages

CMA-E-016JSON file: <file> content is invalid.Explanation Installation error: JSON file: <file> content is invalid.User Response Check if the file is corrupt, ensure the file is in the correct format, and double check the user's permissions.Installation error messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-E-016

JSON file: <file> content is invalid.

Explanation

Installation error: JSON file: <file> content is invalid.

User Response

Check if the file is corrupt, ensure the file is in the correct format, and double check the user's permissions.

Parent topic: Installation error messages

CMA-E-018No CMA features are installed. Cannot create Instance.Explanation Installation error: no CMA features are installed. Cannot create Instance.User Response Ensure that CMAHOME and CMAINSTANCE are set correctly. Also check if the files and folders exist. Lastly, check if the user has correct permissions.Installation error messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-E-018

No CMA features are installed. Cannot create Instance.

Explanation

Installation error: no CMA features are installed. Cannot create Instance.

User Response

Ensure that CMAHOME and CMAINSTANCE are set correctly. Also check if the files and folders exist. Lastly, check if the user has correct permissions.

Parent topic: Installation error messages

CMA-E-019Feature: <feature> was not activated in CMAINSTANCE: <instance> because <feature> was not detected as installed.Explanation Installation error: feature: feature: <feature> was not activated in CMAINSTANCE: <instance> because <feature> was not detected as installed.User Response Check if the feature tarballs are in the correct directory: cmahome/zipfiles and also ensure that the user has the correct permission to the tarballs.Installation error messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-E-019

Feature: <feature> was not activated in CMAINSTANCE: <instance> because <feature> was not detected as installed.

Explanation

Installation error: feature: feature: <feature> was not activated in CMAINSTANCE: <instance> because <feature> was not detected as installed.

User Response

Check if the feature tarballs are in the correct directory: cmahome/zipfiles and also ensure that the user has the correct permission to the tarballs.

Parent topic: Installation error messages

CMA-E-020Invalid CMAHOME detected at <dir>. You may need to recreate the CMAHOME directory.Explanation Installation error: invalid CMAHOME detected at <dir>. You may need to recreate the CMAHOME directory.User Response Create CMAHOME at <dir> and ensure that the user has the correct permission to access CMAHOME.Installation error messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-E-020

Invalid CMAHOME detected at <dir>. You may need to recreate the CMAHOME directory.

Explanation

Installation error: invalid CMAHOME detected at <dir>. You may need to recreate the CMAHOME directory.

User Response

Create CMAHOME at <dir> and ensure that the user has the correct permission to access CMAHOME.

Parent topic: Installation error messages

CMA-E-021Invalid function: <function>.Explanation Installation error: invalid function: <function>.Installation error messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-E-021

Invalid function: <function>.

Explanation

Installation error: invalid function: <function>.

Parent topic: Installation error messages

CMA-E-022Untarring file: <file> failed.Explanation Installation error: untarring file: <file> failed.User Response Check if the file exists and ensure the user has the correct permissions to the file.Installation error messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-E-022

Untarring file: <file> failed.

Explanation

Installation error: untarring file: <file> failed.

User Response

Check if the file exists and ensure the user has the correct permissions to the file.

Parent topic: Installation error messages

CMA-E-023Unable to load Python file: <file>.Explanation Installation error: unable to load Python file: <file>.User Response Check if the file exists and ensure the user has the correct permissions to the file.Installation error messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-E-023

Unable to load Python file: <file>.

Explanation

Installation error: unable to load Python file: <file>.

User Response

Check if the file exists and ensure the user has the correct permissions to the file.

Parent topic: Installation error messages

CMA-E-024Cognos Namespace was not specified. Set the Cognos Namespace before proceeding.Explanation Installation error: Cognos Namespace was not specified. Set the Cognos Namespace before proceeding.User Response Set the Cognos Namespace.Installation error messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-E-024

Cognos Namespace was not specified. Set the Cognos Namespace before proceeding.

Explanation

Installation error: Cognos Namespace was not specified. Set the Cognos Namespace before proceeding.

User Response

Set the Cognos Namespace.

Parent topic: Installation error messages

CMA-E-025Environment variable <envar> not set.Explanation Installation error: Environment variable <envar> not set.User Response Set <envar> in the user profile.Installation error messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-E-025

Environment variable <envar> not set.

Explanation

Installation error: Environment variable <envar> not set.

User Response

Set <envar> in the user profile.

Parent topic: Installation error messages

CMA-E-026Directory: <dir> is invalid or doesn't exist.Explanation Installation error: Directory: <dir> is invalid or doesn't exist.User Response Check if the <dir> exists and ensure that the user has the correct permissions to the <dir>.Installation error messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-E-026

Directory: <dir> is invalid or doesn't exist.

Explanation

Installation error: Directory: <dir> is invalid or doesn't exist.

User Response

Check if the <dir> exists and ensure that the user has the correct permissions to the <dir>.

Parent topic: Installation error messages

CMA-E-027Failed to build the CMA images application war file.Explanation Installation error: failed to build the CMA images application war file.Installation error messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-E-027

Failed to build the CMA images application war file.

Explanation

Installation error: failed to build the CMA images application war file.

Parent topic: Installation error messages

CMA-E-028Failed to execute Python command: <cmd>.Explanation Installation error: failed to execute Python command: <cmd>.Installation error messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-E-028

Failed to execute Python command: <cmd>.

Explanation

Installation error: failed to execute Python command: <cmd>.

Parent topic: Installation error messages

CMA-E-029Failed to create directory(ies): <directory>.Explanation Installation error: failed to create directory(ies): <directory>.User Response Check to see if the user has the correct permissions.Installation error messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-E-029

Failed to create directory(ies): <directory>.

Explanation

Installation error: failed to create directory(ies): <directory>.

User Response

Check to see if the user has the correct permissions.

Parent topic: Installation error messages

CMA-E-030External command: <cmd> failed with return code <retcode>.Explanation Installation error: external command: <cmd> failed with return code <retcode>.User Response Check to see if the user has the correct permissions and check if the username and password are correct.Installation error messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-E-030

External command: <cmd> failed with return code <retcode>.

Explanation

Installation error: external command: <cmd> failed with return code <retcode>.

User Response

Check to see if the user has the correct permissions and check if the username and password are correct.

Parent topic: Installation error messages

CMA-E-031Logger has not been defined.Explanation Installation error: logger has not been defined.User Response Check if your environment variables are set and if you have the correct permission to write to the log.Installation error messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-E-031

Logger has not been defined.

Explanation

Installation error: logger has not been defined.

User Response

Check if your environment variables are set and if you have the correct permission to write to the log.

Parent topic: Installation error messages

CMA-E-032Logger has not been defined.Explanation Installation error: logger has not been defined.User Response Installation error messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-E-032

Logger has not been defined.

Explanation

Installation error: logger has not been defined.

User Response

Parent topic: Installation error messages

CMA-E-033Logger has not been defined.Explanation Installation error: logger has not been defined.User Response Installation error messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-E-033

Logger has not been defined.

Explanation

Installation error: logger has not been defined.

User Response

Parent topic: Installation error messages

CMA-E-034HOST <host> is invalid: Name or service not known.Explanation Installation error: HOST <host> is invalid: Name or service not known.User Response Check if your input host is valid.Installation error messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-E-034

HOST <host> is invalid: Name or service not known.

Explanation

Installation error: HOST <host> is invalid: Name or service not known.

User Response

Check if your input host is valid.

Parent topic: Installation error messages

CMA-E-035Unable to get group name of user <user>.Explanation Installation error: unable to get group name of user <user>User Response Ensure your ID has been set to cmagroup. Refer to the cmagroup section for more detailed information. Add in xref when available.Installation error messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-E-035

Unable to get group name of user <user>.

Explanation

Installation error: unable to get group name of user <user>

User Response

Ensure your ID has been set to cmagroup. Refer to the cmagroup section for more detailed information.

Parent topic: Installation error messages

CMA-E-040Parameter <parameter> doesn't exist or invalid.Explanation Installation error: parameter <parameter> doesn't exist or invalid.User Response Ensure your command line is valid. Use program.py -h to get more information about parameters.Installation error messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-E-040

Parameter <parameter> doesn't exist or invalid.

Explanation

Installation error: parameter <parameter> doesn't exist or invalid.

User Response

Ensure your command line is valid. Use program.py -h to get more information about parameters.

Parent topic: Installation error messages

CMA-E-042Fail to <operation>.Explanation Installation error: fail to <operation>.User Response Check if your environment variables are set correctly and ensure you have valid input.Installation error messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-E-042

Fail to <operation>.

Explanation

Installation error: fail to <operation>.

User Response

Check if your environment variables are set correctly and ensure you have valid input.

Parent topic: Installation error messages

Installation information messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.Installation error, information, and warning messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.CMA-I-001Untarring <file>.CMA-I-002Installing feature: <feature>.CMA-I-003<operation> of CMAINSTANCE: <path> started.CMA-I-004<operation> of CMAINSTANCE: <path> finished.CMA-I-005<operation> of CMAHOME: <path> started.CMA-I-006<operation> of CMAHOME: <path> finished.CMA-I-007--run was not specified. No actions taken.CMA-I-008Check log <log> for more information.CMA-I-009<operation> of <target>: <path> started.CMA-I-010<operation> of <target>: <path> finished.CMA-I-011<operation> started.CMA-I-012<operation> finished.CMA-I-013No features were installed.CMA-I-014No features were instantiated.

Installation information messages

When installing IBM® Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-I-001
Untarring <file>.

CMA-I-002
Installing feature: <feature>.

CMA-I-003
<operation> of CMAINSTANCE: <path> started.

CMA-I-004
<operation> of CMAINSTANCE: <path> finished.

CMA-I-005
<operation> of CMAHOME: <path> started.

CMA-I-006
<operation> of CMAHOME: <path> finished.

CMA-I-007
--run was not specified. No actions taken.

CMA-I-008
Check log <log> for more information.

CMA-I-009
<operation> of <target>: <path> started.

CMA-I-010
<operation> of <target>: <path> finished.

CMA-I-011
<operation> started.

CMA-I-012
<operation> finished.

CMA-I-013
No features were installed.

CMA-I-014
No features were instantiated.

Parent topic: Installation error, information, and warning messages

CMA-I-001Untarring <file>.Explanation Installation information: untarring <file>.Installation information messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-I-001

Untarring <file>.

Explanation

Installation information: untarring <file>.

Parent topic: Installation information messages

CMA-I-002Installing feature: <feature>.Explanation Installation information: installing feature: <feature>.Installation information messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-I-002

Installing feature: <feature>.

Explanation

Installation information: installing feature: <feature>.

Parent topic: Installation information messages

CMA-I-003<operation> of CMAINSTANCE: <path> started.Explanation Installation information: <operation> of CMAINSTANCE: <path> started.Installation information messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-I-003

<operation> of CMAINSTANCE: <path> started.

Explanation

Installation information: <operation> of CMAINSTANCE: <path> started.

Parent topic: Installation information messages

CMA-I-004<operation> of CMAINSTANCE: <path> finished.Explanation Installation information: <operation> of CMAINSTANCE: <path> finished.Installation information messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-I-004

<operation> of CMAINSTANCE: <path> finished.

Explanation

Installation information: <operation> of CMAINSTANCE: <path> finished.

Parent topic: Installation information messages

CMA-I-005<operation> of CMAHOME: <path> started.Explanation Installation information: <operation> of CMAHOME: <path> started.Installation information messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-I-005

<operation> of CMAHOME: <path> started.

Explanation

Installation information: <operation> of CMAHOME: <path> started.

Parent topic: Installation information messages

CMA-I-006<operation> of CMAHOME: <path> finished.Explanation Installation information: <operation> of CMAHOME: <path> finished.Installation information messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-I-006

<operation> of CMAHOME: <path> finished.

Explanation

Installation information: <operation> of CMAHOME: <path> finished.

Parent topic: Installation information messages

CMA-I-007--run was not specified. No actions taken.Explanation Installation information: --run was not specified. No actions taken.User Response Rerun the program and append --run on command to activate actions.Installation information messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-I-007

--run was not specified. No actions taken.

Explanation

Installation information: --run was not specified. No actions taken.

User Response

Rerun the program and append --run on command to activate actions.

Parent topic: Installation information messages

CMA-I-008Check log <log> for more information.Explanation Installation information: check log <log> for more information.User Response Check the log for more information.Installation information messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-I-008

Check log <log> for more information.

Explanation

Installation information: check log <log> for more information.

User Response

Check the log for more information.

Parent topic: Installation information messages

CMA-I-009<operation> of <target>: <path> started.Explanation Installation information: <operation> of <target>: <path> started.Installation information messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-I-009

<operation> of <target>: <path> started.

Explanation

Installation information: <operation> of <target>: <path> started.

Parent topic: Installation information messages

CMA-I-010<operation> of <target>: <path> finished.Explanation Installation information: <operation> of <target>: <path> finished.Installation information messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-I-010

<operation> of <target>: <path> finished.

Explanation

Installation information: <operation> of <target>: <path> finished.

Parent topic: Installation information messages

CMA-I-011<operation> started.Explanation Installation information: <operation> started.Installation information messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-I-011

<operation> started.

Explanation

Installation information: <operation> started.

Parent topic: Installation information messages

CMA-I-012<operation> finished.Explanation Installation information: <operation> finished.Installation information messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-I-012

<operation> finished.

Explanation

Installation information: <operation> finished.

Parent topic: Installation information messages

CMA-I-013No features were installed.Explanation Installation information: no features were installed.Installation information messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-I-013

No features were installed.

Explanation

Installation information: no features were installed.

Parent topic: Installation information messages

CMA-I-014No features were instantiated.Explanation Installation information: no features were instantiated.Installation information messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-I-014

No features were instantiated.

Explanation

Installation information: no features were instantiated.

Parent topic: Installation information messages

Installation warning messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.Installation error, information, and warning messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.CMA-W-001Environment variable CMAINSTANCE not set.CMA-W-002Environment variable CMAHOME not set.CMA-W-003Feature: <feature> was not activated in CMAINSTANCE: <instance> because <feature> was not detected as installed.CMA-W-004Feature: <feature> was not installed in CMAHOME: <home> because <feature> was not detected in the <home>/zipfiles directory.CMA-W-005Fail to get system locale, use default locale en_US.CMA-W-006Fail to get delimiter from csv file <csvfile> or no csv file to read, use default delimiter, .CMA-W-007External command: <cmd> finished with warning code <retcode> .CMA-W-008External command: <cmd> only can run with sudo on Linux Red Hat 6.CMA-W-009External command: Cognos configuration has been updated. Restart Cognos.CMA-V-001No CMA features were instantiated.CMA-V-002Update/create file: <file>.CMA-V-003Copy file <file> to directory: <dir>.CMA-V-004Reinstate file <file>.CMA-V-005<operation> started.CMA-V-006<operation> finished.CMA-V-007Backup original file <file>.CMA-V-008Saving configuration file: <file>.CMA-V-009Updating configuration data.CMA-V-010Getting environment variable: <envar>.CMA-V-011Name match: <match>.CMA-V-012Run external command: <cmd>.CMA-V-013Run python command: <cmd>.CMA-V-014Preview command: <cmd>.

Installation warning messages

When installing IBM® Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-W-001
Environment variable CMAINSTANCE not set.

CMA-W-002
Environment variable CMAHOME not set.

CMA-W-003
Feature: <feature> was not activated in CMAINSTANCE: <instance> because <feature> was not detected as installed.

CMA-W-004
Feature: <feature> was not installed in CMAHOME: <home> because <feature> was not detected in the <home>/zipfiles directory.

CMA-W-005
Fail to get system locale, use default locale en_US.

CMA-W-006
Fail to get delimiter from csv file <csvfile> or no csv file to read, use default delimiter, .

CMA-W-007
External command: <cmd> finished with warning code <retcode> .

CMA-W-008
External command: <cmd> only can run with sudo on Linux Red Hat 6.

CMA-W-009
External command: Cognos configuration has been updated. Restart Cognos.

CMA-V-001
No CMA features were instantiated.

CMA-V-002
Update/create file: <file>.

CMA-V-003
Copy file <file> to directory: <dir>.

CMA-V-004
Reinstate file <file>.

CMA-V-005
<operation> started.

CMA-V-006
<operation> finished.

CMA-V-007
Backup original file <file>.

CMA-V-008
Saving configuration file: <file>.

CMA-V-009
Updating configuration data.

CMA-V-010
Getting environment variable: <envar>.

CMA-V-011
Name match: <match>.

CMA-V-012
Run external command: <cmd>.

CMA-V-013
Run python command: <cmd>.

CMA-V-014
Preview command: <cmd>.

Parent topic: Installation error, information, and warning messages

CMA-W-001Environment variable CMAINSTANCE not set.Explanation Installation warning: environment variable CMAINSTANCE not set.User Response Check if the CMAINSTANCE is set in the user profile.Installation warning messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-W-001

Environment variable CMAINSTANCE not set.

Explanation

Installation warning: environment variable CMAINSTANCE not set.

User Response

Check if the CMAINSTANCE is set in the user profile.

Parent topic: Installation warning messages

CMA-W-002Environment variable CMAHOME not set.Explanation Installation warning: environment variable CMAHOME not set.User Response Check if the CMAHOME is set in the user profile.Installation warning messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-W-002

Environment variable CMAHOME not set.

Explanation

Installation warning: environment variable CMAHOME not set.

User Response

Check if the CMAHOME is set in the user profile.

Parent topic: Installation warning messages

CMA-W-003Feature: <feature> was not activated in CMAINSTANCE: <instance> because <feature> was not detected as installed.Explanation Installation warning: feature: <feature> was not activated in CMAINSTANCE: <instance> because <feature> was not detected as installed.User Response Check if the feature tar file exists in <home>/zipfiles and isn't corrupted.Check if the feature is installed in the <home>/features directory.Check the user permissions for the <home> and <instance> directories.Installation warning messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-W-003

Feature: <feature> was not activated in CMAINSTANCE: <instance> because <feature> was not detected as installed.

Explanation

Installation warning: feature: <feature> was not activated in CMAINSTANCE: <instance> because <feature> was not detected as installed.

User Response

Check if the feature tar file exists in <home>/zipfiles and isn't corrupted.

Check if the feature is installed in the <home>/features directory.

Check the user permissions for the <home> and <instance> directories.

Parent topic: Installation warning messages

CMA-W-004Feature: <feature> was not installed in CMAHOME: <home> because <feature> was not detected in the <home>/zipfiles directory.Explanation Installation warning: feature: <feature> was not installed in CMAHOME: <home> because <feature> was not detected in the <home>/zipfiles directory.User Response Check if the feature tar file exists in the <home>/zipfiles directory and that it isn't corrupted.Installation warning messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-W-004

Feature: <feature> was not installed in CMAHOME: <home> because <feature> was not detected in the <home>/zipfiles directory.

Explanation

Installation warning: feature: <feature> was not installed in CMAHOME: <home> because <feature> was not detected in the <home>/zipfiles directory.

User Response

Check if the feature tar file exists in the <home>/zipfiles directory and that it isn't corrupted.

Parent topic: Installation warning messages

CMA-W-005Fail to get system locale, use default locale en_US.Explanation Installation warning: fail to get system locale, use default locale en_US.User Response Check the system locale setting.Installation warning messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-W-005

Fail to get system locale, use default locale en_US.

Explanation

Installation warning: fail to get system locale, use default locale en_US.

User Response

Check the system locale setting.

Parent topic: Installation warning messages

CMA-W-006Fail to get delimiter from csv file <csvfile> or no csv file to read, use default delimiter, .Explanation Installation warning: fail to get delimiter from csv file <csvfile> or no csv file to read, use default delimiter, .User Response Check if the file exists, that it isn't corrupted, and the format is correct.Installation warning messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-W-006

Fail to get delimiter from csv file <csvfile> or no csv file to read, use default delimiter, .

Explanation

Installation warning: fail to get delimiter from csv file <csvfile> or no csv file to read, use default delimiter, .

User Response

Check if the file exists, that it isn't corrupted, and the format is correct.

Parent topic: Installation warning messages

CMA-W-007External command: <cmd> finished with warning code <retcode> .Explanation Installation warning: external command: <cmd> finished with warning code <retcode>.User Response The command may have failed. Check the warning code for more detailed information.Installation warning messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-W-007

External command: <cmd> finished with warning code <retcode> .

Explanation

Installation warning: external command: <cmd> finished with warning code <retcode>.

User Response

The command may have failed. Check the warning code for more detailed information.

Parent topic: Installation warning messages

CMA-W-008External command: <cmd> only can run with sudo on Linux Red Hat 6.Explanation Installation warning: external command: <cmd> only can run with sudo on Linux Red Hat 6.Installation warning messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-W-008

External command: <cmd> only can run with sudo on Linux Red Hat 6.

Explanation

Installation warning: external command: <cmd> only can run with sudo on Linux Red Hat 6.

Parent topic: Installation warning messages

CMA-W-009External command: Cognos configuration has been updated. Restart Cognos.Explanation Installation warning: external command: Cognos configuration has been updated. Please restart Cognos.User Response Restart Cognos.Installation warning messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-W-009

External command: Cognos configuration has been updated. Restart Cognos.

Explanation

Installation warning: external command: Cognos configuration has been updated. Please restart Cognos.

User Response

Restart Cognos.

Parent topic: Installation warning messages

CMA-V-001No CMA features were instantiated.Explanation Installation verbose: no CMA features were instantiated.User Response Check if you have valid inputs.Installation warning messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-V-001

No CMA features were instantiated.

Explanation

Installation verbose: no CMA features were instantiated.

User Response

Check if you have valid inputs.

Parent topic: Installation warning messages

CMA-V-002Update/create file: <file>.Explanation Installation verbose: update/create file: <file>.Installation warning messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-V-002

Update/create file: <file>.

Explanation

Installation verbose: update/create file: <file>.

Parent topic: Installation warning messages

CMA-V-003Copy file <file> to directory: <dir>.Explanation Installation verbose: copy file <file> to directory: <dir>.Installation warning messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-V-003

Copy file <file> to directory: <dir>.

Explanation

Installation verbose: copy file <file> to directory: <dir>.

Parent topic: Installation warning messages

CMA-V-004Reinstate file <file>.Explanation Installation verbose: reinstate file <file>.Installation warning messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-V-004

Reinstate file <file>.

Explanation

Installation verbose: reinstate file <file>.

Parent topic: Installation warning messages

CMA-V-005<operation> started.Explanation Installation verbose: <operation> started.Installation warning messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-V-005

<operation> started.

Explanation

Installation verbose: <operation> started.

Parent topic: Installation warning messages

CMA-V-006<operation> finished.Explanation Installation verbose: <operation> finished.Installation warning messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-V-006

<operation> finished.

Explanation

Installation verbose: <operation> finished.

Parent topic: Installation warning messages

CMA-V-007Backup original file <file>.Explanation Installation verbose: backup original file <file>.Installation warning messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-V-007

Backup original file <file>.

Explanation

Installation verbose: backup original file <file>.

Parent topic: Installation warning messages

CMA-V-008Saving configuration file: <file>.Explanation Installation verbose: saving configuration file: <file>.Installation warning messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-V-008

Saving configuration file: <file>.

Explanation

Installation verbose: saving configuration file: <file>.

Parent topic: Installation warning messages

CMA-V-009Updating configuration data.Explanation Installation verbose: updating configuration data.Installation warning messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-V-009

Updating configuration data.

Explanation

Installation verbose: updating configuration data.

Parent topic: Installation warning messages

CMA-V-010Getting environment variable: <envar>.Explanation Installation verbose: getting environment variable: <envar>.Installation warning messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-V-010

Getting environment variable: <envar>.

Explanation

Installation verbose: getting environment variable: <envar>.

Parent topic: Installation warning messages

CMA-V-011Name match: <match>.Explanation Installation verbose: name match: <match>.Installation warning messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-V-011

Name match: <match>.

Explanation

Installation verbose: name match: <match>.

Parent topic: Installation warning messages

CMA-V-012Run external command: <cmd>.Explanation Installation verbose: run external command: <cmd>Installation warning messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-V-012

Run external command: <cmd>.

Explanation

Installation verbose: run external command: <cmd>

Parent topic: Installation warning messages

CMA-V-013Run python command: <cmd>.Explanation Installation verbose: name match: run python command: <cmd>.Installation warning messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-V-013

Run python command: <cmd>.

Explanation

Installation verbose: name match: run python command: <cmd>.

Parent topic: Installation warning messages

CMA-V-014Preview command: <cmd>.Explanation Installation verbose: name match: preview command: <cmd>.Installation warning messagesWhen installing IBM Capacity Management Analytics, it's important to understand what error messages, warnings, and information messages you may encounter.

CMA-V-014

Preview command: <cmd>.

Explanation

Installation verbose: name match: preview command: <cmd>.

Parent topic: Installation warning messages

IBM Capacity Management Analytics terminologyThis glossary includes terms and definitions for IBM Capacity Management Analytics.AAdefinitiondefinitionsAAAAApplication AnalyticsThis feature uses a user supplied application mapping table to provide reports by application, function, and environment.APPLdefinitiondefinitionsAPPLAPPLApplicationCMAdefinitiondefinitionsCMACMACapacity Management AnalyticsCPCdefinitiondefinitionsCPCCPCProcessor and CPU can refer to either the complete system box, or to one of the processors (CPUs) within the system box. Although the meaning might be clear from the context, even mainframe professionals must clarify which processor or CPU meaning they are using. IBM uses the term central processor complex (CPC) to refer to the physical collection of hardware that includes main storage, one or more central processors, timers, and channels. (Some system programmers use the term central electronic complex (CEC) to refer to the mainframe box, but the preferred term is CPC.)CPLEXdefinitiondefinitionsCPLEXCPLEXIBM ILOG CPLEX Optimization Studio is an optimization software package.DRLdefinitiondefinitionsDRLDRLDRL is the default three letter identifier for Tivoli Decision Support for z/OS. It is used to reference various objects such as Framework Manager items and database objects.GSSPdefinitiondefinitionsGSSPGSSPIBM Getting Started Sub-Capacity Pricing (GSSP) for z/OS offers entry pricing for customers with select z/OS International Program License Agreement (IPLA) programs and the applicable Subscription and Support annual maintenance charge.Getting Started Sub-capacity Pricing helps you:Deploy small projects on z/OS with improved price/performanceProvide greater software licensing flexibility by improving alignment between software usage and software chargesIncrease LPAR capacity without changing software charges for a constant workloadTransition smoothly to traditional sub-capacity pricing as workload growsHCMdefinitiondefinitionsHCMHCMHCM is the default three letter identifier for CMA. It is used to reference various objects such as Framework Manager items and database objects.HISdefinitiondefinitionsHISHISHardware Instrumentation Services (HIS)—data collection of CPU metrics written to SMF 113 record.IPLAdefinitiondefinitionsIPLAIPLARelated to software pricing—IPLA programs have a one-time-charge (OTC) and an (optional) annual maintenance charge, called Subscription & Support. For more information, see the IBM z Systems Software Pricing page (www.ibm.com/systems/z/resources/swprice/zipla/index.html).IWPdefinitiondefinitionsIWPIWPIBM provides Integrated Workload Pricing (IWP) to enhance utilization reporting capabilities of systems that are running IBM z/OS, which can improve the price/performance for eligible Monthly License Charge (MLC) programs that are running within the same logical partition (LPAR) as select defining programs. These programs are listed in the Description section, Integrated Workload Pricing Defining Programs. The IWP enhancements to the reporting capabilities in z/OS enable the IBM Sub-capacity Reporting Tool (SCRT) to calculate the general-purpose processor time that is consumed by eligible defining programs and adjust the reported MSU values for eligible MLC programs that are running in the same LPAR for eligible Monthly License Charge IWP Adjusted Programs running within the same LPAR as select IWP Defining Programs.LPARdefinitiondefinitionsLPARLPARA Logical Partition on a physical server. Essentially LPAR is a virtual server.LWOdefinitiondefinitionsLWOLWOLPAR Weight Optimization—this feature has reports as well as a CPLEX model that optimizes the LPAR weight value to achieve fitting Target workload within the weight amount of MIPS.MIPSdefinitiondefinitionsMIPSMIPSWe use MIPS to represent the capacity of the zSeries frame or LPAR. Early metrics tended to concentrate on the rate at which a processor executes instructions to represent capacity. One metric of this type is MIPS (millions of instructions per processor second). Although MIPS is used as the term, the measure is not actually millions of instructions per processor second. The value is derived from numbers published by IBM in the Large System Performance Reference (LSPR) table. The default is to use the single image Processor Capacity Index (PCI) value with the average Relative Nest Intensity (RNI) for the hardware. For an LPAR, we divide the total MIPS capacity by the number of physical processors and then multiply by the number of processors defined to the LPAR. You can supply a MIPS table of your own, which might use the results from a zPCR study, which would be more accurate because it takes into account the workload characteristics.zPCR is a PC-based productivity tool under Windows. It is designed to provide capacity planning insight for IBM System z processors that are running various z/OS, z/VM, z/VSE, Linux, IBM zAware, and CFCC workload environments on partitioned hardware. Capacity results are based on IBM’s most recently published LSPR data for z/OS.For more information, see the Large Systems Performance Reference for IBM z Systems page (https://www.ibm.com/servers/resourcelink/lib03060.nsf/pages/lsprindex?OpenDocument).MLCdefinitiondefinitionsMLCMLCMonthly License Charge.For more information, see the IBM z Systems Software Pricing page (www.ibm.com/systems/z/resources/swprice/mlc/index.html).MSUdefinitiondefinitionsMSUMSUA million service units (MSU) are a measurement of the amount of processing work a computer can perform in one hour. MSU ratings are used for Software Pricing charges.NO89definitiondefinitionsNO89NO89Refers to software products that are priced using a sub-capacity pricing method. NO89 products do not generate SMF type 89 records, which report on usage by the product over a time interval.PEAKRANKdefinitiondefinitionsPEAKRANKPEAKRANKIn the Application peak value forecast stream, PEAKRANK is an input parameter that specifies which peak values you want the forecast to support. PWDdefinitiondefinitionsPWDPWDPasswordSCAdefinitiondefinitionsSCASCASoftware Cost Analysis.This feature reports and forecasts software cost based on Sub-Capacity Pricing for z/OS.UIDdefinitiondefinitionsUIDUIDUser ID.

IBM Capacity Management Analytics terminology

This glossary includes terms and definitions for IBM® Capacity Management Analytics.

AA

Application Analytics

This feature uses a user supplied application mapping table to provide reports by application, function, and environment.

APPL

Application

CMA

Capacity Management Analytics

CPC

Processor and CPU can refer to either the complete system box, or to one of the processors (CPUs) within the system box. Although the meaning might be clear from the context, even mainframe professionals must clarify which processor or CPU meaning they are using. IBM uses the term central processor complex (CPC) to refer to the physical collection of hardware that includes main storage, one or more central processors, timers, and channels. (Some system programmers use the term central electronic complex (CEC) to refer to the mainframe box, but the preferred term is CPC.)

CPLEX

IBM ILOG® CPLEX® Optimization Studio is an optimization software package.

DRL

DRL is the default three letter identifier for Tivoli® Decision Support for z/OS®. It is used to reference various objects such as Framework Manager items and database objects.

GSSP

IBM Getting Started Sub-Capacity Pricing (GSSP) for z/OS offers entry pricing for customers with select z/OS International Program License Agreement (IPLA) programs and the applicable Subscription and Support annual maintenance charge.

Getting Started Sub-capacity Pricing helps you:

Deploy small projects on z/OS with improved price/performance

Provide greater software licensing flexibility by improving alignment between software usage and software charges

Increase LPAR capacity without changing software charges for a constant workload

Transition smoothly to traditional sub-capacity pricing as workload grows

HCM

HCM is the default three letter identifier for CMA. It is used to reference various objects such as Framework Manager items and database objects.

HIS

Hardware Instrumentation Services (HIS)—data collection of CPU metrics written to SMF 113 record.

IPLA

Related to software pricing—IPLA programs have a one-time-charge (OTC) and an (optional) annual maintenance charge, called Subscription & Support. For more information, see the IBM z Systems™ Software Pricing page (www.ibm.com/systems/z/resources/swprice/zipla/index.html).

IWP

IBM provides Integrated Workload Pricing (IWP) to enhance utilization reporting capabilities of systems that are running IBM z™/OS, which can improve the price/performance for eligible Monthly License Charge (MLC) programs that are running within the same logical partition (LPAR) as select defining programs. These programs are listed in the Description section, Integrated Workload Pricing Defining Programs. The IWP enhancements to the reporting capabilities in z/OS enable the IBM Sub-capacity Reporting Tool (SCRT) to calculate the general-purpose processor time that is consumed by eligible defining programs and adjust the reported MSU values for eligible MLC programs that are running in the same LPAR for eligible Monthly License Charge IWP Adjusted Programs running within the same LPAR as select IWP Defining Programs.

LPAR

A Logical Partition on a physical server. Essentially LPAR is a virtual server.

LWO

LPAR Weight Optimization—this feature has reports as well as a CPLEX model that optimizes the LPAR weight value to achieve fitting Target workload within the weight amount of MIPS.

MIPS

We use MIPS to represent the capacity of the zSeries frame or LPAR. Early metrics tended to concentrate on the rate at which a processor executes instructions to represent capacity. One metric of this type is MIPS (millions of instructions per processor second). Although MIPS is used as the term, the measure is not actually millions of instructions per processor second. The value is derived from numbers published by IBM in the Large System Performance Reference (LSPR) table. The default is to use the single image Processor Capacity Index (PCI) value with the average Relative Nest Intensity (RNI) for the hardware. For an LPAR, we divide the total MIPS capacity by the number of physical processors and then multiply by the number of processors defined to the LPAR. You can supply a MIPS table of your own, which might use the results from a zPCR study, which would be more accurate because it takes into account the workload characteristics.

zPCR is a PC-based productivity tool under Windows. It is designed to provide capacity planning insight for IBM System z® processors that are running various z/OS, z/VM®, z/VSE®, Linux, IBM zAware, and CFCC workload environments on partitioned hardware. Capacity results are based on IBM’s most recently published LSPR data for z/OS.

For more information, see the Large Systems Performance Reference for IBM z Systems page (https://www.ibm.com/servers/resourcelink/lib03060.nsf/pages/lsprindex?OpenDocument).

MLC

Monthly License Charge.

For more information, see the IBM z Systems Software Pricing page (www.ibm.com/systems/z/resources/swprice/mlc/index.html).

MSU

A million service units (MSU) are a measurement of the amount of processing work a computer can perform in one hour. MSU ratings are used for Software Pricing charges.

NO89

Refers to software products that are priced using a sub-capacity pricing method. NO89 products do not generate SMF type 89 records, which report on usage by the product over a time interval.

PEAKRANK

In the Application peak value forecast stream, PEAKRANK is an input parameter that specifies which peak values you want the forecast to support.

PWD

Password

SCA

Software Cost Analysis.

This feature reports and forecasts software cost based on Sub-Capacity Pricing for z/OS.

UID

User ID.

TroubleshootingThese topics can help you troubleshoot problems and errors you encounter in IBM Capacity Management Analytics.General troubleshootingThis information can help you troubleshoot general problems you encounter while using IBM Capacity Management Analytics.SMO stream troubleshootingThis information can help you troubleshoot SMO stream problems and errors you encounter while using IBM Capacity Management Analytics.SCA stream troubleshootingThis information can help you troubleshoot SCA stream problems and errors you encounter while using IBM Capacity Management Analytics.Anomaly Detection troubleshootingThis information can help you troubleshoot Anomaly Detection stream problems and errors you encounter while using IBM Capacity Management Analytics.Solution Installer troubleshootingThis information can help you troubleshoot the Solution Installer.

Troubleshooting

These topics can help you troubleshoot problems and errors you encounter in IBM® Capacity Management Analytics.

General troubleshooting
This information can help you troubleshoot general problems you encounter while using IBM Capacity Management Analytics.

SMO stream troubleshooting
This information can help you troubleshoot SMO stream problems and errors you encounter while using IBM Capacity Management Analytics.

SCA stream troubleshooting
This information can help you troubleshoot SCA stream problems and errors you encounter while using IBM Capacity Management Analytics.

Anomaly Detection troubleshooting
This information can help you troubleshoot Anomaly Detection stream problems and errors you encounter while using IBM Capacity Management Analytics.

Solution Installer troubleshooting
This information can help you troubleshoot the Solution Installer.

General troubleshootingThis information can help you troubleshoot general problems you encounter while using IBM Capacity Management Analytics.troubleshootingHandling duplicate recordsBefore executing a Tivoli Decision Support for z/OS collect job, you should sort Service Management Facility (SMF) records to avoid duplicate records. For more information, see Tivoli Decision Support for z/OS General best practices ( https://www.ibm.com/developerworks/community/wikis/home/wiki/Tivoli+Decision+Support+for+zOS/page/General+best+practices). If you see duplicate record in your software cost analysis reports, please remove duplicate records in the MVSPM_PROD_T, MVSPM_PROD_INT_T and MVSPM_LPAR_MSU_T tables, sort the SMF records, then again execute a Tivoli Decision Support for z/OS collect job. TroubleshootingThese topics can help you troubleshoot problems and errors you encounter in IBM Capacity Management Analytics.Maintaining date format consistencyYou must ensure that date formats are consistent between SPSS Modeler streams and input files, such as holiday.csv.Tune IBM Cognos Business Intelligence for Capacity Management AnalyticsUse the following information to help you tune your IBM Cognos Business Intelligence report performance for IBM Capacity Management Analytics.Tune IBM SPSS Modeler for Capacity Management AnalyticsIBM Capacity Management Analytics performance can be improved by ensuring that SPSS Modeler is performing optimally.Error: AEQMJ0100E: Script error (Array index out of range: 1) on line 252 column 1This error occurs when you are running a stream but have a mismatch between the ENCODED parameter and whether you entered an encoded password for the PWD value.

General troubleshooting

This information can help you troubleshoot general problems you encounter while using IBM® Capacity Management Analytics.

Handling duplicate records

Before executing a Tivoli® Decision Support for z/OS® collect job, you should sort Service Management Facility (SMF) records to avoid duplicate records. For more information, see Tivoli Decision Support for z/OS General best practices ( https://www.ibm.com/developerworks/community/wikis/home/wiki/Tivoli+Decision+Support+for+zOS/page/General+best+practices).

If you see duplicate record in your software cost analysis reports, please remove duplicate records in the MVSPM_PROD_T, MVSPM_PROD_INT_T and MVSPM_LPAR_MSU_T tables, sort the SMF records, then again execute a Tivoli Decision Support for z/OS collect job.

Maintaining date format consistency
You must ensure that date formats are consistent between SPSS® Modeler streams and input files, such as holiday.csv.

Tune IBM Cognos Business Intelligence for Capacity Management Analytics
Use the following information to help you tune your IBM Cognos® Business Intelligence report performance for IBM Capacity Management Analytics.

Tune IBM SPSS Modeler for Capacity Management Analytics
IBM Capacity Management Analytics performance can be improved by ensuring that SPSS Modeler is performing optimally.

Error: AEQMJ0100E: Script error (Array index out of range: 1) on line 252 column 1
This error occurs when you are running a stream but have a mismatch between the ENCODED parameter and whether you entered an encoded password for the PWD value.

Parent topic: Troubleshooting

Maintaining date format consistencyYou must ensure that date formats are consistent between SPSS Modeler streams and input files, such as holiday.csv. For example, if you use Microsoft Excel to edit csv files and your computer uses a different locale than what is used in the SPSS Modeler streams, you must ensure that the data format in the csv remains consistent with date format that is used in the streams. Microsoft Excel can change the locale that is used in the file to match your computer's locale.General troubleshootingThis information can help you troubleshoot general problems you encounter while using IBM Capacity Management Analytics.

Maintaining date format consistency

You must ensure that date formats are consistent between SPSS® Modeler streams and input files, such as holiday.csv.

For example, if you use Microsoft Excel to edit csv files and your computer uses a different locale than what is used in the SPSS Modeler streams, you must ensure that the data format in the csv remains consistent with date format that is used in the streams. Microsoft Excel can change the locale that is used in the file to match your computer's locale.

Parent topic: General troubleshooting

Tune IBM Cognos Business Intelligence for Capacity Management AnalyticsUse the following information to help you tune your IBM Cognos Business Intelligence report performance for IBM Capacity Management Analytics.The following factors can influence IBM Cognos report performance:Peak system usage periodsNumber of concurrent users the system can supportNumber of concurrent users expectedAcceptable response times for the systemSize of the data that is being transferred and the processing capacity of the database serverYou can also use cached data when you run report to performance improvements.For more performance tuning information, see the IBM Cognos Business Intelligence Server documentation on IBM Knowledge Center (www.ibm.com/support/knowledgecenter/SSEP7J_10.2.2/com.ibm.swg.ba.cognos.crn_arch.10.2.2.doc/c_arch_performancetuning.html%23arch_PerformanceTuning)General troubleshootingThis information can help you troubleshoot general problems you encounter while using IBM Capacity Management Analytics.

Tune IBM Cognos Business Intelligence for Capacity Management Analytics

Use the following information to help you tune your IBM® Cognos® Business Intelligence report performance for IBM Capacity Management Analytics.

The following factors can influence IBM Cognos report performance:

Peak system usage periods

Number of concurrent users the system can support

Number of concurrent users expected

Acceptable response times for the system

Size of the data that is being transferred and the processing capacity of the database server

You can also use cached data when you run report to performance improvements.

For more performance tuning information, see the IBM Cognos Business Intelligence Server documentation on IBM Knowledge Center (www.ibm.com/support/knowledgecenter/SSEP7J_10.2.2/com.ibm.swg.ba.cognos.crn_arch.10.2.2.doc/c_arch_performancetuning.html%23arch_PerformanceTuning)

Parent topic: General troubleshooting

Tune IBM SPSS Modeler for Capacity Management AnalyticsIBM Capacity Management Analytics performance can be improved by ensuring that SPSS Modeler is performing optimally.For more performance tuning information, see the IBM SPSS Modeler documentation on IBM Knowledge Center (www.ibm.com/support/knowledgecenter/SS3RA7_17.0.0/clementine/server/adminguidesource/admin_perf_optimize.dita).General troubleshootingThis information can help you troubleshoot general problems you encounter while using IBM Capacity Management Analytics.

Tune IBM SPSS Modeler for Capacity Management Analytics

IBM® Capacity Management Analytics performance can be improved by ensuring that SPSS® Modeler is performing optimally.

For more performance tuning information, see the IBM SPSS Modeler documentation on IBM Knowledge Center (www.ibm.com/support/knowledgecenter/SS3RA7_17.0.0/clementine/server/adminguidesource/admin_perf_optimize.dita).

Parent topic: General troubleshooting

Error: AEQMJ0100E: Script error (Array index out of range: 1) on line 252 column 1This error occurs when you are running a stream but have a mismatch between the ENCODED parameter and whether you entered an encoded password for the PWD value.For example, if you set the ENCODED value in a parameter.txt file to true, but you did not enter an encoded password in PWD, then this error occurs.General troubleshootingThis information can help you troubleshoot general problems you encounter while using IBM Capacity Management Analytics.

Error: AEQMJ0100E: Script error (Array index out of range: 1) on line 252 column 1

This error occurs when you are running a stream but have a mismatch between the ENCODED parameter and whether you entered an encoded password for the PWD value.

For example, if you set the ENCODED value in a parameter.txt file to true, but you did not enter an encoded password in PWD, then this error occurs.

Parent topic: General troubleshooting

SMO stream troubleshootingThis information can help you troubleshoot SMO stream problems and errors you encounter while using IBM Capacity Management Analytics. You can find error log files in the log folder that you specify with the LOGPATH parameter or CMAINSTANCE/data. The log file names consist of the stream name and a timestamp.troubleshootingETL streamsTroubleshooting distribution_procedure.str The ETL stream distribution_procedure.str reports the following error: AEQMJ0142E: cannot concatenate 'str' and {1} objects You can resolve this in IBM SPSS Modeler Client by clicking ToolsStream PropertiesParameters, and changing the storage of stream parameters start_date and end_date from Date to String.TroubleshootingThese topics can help you troubleshoot problems and errors you encounter in IBM Capacity Management Analytics.

SMO stream troubleshooting

This information can help you troubleshoot SMO stream problems and errors you encounter while using IBM® Capacity Management Analytics.

You can find error log files in the log folder that you specify with the LOGPATH parameter or CMAINSTANCE/data. The log file names consist of the stream name and a timestamp.

Troubleshooting distribution_procedure.str

The ETL stream distribution_procedure.str reports the following error:

AEQMJ0142E: cannot concatenate 'str' and {1} objects

You can resolve this in IBM SPSS® Modeler Client by clicking Tools > Stream Properties > Parameters, and changing the storage of stream parameters start_date and end_date from Date to String.

Parent topic: Troubleshooting

SCA stream troubleshootingThis information can help you troubleshoot SCA stream problems and errors you encounter while using IBM Capacity Management Analytics.troubleshootingsca streamsTroubleshooting SCA forecast streamsYou can find the following error log files for the SCA forecast streams in the log folder that you specify with the LOGPATH parameter. msu_prod_forecast_timeseries_<timestamp>.log for the product forecast stream.msu_gssp_forecast_timeseries_<timestamp>.log for the GSSP forecast stream.The log files may contain the following errors:not enough data errorIf the product in one LPAR of one CPC doesn't have 24 hours of data in any day of the week, the log file will contain the message Do not have 24 hours data in one day. You can enter the missing values according to your SMF data in the database to correct this error.null value errorIf the product in one LPAR of one CPC contains only null values in any day of week, the log file will contain the message Contained null value. You can fill in the null values in the database to correct this error.not enough data and null value messagIf the product in one LPAR of one CPC contains only null values in any day of week and doesn't have 24 hours of data in any day of the week, the log file will contain with message Contained null value and do not have 24 hours data in one day. You can fill in the null values and enter the missing values in the database to correct this error.Troubleshooting SCA optimization streams For any of the following situations, there should be one or more error messages in the log file named prod_bill_msu_optimization_xxx.log, where xxx is the timestamp when the log is created: If you cannot get an optimized result.If the stream is interrupted. The location of the log is specified by the LOGPATH parameter. If you run the stream in batch mode, LOGPATH is set in the file named parameter.txt. If you run the stream in client mode, LOGPATH is set in the parameter input window. For more information about the error messages, see Error, warning, and installation messagesError, warning, and installation messages are displayed for IBM Capacity Management Analytics.. In addition to the previous errors, in some cases you might receive an optimized result containing the following errors:Didn’t find the parent program in the LPAR where child program locates after optimizationConfirm that the program is configured in the bounded_prod file in opt_bound_prod.csv. This can be found in the Configuration chapter. The program is reserved in a certain LPAR, but it is located in other LPARs after the optimizationThere are two possible causes for this error:The LPAR that the program reserved may not suitable, already out of capacity, or be configured with a very small capacity definition. You should try to increase the capacity for this LPAR or change to another LPAR to reserve this product. The program is a child program and set to be in a bound group, and it also set to be reserved in an LPAR, but the parent program is not reserved. In this case you should also reserve the parent program in that LPAR.The program is reserved in a certain LPAR, but it is still in the original LPAR after the optimizationTry to decrease the reserved amount. If the reserved amount is greater than the actual MSU load of the program, the optimization ignores this reservation and keep the program in its original location.Troubleshooting sca_no89.str If this stream returns a no data source error, ensure that the NO89 file is in the location specified by the NO89_FILE parameter. Additionally, ensure that the NO89 file contains the correct start and end strings. The file should start with //NO89 DD * NON-89 PRODUCT SECTION and end with //SYSLIN DD *.If the stream fails with a merge error, verify that the HCM.MVSPM_LPAR and HCM. SUBCAP_PROGRAMS HCM tables contain all installed product information and LPAR information. To insert correct data into these tables, execute the mvspm_lpar_insert.str, mvspm_lpar_update.str, and sca_subcap.str streams in sequence.If this stream returns a table not found error, verify that the HCM.NO89_PRODUCTS table exists in your HCM database.Troubleshooting sca_pricing.str If this stream returns a no data source error, ensure that the mlc_prod.csv, ipla_prod.csv, enterprise.csv, and currency.csv files are in the CMAINSTANCE/data directory.If the stream fails with a merge error, verify that the SUBCAP_PROGRAMS, MSU_TIERS and VUE_TIERS HCM tables contain all installed product information, MSU tier and VU tier. To insert correct data into these tables, execute the sca_subcap.str and sca_tiers.str streams. If this stream returns a table not found error, verify that the HCM.CUSTOMER_PRICE table exists in your HCM database.Troubleshooting sca_tiers.str If this stream returns a no data source error, ensure that the u_tiers.csv and msu_tiers.csv files are in the CMAINSTANCE/data directory.If this stream returns a table not found error, verify that the HCM.VUE_TIERS and HCM.MSU_TIERS tables exists in your HCM database.Troubleshooting sca_subcap.str If this stream returns a no data source error, ensure that the gssp_scaling_factors.csv, gssp.csv, iwpadjust.csv, iwpdefine.csv, mlc.csv, parent.csv, , referencebased.csv, and zos_based.csv files are in the CMA_ROOT/streams/data directory.If this stream returns a table not found error, verify that the HCM.SUBCAP_PROGRAMS table exists in your HCM database.TroubleshootingThese topics can help you troubleshoot problems and errors you encounter in IBM Capacity Management Analytics.

SCA stream troubleshooting

This information can help you troubleshoot SCA stream problems and errors you encounter while using IBM® Capacity Management Analytics.

Troubleshooting SCA forecast streams

You can find the following error log files for the SCA forecast streams in the log folder that you specify with the LOGPATH parameter.

msu_prod_forecast_timeseries_<timestamp>.log for the product forecast stream.

msu_gssp_forecast_timeseries_<timestamp>.log for the GSSP forecast stream.

The log files may contain the following errors:

not enough data error

If the product in one LPAR of one CPC doesn't have 24 hours of data in any day of the week, the log file will contain the message Do not have 24 hours data in one day. You can enter the missing values according to your SMF data in the database to correct this error.

null value error

If the product in one LPAR of one CPC contains only null values in any day of week, the log file will contain the message Contained null value. You can fill in the null values in the database to correct this error.

not enough data and null value messag

If the product in one LPAR of one CPC contains only null values in any day of week and doesn't have 24 hours of data in any day of the week, the log file will contain with message Contained null value and do not have 24 hours data in one day. You can fill in the null values and enter the missing values in the database to correct this error.

Troubleshooting SCA optimization streams

For any of the following situations, there should be one or more error messages in the log file named prod_bill_msu_optimization_xxx.log, where xxx is the timestamp when the log is created:

If you cannot get an optimized result.

If the stream is interrupted.

The location of the log is specified by the LOGPATH parameter. If you run the stream in batch mode, LOGPATH is set in the file named parameter.txt. If you run the stream in client mode, LOGPATH is set in the parameter input window.

For more information about the error messages, see Error, warning, and installation messages.

In addition to the previous errors, in some cases you might receive an optimized result containing the following errors:

Didn’t find the parent program in the LPAR where child program locates after optimization

Confirm that the program is configured in the bounded_prod file in opt_bound_prod.csv. This can be found in the Configuration chapter.

The program is reserved in a certain LPAR, but it is located in other LPARs after the optimization

There are two possible causes for this error:

The LPAR that the program reserved may not suitable, already out of capacity, or be configured with a very small capacity definition. You should try to increase the capacity for this LPAR or change to another LPAR to reserve this product.

The program is a child program and set to be in a bound group, and it also set to be reserved in an LPAR, but the parent program is not reserved. In this case you should also reserve the parent program in that LPAR.

The program is reserved in a certain LPAR, but it is still in the original LPAR after the optimization

Try to decrease the reserved amount. If the reserved amount is greater than the actual MSU load of the program, the optimization ignores this reservation and keep the program in its original location.

Troubleshooting sca_no89.str

If this stream returns a no data source error, ensure that the NO89 file is in the location specified by the NO89_FILE parameter. Additionally, ensure that the NO89 file contains the correct start and end strings. The file should start with //NO89 DD * NON-89 PRODUCT SECTION and end with //SYSLIN DD *.

If the stream fails with a merge error, verify that the HCM.MVSPM_LPAR and HCM. SUBCAP_PROGRAMS HCM tables contain all installed product information and LPAR information. To insert correct data into these tables, execute the mvspm_lpar_insert.str, mvspm_lpar_update.str, and sca_subcap.str streams in sequence.

If this stream returns a table not found error, verify that the HCM.NO89_PRODUCTS table exists in your HCM database.

Troubleshooting sca_pricing.str

If this stream returns a no data source error, ensure that the mlc_prod.csv, ipla_prod.csv, enterprise.csv, and currency.csv files are in the CMAINSTANCE/data directory.

If the stream fails with a merge error, verify that the SUBCAP_PROGRAMS, MSU_TIERS and VUE_TIERS HCM tables contain all installed product information, MSU tier and VU tier. To insert correct data into these tables, execute the sca_subcap.str and sca_tiers.str streams.

If this stream returns a table not found error, verify that the HCM.CUSTOMER_PRICE table exists in your HCM database.

Troubleshooting sca_tiers.str

If this stream returns a no data source error, ensure that the u_tiers.csv and msu_tiers.csv files are in the CMAINSTANCE/data directory.

If this stream returns a table not found error, verify that the HCM.VUE_TIERS and HCM.MSU_TIERS tables exists in your HCM database.

Troubleshooting sca_subcap.str

If this stream returns a no data source error, ensure that the gssp_scaling_factors.csv, gssp.csv, iwpadjust.csv, iwpdefine.csv, mlc.csv, parent.csv, , referencebased.csv, and zos_based.csv files are in the CMA_ROOT/streams/data directory.

If this stream returns a table not found error, verify that the HCM.SUBCAP_PROGRAMS table exists in your HCM database.

Parent topic: Troubleshooting

Anomaly Detection troubleshootingThis information can help you troubleshoot Anomaly Detection stream problems and errors you encounter while using IBM Capacity Management Analytics.troubleshootingAnomaly Detection streamsTroubleshooting the model building streamYou can find the Modeler stream log files in the log folder that you specify with the LOGPATH parameter. anomaly_detect_building_c_<timestamp>.log for the response stream.anomaly_detect_building_r_<timestamp>.log for the CPU stream. The log files may contain the following errors:Not enough data errorModel building streams generate errors when there is not enough data. If you have a log entry such as XXXX doesn't have enough data for model building, where XXXX is the TRANSACTION_ID, you need to check the data for that specified transaction to determine how many records exist between the start and end date you specified for model building. Capacity Management Analytics requires at least 40 records for model building. Large data exceed memory errorModel building streams generate errors when the amount of data used for model building is larger than the memory capacity of your machine. In this case, you will see a log entry such as XXXX has exceed data limitation for model building, which is xxxxx. Please sample your data before you build the model. In this log entry, XXXX is TRANSACTION_ID, and xxxxx is the record number limitation for model building. There are two ways of solving this problem: you can either sample your data before you build the model or reduce the range for your model building input data. Troubleshooting the model scoring streamThis stream writes errors to the anomaly_detect_scoring_<timestamp>.log.Not enough data error:The model scoring stream generates errors when your input data for model scoring is empty. In this case, you will see a log entry such as XXXX doesn't have enough data for model scoring, where XXXX is TRANSACTION_ID. You should ensure that the date set for scoring in date_info.csv and transaction.csv are valid, and that your database contains the data want to score. Troubleshooting the classification stream This stream writes errors to the anomaly_classification_<timestamp>.log. No scoring result for both CPU and RESPONSE modelThe classification stream generates errors when you don't have any scoring result from both the CPU model and RESPONSE model in database. In this case, you will see a log entry such as Didn't find any cpu and response scoring results in database, please run cpu and response scoring before executing the classification stream. To resolve this issue, must score both the CPU and REPONSE models, and store the results in the database.No scoring result for CPU modelThe classification stream generates errors when you don't have any scoring result from the CPU model. In this case, you will see a log entry such as Didn't find any cpu scoring results in database, please run cpu scoring before executing the classification stream. To resolve this issue, you need to score the CPU model and store the result to the database before executing the Classification stream.No scoring result for RESPONSE modelThe classification stream generates errors when you don't have any scoring result from the RESPONSE model. In this case, you will see a log entry such as Didn't find any response scoring results in database, please run response scoring before execute classification stream. To resolve this issue, you need to score the RESPONSE model and store the result to the database before executing Classification streamCPU result and RESPONSE result doesn't match each otherThe classification stream generates errors when CPU and RESPONSE don't have any intersection data. In this case, you will see a log entry such as CPU and RESPONSE scoring results don't have intersection data to classify results. .To resolve this issue, you need to make sure the CPU and RESPONSE results in the database are consistent with each other. For any given transaction, both CPU and RESPONSE scoring results must exist.TroubleshootingThese topics can help you troubleshoot problems and errors you encounter in IBM Capacity Management Analytics.

Anomaly Detection troubleshooting

This information can help you troubleshoot Anomaly Detection stream problems and errors you encounter while using IBM® Capacity Management Analytics.

Troubleshooting the model building stream

You can find the Modeler stream log files in the log folder that you specify with the LOGPATH parameter.

anomaly_detect_building_c_<timestamp>.log for the response stream.

anomaly_detect_building_r_<timestamp>.log for the CPU stream.

The log files may contain the following errors:

Not enough data error

Model building streams generate errors when there is not enough data. If you have a log entry such as XXXX doesn't have enough data for model building, where XXXX is the TRANSACTION_ID, you need to check the data for that specified transaction to determine how many records exist between the start and end date you specified for model building. Capacity Management Analytics requires at least 40 records for model building.

Large data exceed memory error

Model building streams generate errors when the amount of data used for model building is larger than the memory capacity of your machine. In this case, you will see a log entry such as XXXX has exceed data limitation for model building, which is xxxxx. Please sample your data before you build the model. In this log entry, XXXX is TRANSACTION_ID, and xxxxx is the record number limitation for model building. There are two ways of solving this problem: you can either sample your data before you build the model or reduce the range for your model building input data.

Troubleshooting the model scoring stream

This stream writes errors to the anomaly_detect_scoring_<timestamp>.log.

Not enough data error:

The model scoring stream generates errors when your input data for model scoring is empty. In this case, you will see a log entry such as XXXX doesn't have enough data for model scoring, where XXXX is TRANSACTION_ID. You should ensure that the date set for scoring in date_info.csv and transaction.csv are valid, and that your database contains the data want to score.

Troubleshooting the classification stream

This stream writes errors to the anomaly_classification_<timestamp>.log.

No scoring result for both CPU and RESPONSE model

The classification stream generates errors when you don't have any scoring result from both the CPU model and RESPONSE model in database. In this case, you will see a log entry such as Didn't find any cpu and response scoring results in database, please run cpu and response scoring before executing the classification stream. To resolve this issue, must score both the CPU and REPONSE models, and store the results in the database.

No scoring result for CPU model

The classification stream generates errors when you don't have any scoring result from the CPU model. In this case, you will see a log entry such as Didn't find any cpu scoring results in database, please run cpu scoring before executing the classification stream. To resolve this issue, you need to score the CPU model and store the result to the database before executing the Classification stream.

No scoring result for RESPONSE model

The classification stream generates errors when you don't have any scoring result from the RESPONSE model. In this case, you will see a log entry such as Didn't find any response scoring results in database, please run response scoring before execute classification stream. To resolve this issue, you need to score the RESPONSE model and store the result to the database before executing Classification stream

CPU result and RESPONSE result doesn't match each other

The classification stream generates errors when CPU and RESPONSE don't have any intersection data. In this case, you will see a log entry such as CPU and RESPONSE scoring results don't have intersection data to classify results. .To resolve this issue, you need to make sure the CPU and RESPONSE results in the database are consistent with each other. For any given transaction, both CPU and RESPONSE scoring results must exist.

Parent topic: Troubleshooting

Solution Installer troubleshootingThis information can help you troubleshoot the Solution Installer.troubleshootingSolution InstallerTo update the database alias, you must first manually remove the existing one.Uncatalog DB2 alias:uncatalog db2 node <CMADBALIAS>uncatalog db2 database <CMADBALIAS>Clean up the CMADBALIAS value in the DB2HOME/cfg/db2cli.ini file, remove the section for the CMADBALIAS. For example: [CMA_CMAD] Hostname=myserver.mydomain.com Port=1234 Protocol=TCPIP DriverUnicodeType=1 Database=CMADCreate the alias with the correct information by executing cmainstance.py or cmahome.py.For more information about cmainstance.py and cmahome.py, see Manually installing IBM Capacity Management Analytics 2.1.1If you want to run IBM Capacity Management Analyticson a platform other than 64-bit Red Hat Enterprise Linux Server Edition version 6 and 7, you must install the product manually..TroubleshootingThese topics can help you troubleshoot problems and errors you encounter in IBM Capacity Management Analytics.

Solution Installer troubleshooting

This information can help you troubleshoot the Solution Installer.

About this task

To update the database alias, you must first manually remove the existing one.

Procedure

Uncatalog DB2 alias:

uncatalog db2 node <CMADBALIAS>

uncatalog db2 database <CMADBALIAS>

Clean up the CMADBALIAS value in the DB2HOME/cfg/db2cli.ini file, remove the section for the CMADBALIAS.

For example:

[CMA_CMAD]

Hostname=myserver.mydomain.com

Port=1234

Protocol=TCPIP

DriverUnicodeType=1

Database=CMAD

Create the alias with the correct information by executing cmainstance.py or cmahome.py.

For more information about cmainstance.py and cmahome.py, see Manually installing IBM Capacity Management Analytics 2.1.1.

Parent topic: Troubleshooting

NoticesThis information was developed for products and services offered worldwide.This material may be available from IBM in other languages. However, you may be required to own a copy of the product or product version in that language in order to access it. IBM may not offer the products, services, or features discussed in this document in other countries. Consult your local IBM representative for information on the products and services currently available in your area. Any reference to an IBM product, program, or service is not intended to state or imply that only that IBM product, program, or service may be used. Any functionally equivalent product, program, or service that does not infringe any IBM intellectual property right may be used instead. However, it is the user's responsibility to evaluate and verify the operation of any non-IBM product, program, or service. This document may describe products, services, or features that are not included in the Program or license entitlement that you have purchased. IBM may have patents or pending patent applications covering subject matter described in this document. The furnishing of this document does not grant you any license to these patents. You can send license inquiries, in writing, to: IBM Director of Licensing IBM Corporation North Castle Drive Armonk, NY 10504-1785 U.S.A. For license inquiries regarding double-byte (DBCS) information, contact the IBM Intellectual Property Department in your country or send inquiries, in writing, to: Intellectual Property Licensing Legal and Intellectual Property Law IBM Japan Ltd. 19-21, Nihonbashi-Hakozakicho, Chuo-ku Tokyo 103-8510, Japan The following paragraph does not apply to the United Kingdom or any other country where such provisions are inconsistent with local law: INTERNATIONAL BUSINESS MACHINES CORPORATION PROVIDES THIS PUBLICATION "AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF NON-INFRINGEMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Some states do not allow disclaimer of express or implied warranties in certain transactions, therefore, this statement may not apply to you. This information could include technical inaccuracies or typographical errors. Changes are periodically made to the information herein; these changes will be incorporated in new editions of the publication. IBM may make improvements and/or changes in the product(s) and/or the program(s) described in this publication at any time without notice. Any references in this information to non-IBM Web sites are provided for convenience only and do not in any manner serve as an endorsement of those Web sites. The materials at those Web sites are not part of the materials for this IBM product and use of those Web sites is at your own risk. IBM may use or distribute any of the information you supply in any way it believes appropriate without incurring any obligation to you.Licensees of this program who wish to have information about it for the purpose of enabling: (i) the exchange of information between independently created programs and other programs (including this one) and (ii) the mutual use of the information which has been exchanged, should contact: IBM Software Group Attention: Licensing 3755 Riverside Dr. Ottawa, ON K1V 1B7 Canada Such information may be available, subject to appropriate terms and conditions, including in some cases, payment of a fee. The licensed program described in this document and all licensed material available for it are provided by IBM under terms of the IBM Customer Agreement, IBM International Program License Agreement or any equivalent agreement between us.Any performance data contained herein was determined in a controlled environment. Therefore, the results obtained in other operating environments may vary significantly. Some measurements may have been made on development-level systems and there is no guarantee that these measurements will be the same on generally available systems. Furthermore, some measurements may have been estimated through extrapolation. Actual results may vary. Users of this document should verify the applicable data for their specific environment. Information concerning non-IBM products was obtained from the suppliers of those products, their published announcements or other publicly available sources. IBM has not tested those products and cannot confirm the accuracy of performance, compatibility or any other claims related to non-IBM products. Questions on the capabilities of non-IBM products should be addressed to the suppliers of those products. All statements regarding IBM's future direction or intent are subject to change or withdrawal without notice, and represent goals and objectives only. This information contains examples of data and reports used in daily business operations. To illustrate them as completely as possible, the examples include the names of individuals, companies, brands, and products. All of these names are fictitious and any similarity to the names and addresses used by an actual business enterprise is entirely coincidental. If you are viewing this information softcopy, the photographs and color illustrations may not appear.This Software Offering does not use cookies or other technologies to collect personally identifiable information.©

Notices

This information was developed for products and services offered worldwide.

This material may be available from IBM in other languages. However, you may be required to own a copy of the product or product version in that language in order to access it.

IBM may not offer the products, services, or features discussed in this document in other countries. Consult your local IBM representative for information on the products and services currently available in your area. Any reference to an IBM product, program, or service is not intended to state or imply that only that IBM product, program, or service may be used. Any functionally equivalent product, program, or service that does not infringe any IBM intellectual property right may be used instead. However, it is the user's responsibility to evaluate and verify the operation of any non-IBM product, program, or service. This document may describe products, services, or features that are not included in the Program or license entitlement that you have purchased.

IBM may have patents or pending patent applications covering subject matter described in this document. The furnishing of this document does not grant you any license to these patents. You can send license inquiries, in writing, to:

IBM Director of Licensing

IBM Corporation

North Castle Drive

Armonk, NY 10504-1785

U.S.A.

For license inquiries regarding double-byte (DBCS) information, contact the IBM Intellectual Property Department in your country or send inquiries, in writing, to:

Intellectual Property Licensing

Legal and Intellectual Property Law

IBM Japan Ltd.

19-21, Nihonbashi-Hakozakicho, Chuo-ku

Tokyo 103-8510, Japan

The following paragraph does not apply to the United Kingdom or any other country where such provisions are inconsistent with local law: INTERNATIONAL BUSINESS MACHINES CORPORATION PROVIDES THIS PUBLICATION "AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF NON-INFRINGEMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Some states do not allow disclaimer of express or implied warranties in certain transactions, therefore, this statement may not apply to you.

This information could include technical inaccuracies or typographical errors. Changes are periodically made to the information herein; these changes will be incorporated in new editions of the publication. IBM may make improvements and/or changes in the product(s) and/or the program(s) described in this publication at any time without notice.

Any references in this information to non-IBM Web sites are provided for convenience only and do not in any manner serve as an endorsement of those Web sites. The materials at those Web sites are not part of the materials for this IBM product and use of those Web sites is at your own risk.

IBM may use or distribute any of the information you supply in any way it believes appropriate without incurring any obligation to you.

Licensees of this program who wish to have information about it for the purpose of enabling: (i) the exchange of information between independently created programs and other programs (including this one) and (ii) the mutual use of the information which has been exchanged, should contact:

IBM Software Group

Attention: Licensing

3755 Riverside Dr.

Ottawa, ON

K1V 1B7

Canada

Such information may be available, subject to appropriate terms and conditions, including in some cases, payment of a fee.

The licensed program described in this document and all licensed material available for it are provided by IBM under terms of the IBM Customer Agreement, IBM International Program License Agreement or any equivalent agreement between us.

Any performance data contained herein was determined in a controlled environment. Therefore, the results obtained in other operating environments may vary significantly. Some measurements may have been made on development-level systems and there is no guarantee that these measurements will be the same on generally available systems. Furthermore, some measurements may have been estimated through extrapolation. Actual results may vary. Users of this document should verify the applicable data for their specific environment.

Information concerning non-IBM products was obtained from the suppliers of those products, their published announcements or other publicly available sources. IBM has not tested those products and cannot confirm the accuracy of performance, compatibility or any other claims related to non-IBM products. Questions on the capabilities of non-IBM products should be addressed to the suppliers of those products.

All statements regarding IBM's future direction or intent are subject to change or withdrawal without notice, and represent goals and objectives only.

This information contains examples of data and reports used in daily business operations. To illustrate them as completely as possible, the examples include the names of individuals, companies, brands, and products. All of these names are fictitious and any similarity to the names and addresses used by an actual business enterprise is entirely coincidental.

If you are viewing this information softcopy, the photographs and color illustrations may not appear.

This Software Offering does not use cookies or other technologies to collect personally identifiable information.

©

© Product Information This document applies to IBM Capacity Management Analytics Version 2.1.1 and may also apply to subsequent releases. Copyright Licensed Materials - Property of IBM © Copyright IBM Corp. 2013, 2016. US Government Users Restricted Rights – Use, duplication or disclosure restricted by GSA ADP Schedule Contract with IBM Corp. IBM, the IBM logo and ibm.com are trademarks or registered trademarks of International Business Machines Corp., registered in many jurisdictions worldwide. Other product and service names might be trademarks of IBM or other companies. A current list of IBM trademarks is available on the Web at Copyright and trademark information at www.ibm.com/legal/copytrade.shtml. The following terms are trademarks or registered trademarks of other companies: Microsoft, Windows, Windows NT, and the Windows logo are trademarks of Microsoft Corporation in the United States, other countries, or both.Linux is a registered trademark of Linus Torvalds in the United States, other countries, or both.UNIX is a registered trademark of The Open Group in the United States and other countries. Notices

©

Product Information

This document applies to IBM Capacity Management Analytics Version 2.1.1 and may also apply to subsequent releases.

Copyright

Licensed Materials - Property of IBM

© Copyright IBM Corp. 2013, 2016.

US Government Users Restricted Rights – Use, duplication or disclosure restricted by GSA ADP Schedule Contract with IBM Corp.

IBM, the IBM logo and ibm.com are trademarks or registered trademarks of International Business Machines Corp., registered in many jurisdictions worldwide. Other product and service names might be trademarks of IBM or other companies. A current list of IBM trademarks is available on the Web at " Copyright and trademark information " at www.ibm.com/legal/copytrade.shtml.

The following terms are trademarks or registered trademarks of other companies:

Microsoft, Windows, Windows NT, and the Windows logo are trademarks of Microsoft Corporation in the United States, other countries, or both.

Linux is a registered trademark of Linus Torvalds in the United States, other countries, or both.

UNIX is a registered trademark of The Open Group in the United States and other countries.

Parent topic: Notices