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Stay Informed With IBM Z Decision Support

IBM Z Decision Support provides over 1,400 reports covering all aspects of performance management, capacity planning and accounting.

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How many times are you called upon to make a decision quickly? It might be an easy decision or a tough one. In any case, wouldn’t you like to have some level of support to be more comfortable with the choice you’re going to make? Experience is always a great source for making proper decisions, but it’s even better to have information to help you in the decision-making process and support your conclusion. Credible data can increase confidence in a decision, but it can also demonstrate the rationality of your decision to whoever your decision impacts.

The importance of informed decisions applies to many different areas and disciplines, including the management and governance of a system like IBM Z. IBM Z has information about system performance, resource usage and more, but finding the data you need for making decisions quickly isn’t always easy. There’s so much data, and so many sources, so it can be difficult to find the right data and bring out the value you need, when you need it.

Making Decisions With IBM Z Decision Support

That's where IBM Z Decision Support (formerly known as IBM Tivoli Decision Support for z/OS) comes in. IBM Z Decision Support is designed to help you understand your performance challenges by collecting raw systems metrics data, consolidating it in an IBM Db2 for z/OS repository.

IBM Z Decision Support provides over 1,400 reports covering all aspects of performance management, capacity planning and accounting. These reports provide the user with aggregated and curated data. The product captures the right information from the raw data and provides the necessary math to get value from this data, enabling end users to easily transform data into useful information. These reports, along with trends of resource consumption, and system and subsystem performance are easily accessible.

IBM Z Decision Support provides the following reports, and more:

  • Coupling facility statistics
  • CICS transactions
  • Db2 transactions
  • Device statistics
  • IMS statistics
  • Interval job step accounting
  • LPAR statistics
  • System statistics
  • TCP/IP server connections
  • DFSMS volume information
  • Workload statistics
  • Linux on IBM Z statistics

IBM Z Decision Support’s Key Performance Metric exception reporting allows users to easily identify exceptional behaviors. It contains built-in exception reporting, where any metric that crosses a pre-defined threshold will be written to an exception table for easy reporting and investigation. Default threshold values are then provided, and can easily be modified in a lookup table.

A Complete View of Resource Utilization

Once you have a complete view of the performance of your systems and subsystems, it’s also useful see which applications are causing resource utilization, and which departments are triggering the resource consumption. This kind of data is useful for accounting and charge-back as well as for capacity planning activities.

IBM Z Decision Support provides the end user with a complete view of resource utilization, and collects raw system data that can be enriched with logical data meaningful for your organization. For example, it can link transactions initiated by a specific user to the department the user belongs to, providing users with a department-level view of resource utilization.

Enhancing an Already Robust Tool

This past July, IBM announced a new version of Tivoli Decision Support for z/OS. In this release, the product name was changed to IBM Z Decision Support—a change that was just a small indication of a larger re-vitalization of the product itself.

The features of IBM Z Decision Support V1.9.0 include:

  • A new Java agent to collect raw systems metrics data in near real time, removing the need for a manual step for data collection. The Java agent sends collected data to the IBM Z Decision Support engine.
  • Aggregation and curation of raw system data collected that’s now performed as data arrives, avoiding long batch processing once per day, which could cause CPU consumption peaks.
  • Integration with IBM Common Data Provider, allowing users to stream IBM Z Decision Support curated data to the analytic engines like Splunk, Elastic Stack and so on.
  • Valuable Splunk dashboards, based on curated IBM Z Decision Support, which will be available soon.
  • Continuous SMF data collection, curation and reporting while reducing 4HRA impact.
  • Reduced peak batch MSU loads
  • Automated data gathering by a new IBM Z Decision Support agent, which utilizes a real-time SMF collector to extract SMF data
  • New, Java-based Data Mover technology to transport SMF data to IBM Z Decision Support for processing.

Continuous collection and curation spreads the load of the data collation part of the product throughout the day, so users don’t have to run high MSU batch jobs to process each day’s SMF data. This significantly reduces the impact of processing the SMF data upon your four-hour rolling average. Another added benefit of continuous data curation is that users can now run reports against each day’s data. With earlier releases, users had to wait until the day’s data had been processed in batch overnight before viewing the reports. With continuous processing, new SMF-based data is regularly published to Db2 as it becomes available throughout the day and can be viewed in reports immediately.

Optimizing Costs and Enabling IT Modernization

IBM Tivoli Decision Support for z/OS V1.8.2, along with the new IBM Z Decision Support V1.9.0 provide useful tools for two key areas of enhancement:

1. Cost Optimization

Reduced CPU consumption achieved through improved data collection capabilities often resulted in cost optimization. There are four new subcomponents within the existing components:

  • The z/OS key metrics subcomponent (part of the existing system performance feature)
  • The Db2 key metrics subcomponent (part of the existing system performance feature)
  • The CICS key metrics subcomponent (part of the existing CICS feature)
  • The IMS key metrics subcomponent (part of the existing IMS feature)

These new subcomponents collect key metrics for mainframe performance tuning and consolidate them into a new set of tables for easy reporting and analysis. The volume of data collected is greatly reduced, resulting in significant performance improvements and throughput compared to the more traditional components.

2. IT Modernization and Skills

  • Improved time to value through the creation of key metrics collectors that simplify the installation and configuration of the product
  • Improved enterprise coverage with new script-based data collectors that can be easily customized and deployed for the many different flavors of UNIX, Linux and Microsoft Windows to collect basic performance metrics
  • A new version of IBM Tivoli Common Reporting, further enhancing the reporting capability with an updated IBM Cognos Business Intelligence V10.2 reporting engine.

Other new functions in the key metrics subcomponents of Tivoli Decision Support for z/OS, V1.8.2:

  • Support for calculating IBM IMS processing time in a similar manner to IBM IMS Performance Analyzer for z/OS
  • Exception reporting for the new IBM z/OS key metrics subcomponent
  • Support for IBM Db2 table-controlled partitioning, including partition by range and partition by growth
  • Ability to create Db2 tablespace profiles at a high level for all components or down to the component or subcomponent level
  • Improved collect batch job statistics based on the IBM Systems Management Facility interval

An Indispensable Tool for Making Informed Decisions

With all of these useful features, IBM Z Decision Support provides the tools you need for improving operational planning, cost management, responsiveness and decision making. IBM Z Decision Support is also a highly effective, adaptable performance reporting tool that can help you successfully track system performance and resource usage, anticipate and solve problems, and plan for future resource needs.

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