IBM Z® IntelliMagic Vision for z/OS® enables performance analysts to manage and optimize their z/OS MQ configurations and activity more effectively and efficiently, and proactively assess the health of their queue managers.
Access built-in health insights that rate hundreds of critical metrics to proactively identify risks to your application health and performance. AI-derived anomaly detection highlights statistically significant changes, accelerating problem-solving.
Use thousands of out-of-the-box reports combined with a powerful, intuitive GUI, real-time comparisons and editing, and context-sensitive drill-down capabilities to maximize time spent preventing and resolving issues. Minimize downtime without the need for custom coding.
Augment the effectiveness of staff with interactive, customizable and shareable dashboards, built-in explanations and extensive drill downs. Use AI as a force multiplier to expedite learning, promote collaboration and enhance analytical effectiveness.
What's included
IBM Z IntelliMagic Vision for z/OS provides an effective way to focus your analysis and generate interactive reports. You can use context-sensitive drill downs to facilitate rapid and focused access to MQ data to manage, tune and optimize your environment.
Responsive performance from MQ relies on data in memory, so buffer pool management is important. IBM Z IntelliMagic Vision for z/OS automatically assesses every buffer pool in every queue manager to identify areas for investigation and presents them in red, yellow or green. Drill-down capabilities facilitate further analysis.
Viewing the number of requests by type of MQ command can be helpful for identifying a workload baseline, as well as indicating any significant workload changes. Visibility into this data can show the volume of requests to PUT messages to the queues, which queue managers have the most activity, the time-of-day profile, and more.
Views of buffer pool utilizations over time can indicate when these values are approaching thresholds that prompt automated de-staging to disk.
A well-performing MQ logging infrastructure is essential to support recovery and backout (driven largely by persistent messages) without impacting ongoing performance. Log Manager metrics can show the volume of data being logged and help identify any bottlenecks in log processing.
MQ accounting data provides detailed activity metrics at many levels, which are invaluable to performance specialists as they investigate application problems and carry out performance tuning. This example of command rates by queue name shows distributions across various types of queues.
Another level of detail that is found in the MQ accounting data is the type of work calling MQ (“connection type”). In this example, the two primary drivers of MQ CPU are work arriving from CICS and channel initiators.
Other types of analysis may focus on metrics such as CPU and elapsed times on a per-call basis. Though the absolute numbers are small, this view indicates that CPU time per MQGET call for work arriving from IMS is approximately double that of other types of work.
The capability to customize reports to combine multiple variables and analyze potential correlations can greatly aid analysis, as in this example. In many of today’s solutions that rely on catalogs of static reports, this analysis requires the coding effort to develop a new report.
Even with massive volumes of MQ accounting data, dynamic navigation and context-sensitive drill-down capabilities enable you to focus on a specific subset of data. In this example, drill downs into work originating from CICS and then further by CICS transaction ID, profile the length of messages being “PUT” by transaction.
Advantages to adopting a cloud model include rapid implementation (no lead time to install and set up the product locally), minimal setup (only for transmitting SMF data), offloading staff resources, and access to IntelliMagic consulting services to supplement local skills.