Discover how to transform unpredictable outages into predictable ones by creating Availability Intelligence out of the RMF and SMF data using built-in expert domain knowledge.
The mainframe is well known for superior availability and resilience, but unpredictable service disruptions do still occur. How can most of these “unpredictable” incidents become predictable instead? Fortunately, the mainframe produces the world’s richest source of performance and configuration data in the form of RMF and SMF, yet this data remains vastly underutilized. Seeking and avoiding threats to production availability requires new methods that go far beyond yesterday’s and go beyond more recent statistical analysis approaches. The key to outsmarting unavailability is to automatically interpret the RMF and SMF data using built-in expert domain knowledge about the hardware capabilities and best practices. IntelliMagic Vision does for z/OS Processors, Disks, Replication, and Virtual Tape components, and it’s easiliy accessbile via software-as-a-service. Learn more about Availability Intelligence at www.intellimagic.com
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This year has certainly stretched everyone. Learn some ways to overcome some new and unexpected challenges that likely weren’t even on your radar in January.
Health Insights and Change Detection
To help enhance the value you can derive from that SMF data, IntelliMagic Vision provides two complementary types of automated anomaly detection: health insights and change detection.
CICS Max Tasks: Automated Change Detection Use Case 7
Change Detection and Health Assessments (based on pre-defined best practice conditions) play complementary roles, but there can be scenarios where they both flag issues. This use case with CICS Max Tasks is one such example.
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