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.
Batch Job Elapsed Time: Automated Change Detection Use Case 6
Elapsed and CPU times can be of interest for batch jobs, especially those on the critical path for key business job cycles. This use case reflects an abnormally long run time for a daily batch job, and illustrates how potential causes might be investigated.
Coupling Facility Request Rate: Automated Change Detection Use Case 5
Key coupling facility performance metrics at the structure level include synchronous and asynchronous request rates and service times, making them attractive candidates for Change Detection.
Db2 Getpage Rate: Automated Change Detection Use Case 4
“Getpages” are a primary indicator of Db2 activity, reflecting the count of operations to access data in a page. This use case illustrates the identification of a significant increase of Getpages.
CICS CPU Per Transaction: Automated Change Detection Use Case 3
This example identifies significant increases in CPU per transaction for selected transactions, isolates it to specific CICS regions, and investigates that further.
CPU by WLM Importance Level: Automated Change Detection Use Case 2
Leveraging context-sensitive drilldowns, based on an unanticipated finding of a spike in CPU consumption for the highest importance “Systems” work, this video investigates the address space(s) driving that.
CPU by System: Automated Change Detection Use Case 1
One common use case for Change Detection is identifying significant variances in key metrics at the system level including: CPU usage, zIIP usage, percent utilization of the CPC, etc.
Introduction to Automated Change Detection for z/OS Performance Management
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 Views
Deriving Insights from SMF 116 MQ Accounting Data
MQ Accounting records are often considered to be too expensive, both in CPU overhead and SMF record volume. However, we believe that these concerns may be overstated.