CICS Max Tasks: Automated Change Detection Use Case 7
View a CICS Max Tasks use case where Change Detection and Health Assessments play complementary roles in flagging serious issues.
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 investigates significant increases in CPU per transaction for selected transactions, isolated to specific CICS regions.
CPU by WLM Importance Level: Automated Change Detection Use Case 2
This video investigates the address space(s) driving an unanticipated spike in CPU consumption for the highest importance “Systems” work.
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
Troubleshoot High CPU and zIIP Utilization in WebSphere Application Server
A common problem performance analysts encounter is high CPU utilization on a server or application without the ability to identify the root cause of the problem quickly and easily.