Obtaining key metrics for CICS, Db2, and IMS transactions has historically required processing massive volumes (often hundreds of millions or more) of SMF 110 records (for CICS), SMF 101 records (for Db2), and subsystem log records (for IMS).
As an unexpected “side benefit” of IBM’s Mobile workload pricing support, key transaction level metrics are available in the RMF type 72 records at the service class or report class level. You can take advantage of this capability by mapping transactions of interest into a set of report classes using the wide range of classification methods available in WLM.
Reduced Time on Reporting & Cost Savings
Extracting, transmitting, and processing these enormous volumes of records has typically been a very CPU-intensive task requiring many hours of elapsed time. By taking advantage of this improved data collection capability, you have the opportunity to save on reduced CPU consumption and the manpower costs of reporting on this data manually by looking at the key transaction level metrics.
IntelliMagic Vision’s visibility into these transaction level metrics is designed to help you understand your performance challenges by collecting raw systems metrics data, generating easy to understand charts, ratings, and dashboards, and providing you the information you need to help improve root cause identification, operational planning, cost management, responsiveness, and decision making.
Visibility into Key Metrics
IntelliMagic Vision provides visibility into the widely-used transaction level metrics available in the RMF 72 records, including transaction rate, transaction response time, average concurrent transactions, CPU and I/O per transaction, and total CPU and I/O activity. Visibility through advanced IntelliMagic Vision interface that was previously not available; opportunity to avoid CPU & manpower costs of processing millions of detailed transaction records.
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.
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.