Cumulative
In the context of z/OS and mainframe systems, "cumulative" refers to a metric or value that continuously accumulates or sums up over a defined period, typically from a starting point (like system IPL, job start, or transaction initiation) until the current point in time. It provides a running total of an activity or resource usage, reflecting the total work performed or resources consumed over that duration.
Key Characteristics
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- Continuous Accumulation: Values are constantly added to the total, rather than representing a point-in-time snapshot or an average.
- Defined Scope/Period: The accumulation period is crucial, often spanning from system IPL, job execution start, address space creation, or the beginning of a specific monitoring interval.
- Monotonic Increase: For most resource usage metrics (e.g., CPU time, I/O counts), cumulative values are expected to be non-decreasing (always increasing or staying the same), unless explicitly reset.
- Reset Mechanism: Many cumulative counters can be reset, either automatically (e.g., at job completion, transaction end) or manually (e.g., by an operator command for system-wide statistics or a specific component).
- Historical Context: Provides insight into the total work performed or resources consumed over an extended duration, essential for trend analysis, capacity planning, and chargeback.
Use Cases
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- Performance Monitoring: Tracking cumulative CPU time, I/O operations (
EXCPcounts), or elapsed time for a batch job, started task, or the entire system to assess overall resource consumption over its lifetime. - System Accounting and Chargeback: Calculating total resource usage (CPU service units, I/O operations, memory consumption) for billing purposes by summing up consumption over a billing cycle using data from SMF records.
- Database Statistics: Monitoring cumulative buffer pool reads/writes, transaction counts, or lock waits in DB2 or IMS to identify long-term performance trends or resource contention within the database subsystem.
- CICS Transaction Analysis: Observing cumulative transaction counts, response times, or resource usage for a CICS region or specific transactions to understand workload patterns and resource demands over time.
- SMF Record Analysis: Extracting cumulative metrics from various SMF record types (e.g.,
SMF Type 30for job/step activity,SMF Type 70for CPU activity) to analyze system-wide resource consumption and workload characteristics over extended periods.
- Performance Monitoring: Tracking cumulative CPU time, I/O operations (
Related Concepts
Cumulative metrics are fundamental to performance monitoring and capacity planning on z/OS. They are often collected via System Management Facilities (SMF) records, Resource Measurement Facility (RMF) reports, and various product-specific monitors (e.g., DB2 PM, CICS PA). These values contrast with *delta* or *interval* metrics, which represent changes or averages over a specific, shorter interval. Understanding cumulative values is crucial for interpreting system and application behavior over extended periods and for understanding the total impact of a workload.
- Understand the Reset Point: Always know when a cumulative counter was last reset (e.g., system IPL, job start, manual command) to correctly interpret its value and avoid misinterpretations.
- Combine with Interval Data: Use cumulative data for long-term trends, capacity planning, and historical analysis, but combine it with interval (delta) data for identifying short-term spikes, immediate performance issues, or current activity levels.
- Monitor Key System Metrics: Regularly review cumulative CPU time, I/O counts, and memory usage for critical address spaces and the entire system to detect anomalies, resource exhaustion, or unexpected growth.
- Automate Data Collection: Utilize tools like RMF and SMF to automatically collect and store cumulative performance data, ensuring comprehensive historical records for analysis and reporting.
- Baseline and Trend Analysis: Establish baselines for cumulative metrics during normal operations to identify deviations that might indicate performance degradation, system issues, or changes in workload patterns over time.