Apply Statistical Analytics to Your z/OS Performance Analysis
The days of having z/OS Performance and Capacity planning teams manually evaluate metrics to detect and resolve changes and risks are over.
Advanced statistical algorithms applied to the most meaningful RMF/SMF metrics will automatically identify significant changes to the z/OS workloads and applications and enable capacity planners to accurately forecast workload growth.
This statistical approach, combined with the predictive capabilities of Availability Intelligence, is the most advanced way to understand the performance and availability of the z/OS platform.
This webinar demonstrates how applying statistical algorithms to meaningful RMF/SMF metrics will result in benefits such as:
- Automatic detection of z/OS workload changes
- Quick notifications of service degradation
- Understanding workload peaks in context
- Accurately forecasting workload growth
- Feed cross-platform application monitoring
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IntelliMagic Vision Adds SMF Field Name Mapping to Live Report Editor
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