Identify Root Causes of Coupling Facility Configuration Issues
This video demonstrates how you can identify performance issues and root causes for issues in your z/OS Coupling Facility configuration. Addressing these issues could save you precious CPU.
Doing more for less money is the goal of every business, and this case clearly shows to achieve that.
What is a Coupling Facility?
A coupling facility is a piece of hardware that enables processors to communicate. The coupling facility is designed to be much faster for data access than going to physical storage.
Sometimes, a coupling facility runs so well that performance analysts do not regularly monitor and report on the performance of the underlying structures. However, individual structure performance is important to the workload using the structure as well as the overall system.
Sync Service Time Issues
In the case demonstrated in the video, when we loaded RMF data for a customer, we noticed that several structures had warnings for Sync Service Time and Async Service Time.
The customer was not aware that there was a performance problem for any of the structures, but as we saw, many structures were getting worse than 20 microseconds, which was the initial threshold set by IntelliMagic Vision.
We then found that the structure’s synchronous service time was very high for the entire time period.
Delaying Expensive CPU Upgrades
Investigation led to the fact that coupling facilities were being shared which was causing the high service times. A dynamic change was implemented to the coupling facility to use the parameter “Dyndisp=Thin” which had dramatic results.
Besides the obvious result of the structure getting better service times, the “real” CPU cycles spent waiting on the synchronous service requests dropped dramatically as well. This will allow the customer to delay an expensive CPU upgrade.
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Understanding Physical Sequential Datasets
Oftentimes it is sufficient to analyze I/O performance without needing to understand the datasets. The SMF 42 records are useful to obtain a deeper understanding of the datasets and the applications that are using the storage.
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