Automated assessments of key CICS Statistics Transaction and Enqueue metrics facilitate proactive analysis and prevention of outages by identifying areas that may warrant investigation. These views also expedite learning by highlighting metrics that are important to understand and analyze.
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Okay. Another type of CICS Statistics data that lends itself to health assessments is Transaction Health. And so one metric of interest here is Times Reaching MAXTASKS. Again, we’ll scroll down. We see here at the bottom that there’s a couple of exceptions for this particular group. So one way we could look at it is initially just look at it by region. And we do that. We see that those exceptions are occurring at least to some degree for broad across almost all the regions that are in this group.
All right. So again, let’s look at the metrics over time. And so here we see the times at max task. Here we see what the peak active user transactions were. We could see that for three consecutive hours that we hit 75. So that suggests that that’s the MAXTASK value. And we could go elsewhere in the statistics data and we could validate that that is indeed that value.
So when we look at the number of MAXTASK exceptions, we had just a few in the seven o’clock hour, quite a few more in the eight o’clock hour, and then almost 800 in the nine o’clock hour. Again, if we want to look at this across the regions that are in that group, we see that it’s primarily occurring in three of the many, many, many regions in that group.
So one more thing let’s just broaden this out and look at the whole week. When we do that, we see that hitting the MAXTASK was very much an anomaly that day and didn’t happen any other times during that week. All right. One other area of assessment would be CICS Enqueues. CICS can use Enqueues to serialize within activity going on within a region. So here there’s a lot of Enqueues rejected due to Enqueue busy. We look into that by Enqueue pool name. We see that it is this particular single system-defined resource name. And again, here, let’s go ahead and look at it over time. And we can see the Enqueues are being rejected. We see the total number of Enqueue requests, which is very sizeable.
And so one thing we might want to do is see the number of Enqueues that were rejected in context, along with the total volume of Enqueues, and then that way we can assess whether the volume of rejected requests is, you know, that percentage is kind of what we expected based on how the application was designed, or if it would warrant some additional investigation.
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