WLM Response Time Distribution Reporting With RMFIf you’re running a workload with WLM Percentile Response Time goals take a look at the RMF Service Class Period Response Time Goal Attainment instrumentation. It’s in the Workload Activity Report but this post is about using the raw data to tell the story better than a single snapshot (or long-term “munging”) can. (An example of a percentile response time goal is “90% of transactions must end in 0.2 seconds or less”.) The raw data is in the SMF 72 Subtype 3 Response Time Distribution Data Section. For each Service Class Period an array of values is given: Each value represents a count of the number of transactions that ended within the response time constraints of that bucket. Here are some examples:
I’ve omitted the middle buckets for brevity but note there’s one that’s up to 100% of the goal response time - a handy characteristic. This “response time bucket” data is clearly a lot more use than just knowing the average response time achieved (or even the standard deviation). My first implementation stacked up the buckets as percentages, and here’s an example: 
Isn’t it “busy”. Because I couldn’t see the wood for the trees I refurbished this graph a couple of years ago:
The result looks like:
(This is actually from a customer performance test so don’t be put off by the repetitive hour labels on the x axis. One day I’ll get round to tidying up fractional hour labels - when I get sufficiently disgusted.) This is much cleaner than the old version:
So, this has served me well for a while. Thoughts For The FutureI think I might’ve gone too far in the direction of simplification with this: I’d like to add the “just made it” and “almost made it” buckets back in. (Whether I use shading or different colours for these is still up for debate.) The buckets I’m tempted to break out are 90% to 100% and 100% to 110%. The data I see, though, might drive me to use 80% to 100% and 100% to 120%. We’ll see. I also can’t see how goal attainment relates to transaction rate:
So, adding a second y axis and plotting transaction rate against it would tell that part of the story. I’d like to understand how the percentage of transactions ending in Period 1, Period 2, etc varies: Just today I had a situation where - over a weekend - the percentage of transactions ending in Period 1 dropped, as transactions got suddenly more CPU-heavy. At present the code treats each Service Class Period independently, though it does print shift-average transaction rates ending in each period, along with the average CPU (not per transaction but totalled). One thing I consider a very long stretch would be to make this a 3D chart - with the bucket boundaries considered to be “contour lines”.
That would be very pretty ConclusionsI hope you’ll agree there’s lots you can usefully do with Response Time Distribution statistics. Most particularly If you have significant workloads with percentile goals - which would be almost 100% true for DDF, and true of quite a few CICS workloads. I also hope you’ve found the evolution of a chart interesting: It’s been occasioned by lots of customer interactions over a number of years. I can’t say either of the two charts I’ve shown actually caused evolutions but I think them interesting examples. We’ll see if I actually get to make the changes I’m contemplating:
My hunch is I will - but I wouldn’t expect me to supply 3D glasses any time soon. 
DISCOVER RELATED RESOURCES TAGGED [workload_activity wlm smf rmf transaction percentile goal rexx response_time gddm smf72-3]
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And what was the goal?
And the legend is pretty cruddy, too.
(This is explained by the reporting tool (SLR) insisting on using table column names as series names.)
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