Monthly Archives: February 2021

20-second Peak Metrics

vRealize Operations 8.3 sports a set of 12 metrics that captures the highest 20-second average in any given 5 minute (the default collection interval). Why only 12 and how are they chosen?

First, some background. vRealize Operations collects and stores data every 5 minute. This is good enough for monitoring use case, but not for troubleshooting. 300-second average is not granular enough, as performance problem may not be sustained that long. Even a performance issue that last hours may consist of repeated short bursts. On the other hand, 5-minute produces too much noise for Capacity, where you need 3-month or 1 year overall trend. Are you going to increase the VM vCPU size just because it has 20 second – 1 minute high utilization? What if that’s caused by Windows and not real business workload? I’m keen to learn if I’m wrong.

Take a look at the table below. It shows a VM with 2 virtual disks. Each disk has its own read latency and write latency, giving us a total of 4 counters.

While vRealize Operations collects every 300 seconds, it actually grabs 15 data points. Why 15? Those 15 matches the 20-second that vCenter produces. Each 20-second data point is an average of the entire 20 seconds. So all along, vR Ops actually has 20-second visibility. However, it averages these 15 data points, losing the 20-second granularity.

3 different kinds of summary

What vR Ops 8.3 does is to add a new metric. It does not change the existing metric, because both have their own purpose. The 5-minute average is better for your SLA and performance guarantee claim. If you guarantee 10 ms disk latency for every single IOPS, you’d be hard pressed to deliver that service. These new counters acts as early warning. It’s an internal threshold that you use to monitor if your 5-minute SLA is on the way to be breached.

vR Ops 8.3 takes the peak of these 15 data points, and stores it every 5 minutes. It does not store all 15 data points, because that will create a lot more IOPS and consume more storage. It answers the question “Does the VM or Guest OS experience any performance problem in any 20-second period?”

Having all 20-second data points are more natural to us, as we’re used to 1 second in Windows and 20 second in vCenter performance charts. But how does that additional 14 data points change the end remediation action? If the action you take to troubleshoot is the same (e.g. adjust the VM size), why pay the price of storing 15x more data points?

If you need to store them all, vR Ops Cloud does it for you. Note that it’s limited to 7 days, while this technique lets you store for 6 months as it’s just like any other regular metric.

In the case of virtual disk (as opposed to say memory), a VM can have many of them. A database VM with 20 virtual disks will have 40 peak counters. That also means you need to check each one by one. So what vR Ops 8.3 does is to take the peak among all virtual disks read and writes. It does the same thing with vCPU. A monster VM with 64 vCPU will only have 1 metric, but this metric is the highest among 64 virtual CPU. There is no need to have visibility into each vCPU as the remediation action is the same. Whether it’s vCPU 7 or vCPU 63 that has the problem, it does not change the conclusion of troubleshooting in most cases.

Why these 12?

The next question is naturally why we picked the above 12. You notice they are only VM counters. No ESXi, Resource Pool, Datastore, Cluster, etc counters. The reason is the counters at these “higher-level” objects are mathematically an average of the VMs in the object. A datastore with 10 ms disk latency represents a normalized average of all the VMs in the datastore. Another word, these counters give less visibility than the 12 above, and they can be calculated from the 12. And 1 more reason:

You troubleshoot VM, not infrastructure. If there is no VM, there is no problem :-)

Among the 12 counters, you notice only 1 counter tracks utilization. The other 11 tracks contention. Utilization is not a counter for performance. It’s a counter for capacity. The higher the utilization, the more work gets done, and hence the better the performance. Utilization at 100% is in fact the best possible performance, so long there is no contention. Since we can track contention explicitly, the performance counter becomes secondary, supporting counter.

Why is Guest OS level metrics provided? Because they do not have VM equivalent, and change the course of troubleshooting. If you have high CPU run queue, you look inside Windows and Linux, not at the underlying ESXi.

For CPU, the complete set of contention is provided. There are 6 counters tracking the different type of contention or wait that CPU experiences.

For Memory, popular metrics such as Consumed, Active, Balloon, Swap, Compress, Granted, etc are not shown as they do not indicate performance problem. Memory Contention is the only counter tracking if the VM has memory problem. VM and Guest OS can have memory problem independently. In future, we should add Guest OS memory performance counters, if we find a good one. Linux and Windows does not track memory latency, only track memory disk space consumption, throughput and IOPS. These 2 OS do not track latency, which is the main counter for performance.

For Network, vCenter does not have latency and re-transmit. It has dropped packet, but unfortunately this is subject to false positive. So we have to resort to utilization metric. In future, we should add packets per second.

To use, enable them in the policy

The 12 metrics are disabled by default. Let us know if you think we should enable them by default, and what VM metrics we should disable to compensate 🙂

Lastly, just in case you ask why we do not cover Availability (e.g. something goes down). Reason is this is better covered by event. Log Insight does a better job on this.