Large scale VMware vSAN operations raises the need for easier and faster monitoring. With many and large vSAN clusters, monitoring and troubleshooting become more challenging. To illustrate, let’s take a single vSAN cluster with the following setup:
Here are some of the questions you want to ask in day to day operations:
- Is any of the ESXi running high CPU utilization?
- Is any of the ESXi running high Memory utilization?
- Is any of the NIC running high utilization?
- With 4 NIC per ESXi, you have 40 TX + 40 RX metrics.
- Is vSAN vmkernel network congested?
- Is the Read Cache used?
- Is the Write Buffer sufficient?
- Is the Cache Tier performing fast?
- Each disk has 4 metrics: Read Cache Read Latency, Read Cache Write Latency, Write Buffer Write Latency, Write Buffer Read Latency
- Since there are 20 disks, you need to check 80 counters
- Is the Capacity Disks performing fast?
- Check both Read and Write latency.
- Total 120 x 2 = 240 counters.
- Is any of the Disk Group running low on space?
- Is any of the Disk Group facing congestion?
- You want to check both the max and count the number of occurrence > 60.
- Is there outstanding IO on any of the Disk Group?
If you add them the above, you are looking at 530 metrics for this vSAN cluster. And that’s just 1 point in time. In 1 month you’re looking at 530 x 8766 = 4.6+ millions data points!
How do you monitor millions of data so you can be proactive?
vRealize Operation 6.7 sports vSAN KPIs. We collapsed each of those questions. So you only have 12 metrics to check instead of 530, without losing any insight. In fact, you get better early warning, as we hide the average. Early Warning is critical as buying hardware is more than a trip to local DIY hardware store.
The KPIs achieve this simplification by using supermetrics:
Using Min, Max, Count, it picks the early warning.
The KPI has been a hit with customers. But it falls short when you have many vSAN clusters. If you have say 25 hybrid clusters and 25 All Flash clusters, you need to check 50 clusters. While you can click 50x, what you want is to see all 50 at the same time.
This means we need to aggregate the metrics further. There should only be 1 and only 1 metric per cluster.
The challenge is the KPI has different units and scale. How do we normalize them into Green, Yellow, Orange and Red?
We do it by defining a normalization table. We need 1 table for All Flash and 1 for Hybrid, as they have different KPI and threshold. Here is the table for All Flash:
Read Cache Hit Rate (%) is missing from the above as it’s not applicable to All Flash. It does not have dedicated Read Cache.
I’m setting CPU Ready and CPU Co-Stop at 1%, so we can catch early warning. For RAM, as most ESXi sports 512 GB RAM, I set the RAM Contention at 0%.
The metrics that I’m not sure if the Disk Group Congestion. It’s based on 60, which I think is a good starting point in general.
Here is the table for Hybrid:
Do you know why I do not have Utilization counter (e.g. CPU Utilization) there?
Utilization does not impact performance. ESXi running at 99% is not slower than ESXi running at 1%, so long there is no contention or latency. This is vSAN KPI, not vSAN KUI (Key Utilization Indicators). Yes, vSAN KUI needs its own table.
Once you have the table, you can map into threshold. I use Green = 100, Yellow = 67, Orange = 33, Red = 0. I use 0 – 100 scale so it’s easier to see the relative movement. If you don’t want to be confused with %, you can use 0 – 10 or 0 – 50.
vSAN Performance is the average of all these. We are not taking the worst to prevent 1 value from keeping it red all the time. If you take the worst, the value will likely remain constant. That’s not good, as pattern is important in monitoring. The relative movement can be more important than the absolute value.
You implement the above using super metric. You need 2 super metrics, 1 for Hybrid and 1 for All Flash. For simplicity, I’d not use Policy but rather apply both super metrics to all my vSAN clusters. I then use the correct metrics when building the dashboard.
Hope you find it useful.