How many more VMs can you fit into a Cluster, Resource Pool or Host?

Given the speed at which most admins are adding new VMs to their environment ("vm sprawl"), every admin has to figure out where to deploy the new VMs. Simply guessing about resource availability will lead to performance problems and downtime. What I am proposing here is a simple 4 step process you can use to determine how many more VMs you can fit into a Cluster, Resource Pool or a host. Here it goes

1. Select a cluster, resource pool or a host
2. Get info on available memory, storage, cpu, disk i/o and net i/o
3. Calculate an average VM footprint in the cluster, RP or host in terms memory, cpu, storage, disk i/o
4. Apply average VM footprint to every resource type to see which resource you will run out of first.That’s how many more VMs you can fit into host, cluster or resource pool

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Servers are no longer a "Resource Boundary"

One of the hardest concepts for System Administrators new to virtualization to understand is the shared resource management. VMware ESX makes it possible to share resources namely memory, cpu, storage and network not only inside a physical host, but also across multiple physical hosts. The resources are pulled together to create one massive resource pool captured in a concept called a cluster. Even resources inside clusters can be further subdivided into many Resource Pools. For admins who are only used to dealing with physical servers as resource boundaries this can be confusing, especially when it comes to planning and management of capacity. For example when monitoring or determining resource capacity, Admins must now take into consideration how all resource boundaries are affected. Looking just at physical servers is no longer an option!

9 capacity bottlenecks in ESX that kill performance

I have compiled a list of "things" that can cause you to run out of capacity resources in your ESX data center and run into performance problems or even downtime:

1. Adding new VMs though uncontrolled VM sprawl
2. Removing hosts from clusters
3. HA enabling your cluster without accounting for fail over
4. Changing Fail Over Capacity setting in a Cluster
5. Increasing reservations in VMs
6. Changing Resource Pool Configurations
7. Power up many VMs that were powered off or in maintenance
8. Natural growth rates in Storage, CPU, Memory and Network utilization
9. Changes in workloads can result in Disk I/O bottlenecks

Did I miss any? Let me know abakman@vkernel.com