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Capacity Planning for Virtual Environments : Part 1

Posted by Aditya Thatte on August 18, 2010

Capacity planning for virtualized data centers in the light of cloud computing has become a highly sought after topic. Capacity planning for traditional data centers includes development of performance models of stand-alone applications residing on bare-metal architectures, as opposed to a hypervised environment which hosts multiple applications across a shared resource pool in an isolated fashion. Sizing capacity for virtualized environments adds new dimensions in terms of constraint variables and dependencies which are to be considered while developing models.

As we all know by now, the motivation behind Virtualizing applications is to ‘do more with less’, increase ROI, reduce TCO, create a greener environment and so on, planning the size of virtual machines hosting these applications becomes a key aspect. Server consolidation is a means to achieve higher utilization of servers, which may be under-utilized in a dedicated physical environment. Placing multiple VM’s across a shared resource pool is governed by target SLA’s, optimizing power consumption, optimally sharing physical resources, workload type (database, web server)of the application. Sizing and managing capacity of these virtual entities becomes an important factor during the virtualization lifecycle in the context of cloud computing. Understanding issues of VM interference (cache interference, i/o interference), hypervisor overheads should be helpful in sizing VMs.

Analysis could be done for P2V, V2V migrations, thereby estimating VM size and adapting according to existing (current) bottlenecks and future trends. There are many useful P2V tools and capacity analyzers made available by vendors.

– PlateSpin Recon

– Microsoft SCVMM

– Oracle VM Manager

– VMware P2V Assistant

– HP Capacity Advisor

– Vkernel Capacity Optimization

In this article we just scratched the surface of  ‘Capacity Planning for Virtual Environments’ . In the next part we shall see detailed aspects related to interference and performance of applications in a hypervised setup.  Here’s one of my favorite literature on capacity planning

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