Capacity Management Is Crucial In Virtualised Environment from TeamQuest

White Paper

A big enough virtual pool of resources does not come without an effort. If you don t pay

attention to the characteristics of the workloads you re hosting, there s a big risk you ll either

over-provision or starve your applications.

To be sure that workloads will operate efficiently with each other (or that the analogous

Tetris-like shapes will be capable of fitting snuggly with one another) requires some

serious analysis. You need to understand a lot about the resource requirements of the

underlying applications in order to know whether one workload might interfere with

another creating unnecessary resource contention and queuing delays. So, especially for

critical workloads, it makes sense to do some careful up front analysis rather than simply

tossing various workloads onto your systems hoping that dynamic resource scheduling and

migration will be able to quickly and efficiently fit everything together for you on the fly.

Optimized workload placement

made possible by up front planning

Estimation, as we mentioned earlier, typically entails stacking workloads on top of each

other until a predetermined threshold is reached. It is the technique used by most of the tools

available for surveying a data center and identifying virtualization possibilities. The results are

cheap and easy to obtain, but represent rough estimates. By itself estimation will not come

close to helping you reach optimal performance.

Analytic modeling is not as fast, easy, and inexpensive as estimation, but it comes close.

(By analytic modeling we specifically mean modeling using an analytic queuing network

solver. Less sophisticated analytic mathematical models are perhaps better considered as

estimation tools.) Analytic modeling is much simpler and less time-consuming to set up

than load testing (or simulation modeling), and it is much more accurate than estimation.

Load testing can be very accurate if the synthetic loads and the underlying system configurations

are representative of the production environment. However, more time and expense is required

the closer the loads and the infrastructure get to matching production. For most situations, it

7 of 8 Capacity Management is Crucial in Virtualized Environments