Posts Tagged ‘vcva’

h1

Shanghai Economics 101 – Conclusion

May 6, 2009

In the past entries, we’ve looked only at the high-end processors as applied to system prices, and we’ll continue to use those as references through the end of this one. We’ll take a look at other price/performance tiers in a later blog, but we want to finish-up on the same footing as we began; again, with an eye to how these systems play in a virtualization environment.

We decided to finish this series with an analysis of  real world application instead of just theory. We keep seeing 8-to-1, 16-to-1 and 20-to-1 consolidation ratios (VM-to-host) being offered as “real world” in today’s environment so we wanted to analyze what that meant from an economic side.

The Fallacy of Consolidation Ratios

First, consolidation ratios that speak in terms of VM-to-host are not very informative. For instance, a 16-to-1 consolidation ratio sounds good until you realize it was achieved on an $16,000 4Px4C platform. This ratio results in a $1,000-per-VM cost to the consolidator.

In contrast, let’s take the same 16-to-1 ratio on a $6,000 2Px4C platform and it results in a $375-per-VM cost to the consolidator: a savings of nearly 60%. The key to the savings is in vCPU-to-Core consolidation ratio (provided sufficient memory exists to support it). In the first example that ratio was 1:1, but in the last example the ratio is 2:1. Can we find 16:1 vCPU-to-Core ratios out there? Sure, in test labs, but in the enterprise we think the valid range of vCPU-to-Core consolidation ratios is much more conservative, ranging from 1:1 to 8:1 with the average (or sweet spot) falling somewhere between 3:1 and 4:1.

Second, we must note that memory is a growing aspect of the virtualization equation. Modern operating systems no longer “sip” memory and 512MB for a Windows or Linux VM is becoming more an exception than a rule. That puts pressure on both CPU and memory capacity as driving forces for consolidation costs. As operating system “bloat” increases, administrative pressure to satisfy their needs will mount, pushing the “provisioned” amount of memory per VM ever higher.

Until “hot add” memory is part of DRS planning and the requisite operating systems support it, system admins will be forced to either over commit memory, purchase memory based on peak needs or purchase memory based on average memory needs and trust DRS systems to handle the balancing act. In any case, memory is a growing factor in systems consolidation and virtualization.

Modeling the Future

Using data from the Univerity of Chicago and as a baseline and extrapolating forward through 2010, we’ve developed a simple model to predict vMEM and vCPU allocation trends. This approach establishes three key metrics (already used in previous entries) that determine/predict system capacity: Average Memory/VM (vMVa), Average vCPU/VM (vCVa) and Average vCPU/Core (vCCa).

Average Memory per VM (vMVa)

Average memory per VM is determined by taking the allocated memory of all VM’s in a virtualized system – across all hosts – and dividing that by the total number of VM’s in the system (not including non-active templates.) This number is assumed to grow as virtualization moves from consolidation to standardized deployment. Read the rest of this entry ?