Thursday, 27 January 2011

Brian Madden Blogs: DataCore Software releases a "nirvana" VDI storage solution & Benchmark Paper

DataCore releases a "nirvana" VDI storage solution. Full local virtual storage that's really cheap! by Brian Madden

Back in September, I wrote an article describing a product that I wanted that didn't exist: a local "virtual" storage option for VDI. Basically I described why I didn't like SANs for VDI and that I thought it would be cool if there was some sort of software that could virtualize access to the local hard drives that are in a VDI host server. I was thinking a solution like that could create the best of both worlds: fast flexible storage without the overhead costs of a SAN.

In a new white paper from DataCore (direct PDF link), they claim that their SANmelody software running on a VDI host does fulfill my fantasy storage requirements. And they claim they can do it with full multi-server redundancy with a cost of less than $70 per user. (That's $70 for everything.. the VM host, the SANmelody software, the disks you need for storage... everything!)...

Frequent readers know that I'm not one to republish vendor papers. But in this case, the DataCore paper (by Ziya Aral & Jonathan Ely) is actually really, really good. They take a no-BS look at VDI storage, and they validate their architecture with standard tools like Login Consultants' VSI benchmark.

From the paper: Previous publications have reported on configurations which use thousands of virtual desktops to defray the cost of these controllers. Reading between the lines, it becomes immediately apparent that per-virtual desktop hardware costs rise very sharply as such configurations are scaled downward. Yet, it is precisely these smaller VDI configurations which are the more important from most practical standpoints. On the other hand, this configuration may also be scaled upwards, in a linear fashion, to thousands of virtual desktops, thus eliminating distended configurations created by the search for artificial "sweet spots" at which costs are optimized...

Read the full Blog post at:

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