Posts Tagged ‘View’
I have seen different implementations of read caching in arrays and even inside hosts, just to be able to cope with boot storms of VDI workloads. When using linked clones caching really helps; all the VDIs being booted perform massive reads from a very small portion of the infrastructure: the replica(s). VMware came up with a nice software solution to this: Why not sacrifice some memory inside the vSphere nodes to accommodate read caching there? This is what CBRC (vSphere 5) or Host Caching (View 5.1) is all about. And… It really works!
What happens during a boot storm
First of all, we need to figure out what happens during a boot storm. Even wondered just how much data Read the rest of this entry »
A hugely underestimated requirement in larger VDI environments is disk IOPs. A lot of the larger VDI implementations have failed using SATA spindles, when you use 15K SAS or FC disks you get away with it most of the times (as long as you do not scale up too much). I have been looking at ways to get more done using less (especially in current times, who doesn’t!). Dataman, the dutch company I work for (www.dataman.nl) teamed up with Sun Netherlands and their testing facility in Linlithgow, Scotland for testing. I got the honours of performing the tests, and I almost literally broke the sound barrier using Suns newest line of Unified Storage: The 7000 series. Why can you break the sound barrier with this type of storage? Watch the story unroll! For now part one… The intro.
What VMware View offers… And needs
Before a performance test even came to mind, I started to figure what VMware View offers, and what it needs. It is obvious: View gives you linked cloning technology. This means, that only a few full clones (called replicas) are read by a lot of Virtual Desktops (or vDesktops as I will call them from now on) in parallel. So what would really help pushing the limits of your storage? Exactly, a very large cache or solid-state disks. Read the rest of this entry »
Nowadays, more and more companies realize that virtual desktops is the way to go. It seems inevitable. Resistance is Futile. But how do you scale up to for example 1000 users per building block? How much storage do you need, how many spindles do you need? Especially with the availability of VMware View 3, the answers to these questions become more and more complex.
Many people still design their storage requirements based on the amount (in GBytes) of storage needed. For smaller environments, you can actually get away with this. It seems to “fix itself” given the current spindle sizes (just don’t go and fill up 1TB SATA spindles with VMs). The larger spindle sizes of today and the near future however, make it harder and harder to maintain proper performance if you are ignorant about spindle counts. Do not forget, those 50 physical servers you had before actually had at least 100 spindles to run from. After virtualization, you cannot expect them all to “fit” on a (4+1) RAID5. The resulting storage might be large enough, but will it be fast enough?
Then VMware introduced the VMmark Tiles. This was a great move; a Tile is a simulated common load for server VMs. The result: The more VMmark Tiles you can run on a box, the faster the box is from a VMware ESX point of view.
In the world of view, there really is no difference. A thousand physical desktops have a thousand CPUs, and a thousand (mostly SATA) spindles. Just as in the server virtualization world, one cannot expect to be able to run a thousand users off of ten 1TB SATA drives. Although the storage might be sufficient in the number of resulting Terabytes, the number of spindles in this example would obviously not be sufficient. A hundred users would all share have to share a single SATA spindle!
So basically, we need more spindles, and we might even have to keep expensive gigabytes or even terabytes unused. The choice of spindle type is going to be the key here – using 1TB SATA drives, you’d probably end up using 10TB, leaving about 40TB empty. Unless you have a master plan for putting your disk based backups there (if no vDesktops are used at night), you might consider to go for faster, smaller spindles. Put finance in the mix and you have some hard design choices to make.
Just when you thought the equation was relatively simple, like “a desktop has a 10GB virtual drive period”, Linked cloning came about. Now you have master images, replicas of these masters, and linked clones from the replicas. Figuring out how much storage you need, and how many spindles you need just got even harder to determine!
Lets assume we have one master image which is 10GB in size. Per +-64 clones, you are going to need a replica. You can add up to about 4 replicas per master image. All this is not an exact science though; just recommendations found here and there. But how big are these linked clones going to be? This again depends heavily on things like:
- do you design separate D: drives for the linked clones where they can put their local data and page files;
- What operating system are you running for the vDesktops;
- Do you allow vDesktops to “live” beyond one working day (e.g. do you revert to the master image every working day or not).
Luckily, the amount of disk IOPS per VM is not affected by the underlying technology. Or is it? SAN caching is about to add yet another layer of complexity to the world of View…
Cache is King
Let’s add another layer of complexity: SAN caching. From the example above, if you would like to scale up that environment to 1000 users, you would end up with 1000/64 = 16 LUNs, each having their own replica put on there, together with its linked clones. If in a worst-case scenario, all VMs boot up in parallel, you would have enormous amount of disk reads on the replicas (since booting requires mostly read actions). Although all replicas are identical, the SAN has no knowledge of this. The result is, that the blocks used for booting the VMs of all 16 replica’s should be in the read-cache in a perfect world. Lets say our XP image uses 2GB of blocks for booting, you would optimally require a read cache in the SAN of 16*2=32GB. Performance will degrade the less cache you have. Avoiding these worst-case scenarios is another option to manage with less cache of course. Still I guess in a View 3 environment: “Cache is King“!
While I’m at it, I might just express my utmost interest in the development from SUN, their Amber Road product line to be more exact. On the inside of these storage boxes, SUN uses the ZFS file system. One of the things that really could make a huge difference here is the ability of ZFS to move content to different tiers (faster storage versus slower storage) depending on how heavily this content is being used. Add high-performance SSD disks in the mix, and you just might have an absolute winner, even if the slowest-tier storage is “only” SATA. I cannot wait on performance results regarding a VDI-like usage on these boxes! My expectations are high, if you can get a decent load-balance on the networking side of things (even a static load balance per LUN would work in VDI-like environments).
Resistance is ViewTile!
As I laid out in this blog post, there are many layers of complexity involved when attempting to design a VDI environment (especially the storage-side of things). It is becoming almost too complex to use “theory only” on these design challenges. It would really help to have a View-Tile (just like the server-side VMmark Tiles we have now). The server tiles are mostly used just to prove the effectiveness of a physical server running ESX, the CPU, the bus structure etc. A View-Tile would potentially not only prove server effectiveness, but also very much the storage solution used (and the FC- / IP-storage network design in between). So VMware: a View-Tile is definitely on my wish list for Christmas (or should I consider to get a life after all? 😉 )