It is amazing when you’ve been focused on a technology for so long that you start to see major improvements. In 2010, I provided my original guidance on XenDesktop IOPS. Four years later, have we seen any major improvement? See for yourself. As you might be aware, I’ve been working with the Citrix Solutions Lab on validating standardized designs. These validated designs are published as Citrix Design Guides. Part of this latest round of testing with XenDesktop 7.5 and XenApp 7.5 was focused on the new Provisioning Services write cache option “RAM Cache with Overflow to Disk”. When looking at … Continue reading Latest XenDesktop 7.5 IOPS
As technology changes, so too does a recommendation. For years when you deployed XenApp servers with Provisioning Services, the storage Read:Write ratio would be 10:90. This is still the case in most scenarios. But in analyzing the latest data from the Citrix Solutions Lab, who were testing the “RAM Cache with Overflow to Disk” option, we encountered some results that will make us revisit some of our old recommendations. IOPS: For a medium workload on XenApp 7.5 on Hyper-V 2012R2, the average IOPS per user is 1, as explained in the previous blog. R:W Ratio: When using the new write … Continue reading The Latest XenApp 7.5 Read/Write Ratios
As we all know, IOPS are the bane of any application and virtualization project. If you don’t have enough, users will suffer. If you have too many, you probably spent too much money and your business will suffer. So we are always trying to find ways to more accurately estimate IOPS requirements as well as finding ways to reduce the overall number. About 5 months ago, I blogged about IOPS for Application Delivery in XenDesktop 7.0. In the blog, I explained that for the XenApp workload, Machine Creation Services, when used in conjunction with Windows Server 2012 Hyper-V, required a … Continue reading The New XenApp – Reducing IOPS to 1
One of the big questions regarding virtual desktops is storage. In fact, I’ve discussed this numerous times (here and here and here). This has mostly been with a focus on IOPS. This time I want to focus on the high-availability aspect you get with shared storage, but with the focus of being on the SMB/SME space (small to medium business/enterprise). If you want to do live migration, you must have shared storage. So, let me get straight to the point… You don’t need it. You don’t need XenMotion, vMotion or live migration in a SMB hosted VM-based desktop model. Look … Continue reading SAN, VDI and SMB
With desktop virtualization, we hear more and more about how important IOPS are to being able to support the virtual desktop. I’ve had a few blogs about it and plan to have a few more. What I wanted to talk about was an interesting discussion I recently had with 3 Senior Architects within Citrix Consulting (Doug Demskis, Dan Allen and Nick Rintalan). There are 3 smart guys who I talk to fairly regularly and the discussions get quite interesting.
This particular discussion was no different. We were talking about the importance of IOPS, RAID configs, spindle speeds with regards to an enterprise’s SAN infrastructure. (Deciding if you are going to use a SAN for your virtual desktops is a completely different discussion that I’ve had before and Brian Madden had more recently). But for the sake of this article, let’s say you’ve decided “Yes, I will use my SAN.” If your organization already has an enterprise SAN solution, chances are that the solution has controllers with plenty of cache. Does this make the IOPS discussion a moot point? Continue reading “Does Cache Trump IOPS”
Previously I’ve talked about how using local storage can help reduce the costs of desktop virtualization. Paul Wilson tested this type of environment to determine if it is possible or to see if I was talking crazy. The result: it is possible and I’m a little crazy. So we have a new design decision, which way will you go?
I’m working when I’m idle, well from my desktop’s perspective that is the case. We all know that when a desktop starts, when a user logs on, when a user is working and when a user logs off that user has an impact on the system resources: CPU, Memory and Disk. When the user is idle, we expect CPU and Disk to idle as well. But is this accurate? Take a look at my perfmon graph from my desktop for almost an entire workday: As you can see, when I’m working my CPU and disk activity increases. CPU increases to … Continue reading I’m working when I’m Idle