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Performance Assurance - A Key to Virtual Desktop Success

Very often, when an enterprise starts on the virtual desktop journey, the focus is on the user desktop. This is only natural - after all, it is the desktop that is moving - from being on a physical system to a virtual machine.

Therefore, once a decision to try out VDI is made, the primary focus is to benchmark the performance of physical desktops, model their usage, predict the virtualized user experience and based on the results, determine which desktops can be virtualized and which can't. This is what many people refer to as “VDI assessment”.

One of the fundamental changes with VDI is that the desktops no longer have dedicated resources. They share the resources of the physical machine on which they are hosted and they may even be using a common storage subsystem.

While resource sharing provides several benefits, it also introduces new complications. A single malfunctioning desktop can take so much resources that it impacts the performance of all the other desktops. Whereas in the physical world, the impact of a failure or a slowdown was minimal (if a physical desktop failed, it would impact only one user), the impact of failure or slowdown in the virtual world is much more severe (one failure can impact hundreds of desktops). Therefore, even in the VDI assessment phase, it is important to take performance considerations into account.

In fact, performance has to be considered at every stage of the VDI lifecycle because it is fundamental to the success or failure of the VDI rollout. The new types of inter-desktop dependencies that exist in VDI have to be accounted for at every stage.

For example, in many of the early VDI deployments, administrators found that when they just migrated the physical desktops to VDI, backups or antivirus software became a problem. These software components were scheduled to run at the same time on all the desktops. When the desktops were physical, it didn’t matter, because each desktop had dedicated hardware. With VDI, the synchronized demand for resources from all the desktops severely impacted the performance of the virtual desktops. This was not something that was anticipated because the focus of most designs and plans was on the individual desktops.


Understanding the performance requirements of desktops may also help plan the virtual desktop infrastructure more efficiently. For example, known heavy CPU using desktop users can be load balanced across servers. Likewise, by planning to assign a good mix of CPU intensive and memory intensive user desktops are assigned to a physical server, it is possible to get optimal usage of the existing hardware resources.

Lessons Learned

Taking this discussion one step further, it is interesting to draw a parallel with how server virtualization evolved and to see what lessons we can learn as far as VDI is concerned.

A lot of the emphasis in the early days was on determining which applications could be virtualized and which ones could not. Today, server virtualization technology has evolved to a point where there are more virtual machines being deployed in a year than physical machines, and almost every application server (except very old legacy ones) are virtualized fairly well. You no longer hear anyone asking whether this application server can be virtualized or not. From focusing on the hypervisor, virtualization vendors have realized that performance and manageability are key to the success of server virtualization deployments.


VDI deployments could be done more rapidly and more successfully if we learn our lessons from how server virtualization evolved. VDI assessment needs to expand in focus on just the desktop and look at the entire infrastructure. Attention during VDI rollouts has to be paid to performance management and assurance. To avoid a lot of rework and problem remediation down the line, performance assurance must be considered early on in the process and at every stage. This is key to getting VDI deployed on a bigger scale and faster, with great return on investment (ROI).

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Performance Assurance - A Key to Virtual Desktop Success

Very often, when an enterprise starts on the virtual desktop journey, the focus is on the user desktop. This is only natural - after all, it is the desktop that is moving - from being on a physical system to a virtual machine.

Therefore, once a decision to try out VDI is made, the primary focus is to benchmark the performance of physical desktops, model their usage, predict the virtualized user experience and based on the results, determine which desktops can be virtualized and which can't. This is what many people refer to as “VDI assessment”.

One of the fundamental changes with VDI is that the desktops no longer have dedicated resources. They share the resources of the physical machine on which they are hosted and they may even be using a common storage subsystem.

While resource sharing provides several benefits, it also introduces new complications. A single malfunctioning desktop can take so much resources that it impacts the performance of all the other desktops. Whereas in the physical world, the impact of a failure or a slowdown was minimal (if a physical desktop failed, it would impact only one user), the impact of failure or slowdown in the virtual world is much more severe (one failure can impact hundreds of desktops). Therefore, even in the VDI assessment phase, it is important to take performance considerations into account.

In fact, performance has to be considered at every stage of the VDI lifecycle because it is fundamental to the success or failure of the VDI rollout. The new types of inter-desktop dependencies that exist in VDI have to be accounted for at every stage.

For example, in many of the early VDI deployments, administrators found that when they just migrated the physical desktops to VDI, backups or antivirus software became a problem. These software components were scheduled to run at the same time on all the desktops. When the desktops were physical, it didn’t matter, because each desktop had dedicated hardware. With VDI, the synchronized demand for resources from all the desktops severely impacted the performance of the virtual desktops. This was not something that was anticipated because the focus of most designs and plans was on the individual desktops.


Understanding the performance requirements of desktops may also help plan the virtual desktop infrastructure more efficiently. For example, known heavy CPU using desktop users can be load balanced across servers. Likewise, by planning to assign a good mix of CPU intensive and memory intensive user desktops are assigned to a physical server, it is possible to get optimal usage of the existing hardware resources.

Lessons Learned

Taking this discussion one step further, it is interesting to draw a parallel with how server virtualization evolved and to see what lessons we can learn as far as VDI is concerned.

A lot of the emphasis in the early days was on determining which applications could be virtualized and which ones could not. Today, server virtualization technology has evolved to a point where there are more virtual machines being deployed in a year than physical machines, and almost every application server (except very old legacy ones) are virtualized fairly well. You no longer hear anyone asking whether this application server can be virtualized or not. From focusing on the hypervisor, virtualization vendors have realized that performance and manageability are key to the success of server virtualization deployments.


VDI deployments could be done more rapidly and more successfully if we learn our lessons from how server virtualization evolved. VDI assessment needs to expand in focus on just the desktop and look at the entire infrastructure. Attention during VDI rollouts has to be paid to performance management and assurance. To avoid a lot of rework and problem remediation down the line, performance assurance must be considered early on in the process and at every stage. This is key to getting VDI deployed on a bigger scale and faster, with great return on investment (ROI).

Hot Topics

The Latest

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...