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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

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...