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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 14, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud network observability... 

While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...

Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...