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How can IT identify and manage VDI usage fluctuations?

Unpredictable user behavior and boot storms can cause VDI resource usage fluctuations throughout the day. IT can take steps to identify and curtail the effects of these changes.

In any VDI deployment, it is normal for there to be fluctuations in resource usage.

Smaller fluctuations in VDI usage tend to be related to unpredictable end-user behaviors. A user might, for instance, decide to play a video or upload a large file, thereby causing a resource usage spike. These spikes are usually short-lived, however, and do not cause any problems as long as the virtual desktop density is not too high.

A bigger VDI usage issue, however, is the activity spikes that occur as a result of concurrent user activities. Users might cause an activity storm, for instance, as a result of everyone logging in at about the same time in the morning. These Boot storms eventually subside, but they can be highly disruptive until they do.

How to spot fluctuations in VDI usage and deal with them

Identifying VDI usage fluctuations is normally easy to do. Just using a virtual desktop on a day-to-day basis can give IT professionals a good sense of the ongoing activity patterns.

An IT pro might notice, for instance, that performance becomes sluggish at 9:00 a.m. for about 10 minutes, and then again at around noon when everyone heads to lunch. Even though such observations are subjective, IT can quantify sluggish performance during times of heavy activity using performance monitoring software.

IT pros can also use automation to manage VDI usage fluctuations by performing workload redistribution.

The key to overcoming these types of performance fluctuations is to take advantage of automation. If, for example, IT pros observe that a major VDI storm occurs each morning when everyone arrives for work, they can use automation to pre-boot the virtual desktops just prior to everyone's arrival.

IT pros can also use automation to manage VDI usage fluctuations by performing workload redistribution.

Suppose, for example, IT knows that a major resource spike occurs every day at noon. Depending on which hardware components are being over-utilized, they may be able to use an automation engine to automatically power up an additional server node and then live-migrate some of the virtual desktops to that node just before the resource spike occurs. This way, more hardware is available to cope with the demand.

When the usage spike is over, IT can set the deployment to automatically live-migrate those virtual desktops back to their original location and shut down the extra server node to save on power and cooling costs.

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