What Is Desktop Auto-Scaling In Nerdio?

Applies to: Nerdio For Azure (NFA) Enterprise customers only for both RDS and AVD.

Nerdio offers a unique feature called "Desktop Auto-scaling" which helps customers optimize their costs by paying only for the Nerdio resources used. Desktop auto-scaling takes advantage of the elasticity of cloud-hosted environments to increase or decrease resources as per individual customer requirements. The feature reduces the need for an operator to continually monitor the performance of a system and take decisions regarding adding or removing resources on demand.

Nerdio supports two types of auto-scaling:

  • Vertical scaling: Vertical scaling, also known as desktop auto-scaling, involves changing the capacity (such as CPU speed, disk space or memory) of a resource. For example, shifting an application onto a more powerful VM size is vertical scaling. Vertical scaling requires making the system temporarily unavailable while it is being redeployed.

  • Horizontal scaling: Horizontal scaling also called scaling out and in, involves adding or removing multiple instances of a resource. In case of horizontal scaling, the application continues running without interruption as new resources are provisioned. Once the provisioning process is complete, the application is deployed on these additional resources. In case the demand drops, the additional resources can be shut down or de-allocated. Nerdio implements horizontal scaling through RDS Collections.

How does desktop auto-scaling work?

On your account's home page you will see a chart titled "Desktop Login Pattern - Last 30 days" as shown below:


This chart gives you a fair idea about your NFA account's usage. This information helps you uncover opportunities to save money by using the "Desktop Autoscale" feature. With "Desktop Autoscale" feature, Nerdio can scale down or turn off RDS servers and VDI deskops when not in use. Secondly, this information helps you gauge when you can expect support requests to start coming in and staff support personnel accordingly. Let us look at how to enable desktop auto-scale feature.

How do I enable desktop auto-scaling on my account?

To enable desktop auto-scaling, login to Nerdio Admin Portal (NAP) as an IT admin. Login to a specific account (say Test account 5251). From the main menu, select option Optimize as shown below: 


Expand the Optimize menu and select Desktop auto scale tab as shown below:


Through the "Manage Desktop Auto-scale" tab, you need to first enable Desktop auto-scale option by moving the slider to the left as shown below:


Next you need to create various user profiles, depending on your needs:

  • Workaholic : Users that want their desktops to be available 24x7
  • Productivity : Users that need their desktops outside of regular business hours
  • Work/Life balance : Users that need their desktops during regular business hours only

You may change the up-time of Nerdio resources and assign different users to different profiles using the "Edit desktop autoscale profile" option as shown below:


You can study your usage patterns and turn off the resources when not in use. For example, if most users use their Nerdio desktops between 7am and 5pm, you can create a "Desktop auto-scale" profile called "Work-life balance" and turn off the resources after standard business hours, thus saving money.

Nerdio also offers an option to change "VM sizing action plans" for its RDS and VDI desktop users as shown below: 


Note: Nerdio will automatically resize RDSH servers based on the user profiles you have set above. In this section you can lookup the instance sizes by day of the week. Optionally, you can override the automated process and select instance sizes for RDSH servers. Note that VDI desktops will be shut down during non-work hours.

Click "View plan" to change the configuration of your existing desktops:


You can view the number of desktop users at a particular time of the day.

Nerdio also offers a unique feature called "server auto-scaling" which helps you shut-down servers when not in use or scale them down when the workload shrinks. Refer article below for more details:








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