Citrix DaaS

Analyze the effectiveness of Autoscale settings

February 7, 2024

Contributed by:

C C

To use this feature, enable the toggle Autoscale Insights in DaaS > Home > Preview features. It might take about 15 minutes for Autoscale Insights to show up after enabling it.

You can analyze the effectiveness of the Autoscale settings based on machine usage from the previous week. Through the analysis, you can gain these insights into the effectiveness of Autoscale settings:

Identify financial waste resulting from over-provisioning.
Determine whether the user experience is negatively impacted due to under-provisioning.
Ensure that the capacity provisioned is properly aligned with the machine usage.

To achieve this goal, follow these steps:

Select an Autoscale-enabled delivery group.

In the bottom pane, click the Autoscale Insights tab.

The following graph appears, showing the comparison between machine usage data from the previous week and the number of machines to be powered on based on Autoscale settings.

Autoscale insights

* The red vertical line identifies the current time.

The following table provides descriptions of the metrics shown in this graph.

Metric	Description
Autoscale-managed count	Total number of machines managed by Autoscale. Autoscale-managed count = Total number of machines in the delivery group – Number of machines in maintenance mode – Number of machines not tagged for Autoscale (If tagged Autoscale feature is enabled).
Autoscale powered-on count	Total number of machines powered on by Autoscale. Autoscale powered-on count = Schedule-based machine count + Load-based machine count.
Historical usage	Number of machines that were delivered to users.
Schedule-based	Number of machines that are powered on based on the Autoscale schedule-based settings ( Note: Schedule-based settings don’t apply to delivery groups of the static single-session OS type).
Load-based	Number of machines that are powered on based on the Autoscale load-based settings.

To check the effectiveness of Autoscale settings at a specific time slot, hover your mouse over that slot on the graph. An information box appears, showing the comparison results and detailed machine counts:
- Not cost effective. The historical usage is less than 90% of the Autoscale settings (Autoscale powered-on count). As a result, wasted capacity might exist.
- Poor user experience. The historical usage is more than 110% of the Autoscale settings (Autoscale powered-on count). As a result, users might experience longer wait times for machines to power on.
- Good balance between user experience and cost. The difference between the historical usage and the Autoscale settings (Autoscale powered-on count) is less than 10%. The Autoscale settings are aligned with the historical usage.
- No historical data available. No historical data is available. Possible causes include that Autoscale was enabled for the delivery group less than a week ago.
To highlight a date range based on an Autoscale schedule, select the schedule from the View by field.
Based on your analysis, adjust the Autoscale settings. For more information, see Schedule-based and load-based settings.

The official version of this content is in English. Some of the Cloud Software Group documentation content is machine translated for your convenience only. Cloud Software Group has no control over machine-translated content, which may contain errors, inaccuracies or unsuitable language. No warranty of any kind, either expressed or implied, is made as to the accuracy, reliability, suitability, or correctness of any translations made from the English original into any other language, or that your Cloud Software Group product or service conforms to any machine translated content, and any warranty provided under the applicable end user license agreement or terms of service, or any other agreement with Cloud Software Group, that the product or service conforms with any documentation shall not apply to the extent that such documentation has been machine translated. Cloud Software Group will not be held responsible for any damage or issues that may arise from using machine-translated content.

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Analyze the effectiveness of Autoscale settings

February 7, 2024

Contributed by:

C C

February 7, 2024

Contributed by:

C C

Analyze the effectiveness of Autoscale settings

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