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Suboptimal AppStream Fleet Auto Scaling Policies
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Suboptimal AppStream Fleet Auto Scaling Policies
Jason DiDomenico
Service Category
Compute
Cloud Provider
AWS
Service Name
AWS AppStream 2.0
Inefficiency Type
Inefficient Configuration
Explanation

When fleet auto scaling policies maintain more active instances than are required to support current usage—particularly during off-peak hours—organizations incur unnecessary compute costs. Fleets often remain oversized due to conservative default configurations or lack of schedule-based scaling. Tuning the scaling policies to better reflect usage patterns ensures that streaming infrastructure aligns with actual demand.

Relevant Billing Model

AppStream streaming instances are billed by the hour based on instance type and the number of running instances, regardless of whether those instances are being actively used. Minimum instance counts and scheduled provisioning settings directly influence total cost.

Detection
  • Identify AppStream fleets where the minimum instance count consistently exceeds active session demand over a representative period
  • Review fleet usage patterns and idle capacity during off-peak hours or low-concurrency windows
  • Check whether auto scaling policies are based on real usage or static assumptions
  • Assess whether scheduled scaling is configured to adjust fleet size based on predictable usage cycles
Remediation
  • Adjust minimum instance counts in auto scaling policies to reflect observed demand
  • Implement schedule-based scaling to reduce instance counts during predictable low-usage periods and increase during peak hours
  • Regularly review and update scaling policies based on current usage data to ensure ongoing efficiency
Relevant Documentation
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