Amazon AppStream 2.0 Scheduled Scaling Policies + DST
Managing the scalability of your Amazon AppStream 2.0 environment is crucial for maintaining performance and cost efficiency. One important aspect to consider when configuring scheduled scaling policies is accounting for daylight saving time (DST). This ensures that your scaling actions occur at the correct times throughout the year, even as the clocks change.
Published Nov 1, 2024
Amazon AppStream 2.0 is a fully managed application streaming service that allows you to stream desktop applications to any device without rewriting them. It provides a secure, scalable, and cost-effective solution for delivering applications to users. By leveraging AppStream 2.0, organizations can ensure that their applications are always available and perform optimally, regardless of user location or device.
A common use case for Amazon AppStream 2.0 scaling is in enterprise call centers and educational institutions often have predictable usage patterns, with peak demand during class hours and lower demand during evenings and weekends. By implementing scheduled scaling policies, these institutions can ensure that sufficient capacity is available during peak times while minimizing costs during off-peak hours. Additionally, dynamic scaling policies can be used to handle unexpected spikes in demand, such as during exam periods or special events.
Managing the scalability of your Amazon AppStream 2.0 environment is crucial for maintaining performance and cost efficiency. One important aspect to consider when configuring scheduled scaling policies is accounting for daylight saving time (DST). This ensures that your scaling actions occur at the correct times throughout the year, even as the clocks change.
In 2024, daylight saving time in the United States will end on Sunday, November 3, 2024, at 2:00 AM local time, when clocks are set back one hour.
By default, application auto-scaling (which AppStream uses) will utilize Coordinated Universal Time (UTC) as the time zone. Daylight saving time can significantly impact the timing of your scheduled scaling actions. If your scaling policies do not account for DST, you may find that your fleet scales out or in at incorrect times, leading to potential performance issues or unnecessary costs. By configuring your scheduled scaling policies to account for DST, you ensure that your scaling actions align with the actual usage patterns of your users, providing a seamless experience and optimizing resource utilization.
To create a scheduled scaling policy that accounts for DST, you need to specify the time zone in your API request.
Here’s an example of how to specify New_York Eastern time zone using the PutScheduledAction API:
In this example, the --timezone parameter is set to "America/New_York", which accounts for DST. This ensures that the scaling actions will automatically adjust when the time changes.
Here’s an example of how to check an existing scheduled scaling policy using the describe-scheduled-actions API:
Compared to a scheduled scaling policy that was created in the AppStream 2.0 Web Console without DST, you can see that the timezone parameter is not set. AppStream 2.0 utilize UTC time by default.
- Specify Time Zones: Always specify a time zone that observes DST when creating scheduled scaling policies. This ensures that your scaling actions occur at the correct local times throughout the year.
- Combine Scaling Policies: Consider combining different types of scaling policies, such as scheduled scaling and target tracking, to optimize performance and cost. This approach can help you handle both predictable and unpredictable demand patterns.
- Regular Reviews: Regularly review and adjust your scaling policies based on observed performance and usage patterns. Analyze historical data to identify trends and make proactive adjustments to your policies.
- Avoiding Scaling Churn: Monitor your fleet for high churn, which occurs when many users start and end sessions in a short period. Use target tracking scaling policies to offset churn by setting a capacity utilization target that accounts for user turnover.
- Provisioning Rate Limits: Be aware of provisioning rate limits, which impact how quickly instances can be added to your fleet. For example, AppStream 2.0 provisions at a maximum rate of 20 instances per minute for a single fleet.
- Monitoring and Adjusting: Set up CloudWatch alarms to monitor metrics like Insufficient Capacity Errors. This allows you to quickly adjust your scaling policies to optimize availability for users.
Accounting for daylight saving time in your Amazon AppStream 2.0 scheduled scaling policies is essential for maintaining optimal performance and cost efficiency. By specifying the appropriate time zone and regularly reviewing your policies, you can ensure that your scaling actions align with local time changes and effectively manage your AppStream environment.