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Tuning Scaling Policies for Stability
In this lab, you'll tune an AWS compute environment to produce more stable and predictable scaling behavior. You'll replace CPU-based scaling with policies driven by Application Load Balancer request metrics, using CloudWatch alarms and step scaling policies to better align capacity changes with customer demand. You'll then apply controlled CPU-based and request-based workload changes to evaluate how the updated configuration responds. Finally, you'll confirm that the Auto Scaling group scales in response to sustained request demand rather than transient CPU activity.
Lab Info
Table of Contents
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Challenge
Evaluate existing scaling policies and identify tuning opportunities
Establish the Auto Scaling group's baseline behavior.
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Challenge
Tune scaling thresholds and cooldown behavior to improve stability
A. Review the CPU-based scaling policies, CloudWatch alarms, and workload and capacity signals currently controlling and describing Auto Scaling behavior.
B. Disable the CPU-based scaling policies and create CloudWatch alarms based on the RequestCountPerTarget metric.
- Configure a scale-out alarm at 1,000 requests per target.
- Configure a scale-in alarm at 500 requests per target.
- Add the new alarms to the provided CloudWatch dashboard.
C. Create request-based scaling policies with graduated scale-out thresholds and conservative scale-in behavior.
- Add one capacity unit when requests per target are between 1,000 and 1,500.
- Add two capacity units when requests per target reach 1,500 or more.
- Remove one capacity unit when requests per target fall to 500 or below.
D. Apply a 120-second instance warmup period to dampen rapid scaling reactions while new capacity becomes available.
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Challenge
Validate improved scaling behavior under controlled demand
A. Use the provided load generator to apply CPU-only and request-based workloads, and observe the resulting metrics and scaling activity.
B. Compare desired capacity and instance behavior before and after the policy changes.
C. Confirm that CPU-only activity no longer changes capacity and that request demand invokes the expected graduated scale-out, warmup, and conservative scale-in behavior.
D. Confirm that capacity adjustments occur more predictably and with fewer unnecessary scaling events.
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