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Identifying Misaligned Scaling Signals
In this lab, you'll investigate an AWS compute environment that exhibits unstable scaling behavior. Using CloudWatch dashboards, Auto Scaling activity, and controlled workload generation, you'll evaluate how scaling decisions relate to actual system demand. By correlating workload metrics with scaling events, you'll identify signals that are causing unnecessary scaling activity and determine whether the current scaling configuration reflects real workload behavior.
Lab Info
Table of Contents
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Challenge
Correlate workload demand with observed scaling activity
- Review workload and infrastructure metrics available in CloudWatch.
- Examine Auto Scaling activity alongside demand-related metrics.
- Use the provided dashboards to record the baseline signals for the application.
- Use the provided load generator to simulate CPU load on the ASG instances.
- Identify relationships between workload changes and capacity adjustments.
- Use the provided dashboards to record signal changes for the application.
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Challenge
Evaluate scaling signals for evidence of misalignment
- Compare scaling activity against CPU, request, and capacity metrics.
- Determine whether scaling events correspond to meaningful workload changes.
- Identify signals that may be causing unnecessary or excessive scaling activity.
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Challenge
Identify operational risks created by poor signal interpretation
- Analyze the effects of unnecessary scaling activity on system behavior.
- Recognize indicators of instability or overreaction within the Auto Scaling group.
- Document observations that suggest scaling thresholds may require future adjustment.
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