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Difficulty: MediumAuto Scaling and Elasticity

A CPU-intensive data analysis application runs on Amazon EC2 instances within an Auto Scaling group. The group uses a target tracking scaling policy based on average CPU utilization. The workload arrives in unpredictable batches. The operations team notices two problems:

1. When a new batch of workload arrives, it takes up to 10 minutes before the Auto Scaling group begins to launch new instances, causing a temporary performance bottleneck.
2. Once scaling begins, the group frequently launches more instances than are needed to handle the load, resulting in underutilized resources and unnecessary costs.

Which two actions should the administrator take to address these problems? (Select TWO.)

  1. Enable detailed monitoring for the EC2 instances in the launch template used by the Auto Scaling group.Answer
  2. Increase the estimated instance warmup time in the target tracking scaling policy configuration.Answer
  3. C
    Decrease the estimated instance warmup time in the target tracking scaling policy configuration.
  4. D
    Enable detailed monitoring for the Auto Scaling group metrics in the AWS Management Console.
  5. E
    Create an Amazon EventBridge rule that triggers an AWS Systems Manager Automation document to increase group capacity when CPU utilization spikes.

Answer

Enable detailed monitoring for the EC2 instances in the launch template, and increase the estimated instance warmup time in the target tracking scaling policy.
Enabling detailed monitoring on the EC2 instances in the launch template changes the CPU utilization reporting frequency from 5 minutes to 1 minute, which reduces the time required for CloudWatch to trigger scaling actions. Increasing the estimated instance warmup time ensures that newly launched instances are not included in the average metric calculations until they are ready to handle the load, preventing the Auto Scaling group from launching unnecessary additional instances.

Step-by-Step Solution

1
Identify the cause of the scale-out delay.
The 10-minute delay is caused by CloudWatch receiving CPU utilization metrics at the default 5-minute interval (standard monitoring).
By enabling detailed monitoring in the launch template, EC2 instances will report CPU metrics every 1 minute, allowing CloudWatch to detect load spikes and trigger scaling much faster.
2
Identify the cause of the overprovisioning.
The Auto Scaling group launches too many instances because the scaling policy assumes the group still lacks capacity before the new instances are fully warmed up and processing load.
Increasing the estimated instance warmup time ensures that newly launched instances are excluded from the average CPU utilization calculation until they are ready, preventing redundant scale-out actions.

Key Concept

Auto Scaling responsiveness and overprovisioning prevention using detailed monitoring and instance warmup settings.
Estimated Time:2m 0s
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Topics

Auto Scaling and Elasticity

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