Reliability and Business Continuity

The correct answer is to implement AWS Fault Injection Simulator (FIS) experiments combined with CloudWatch Synthetics canaries. This approach directly addresses all the requirements:

1. AWS Fault Injection Simulator is purpose-built for chaos engineering and controlled failure injection testing. It allows you to safely inject failures into AWS resources including EC2, ECS, EKS, RDS, and networking components. FIS experiments can be scheduled to run monthly and include safeguards like stop conditions to minimize risk to production traffic.

2. CloudWatch Synthetics canaries provide synthetic monitoring that can validate end-to-end functionality of the application during and after fault injection. This ensures the entire application stack (application layer, data layer, and routing) is validated from a customer perspective.

3. This combination allows testing in the actual production environment with controlled, minimal-risk failures rather than relying on a separate test environment that may have its own configuration drift.

Why the other options are not optimal:

AWS Resilience Hub is excellent for assessing resilience posture and generating recommendations, but it is primarily an assessment and recommendation tool, not an active testing tool. It doesn't actually inject failures or validate real failover behavior - it analyzes your architecture and provides RTO/RPO assessments. This doesn't meet the requirement for active validation testing.

Systems Manager Automation runbooks with Route 53 health check modifications is a manual scripting approach that could work but has significant risks. Modifying Route 53 health checks directly affects production traffic routing and lacks the built-in safety guardrails that FIS provides. AWS Config rules detect drift but don't validate actual failover functionality.

Creating a separate testing environment introduces the same configuration drift problem the company already experienced. Test environments naturally diverge from production over time, and AWS Application Migration Service is designed for server migration, not configuration synchronization. Testing in an isolated environment doesn't validate that the actual production DR architecture works correctly.

The correct answer is to reduce the default cooldown period value to allow scaling actions to occur more frequently during sustained demand.

The scenario describes a classic cooldown period issue. When using simple scaling policies in Auto Scaling groups, there is a default cooldown period (typically 300 seconds or 5 minutes) that prevents additional scaling activities from occurring immediately after a previous scaling action completes. This is exactly what the operations team observed - the first scale-out triggered, but subsequent scaling was blocked for 5 minutes even though queue depth continued to rise.

The cooldown period is specifically designed to prevent Auto Scaling from launching or terminating additional instances before the effects of previous activities are visible. However, in scenarios with rapidly increasing demand (like the product launch with queue depth rising to 2000+ messages), a shorter cooldown period allows the ASG to respond more quickly to sustained high demand.

The other options are incorrect for the following reasons:

• The health check grace period determines how long Auto Scaling waits before checking the health of newly launched instances. It does not affect the timing between consecutive scaling activities. Reducing this would only cause health checks to start sooner on new instances, not allow more frequent scaling actions.

• The instance warmup period affects how target tracking and step scaling policies account for newly launched instances in metric calculations. While related to scaling behavior, it specifically controls when new instances are included in aggregate metrics, not the waiting period between scaling actions in simple scaling policies.

• Adjusting evaluation periods in CloudWatch alarms affects how quickly an alarm state changes based on metric data, but once the alarm triggers and scaling occurs, the cooldown period still governs when the next scaling action can happen. Even with shorter evaluation periods, the 5-minute block between scaling activities would remain unchanged.

The most likely explanation for this behavior is that larger volumes with more data blocks require longer snapshot creation times. EBS snapshots are incremental and copy data blocks asynchronously to Amazon S3. While the snapshot API call returns immediately (the snapshot enters 'pending' state), the actual data transfer to S3 takes time proportional to the amount of data being copied.

The 2 TB io2 volumes have significantly more data blocks to process compared to the 500 GB gp3 volumes (4x the size). Even though the snapshot operation is initiated at the same 03:00 UTC schedule time, the completion time varies based on volume size and the amount of changed data since the last snapshot. The appropriate resolution is to either adjust backup reporting expectations to account for this variability, or create separate DLM policies with staggered schedules so larger volumes start earlier and complete within the desired reporting window.

The explanation about high IOPS causing DLM throttling is incorrect. Provisioned IOPS on io2 volumes does not affect snapshot operation speeds or cause DLM to throttle. Snapshot operations use a separate background process and do not consume or compete with provisioned IOPS.

The claim that DLM processes volumes sequentially based on size is also incorrect. DLM initiates snapshots for all targeted volumes at the scheduled time, not sequentially. The snapshots are created in parallel, but completion times vary based on volume characteristics.

The suggestion that high IOPS configuration creates additional metadata affecting snapshot times is incorrect. The IOPS configuration is a volume setting and does not add metadata to the snapshot process. Switching volume types would not standardize completion times and could negatively impact application performance, making this an inappropriate solution.