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ASG Policy Types and Lifecycle Hooks

ASG Policy Types and Lifecycle Hooks

Learn Auto Scaling group (ASG) policy types and lifecycle hooks, focusing on automating and controlling the scaling of EC2 instances within an ASG.

For developers preparing for the AWS Certified Developer – Associate (DVA-C02) exam, understanding how to control Auto Scaling group (ASG) behavior is crucial. This lesson focuses on two key aspects of ASG functionality: scaling policies and life cycle hooks. These tools allow us to efficiently scale applications based on real-time metrics and enable fine-grained control over instance behavior during launch and termination. We’ll explore how to select the right policy type for different workload patterns and implement lifecycle hooks for custom initialization or graceful shutdowns—highly testable areas in the exam’s “Development” and “Deployment” domains.

Scaling policies

ASGs’ scaling policies are the brains of the group. They determine when and how the group should adjust the number of EC2 instances. Each policy type evaluates metrics and defines actions like launching or terminating instances.

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Let’s explore each to understand how they behave in real-world scenarios.

Simple scaling policies

Simple scaling policies are event-driven. A single CloudWatch alarm can trigger a single scaling activity. For example, if average CPU utilization exceeds 70%, the policy adds two EC2 instances. After the action, a cooldown period ensures stability before the next scaling event. This approach is easy to implement but may not be responsive enough for rapidly changing traffic.

Consider a blog site experiencing periodic traffic spikes precisely at noon. A simple scaling policy might add instances when the CPU crosses a set threshold. However, because it waits for cooldown periods after each scaling action, it might not be responsive enough to handle a sudden and large load increase, potentially leading to temporary performance degradation.

Step scaling policies

Step scaling policies introduce more nuance and control. They allow us to define different thresholds and corresponding scaling actions based on how far a metric deviates from its normal or desired state. Instead of a single action for a single threshold, step scaling can execute varying ...