This post details about How does Target tracking work?, What is the difference between target scaling and step scaling?, What is the target tracking policy cooldown?
How does Target tracking work?
Target tracking works by specifying a target value for a metric that autoscaling should maintain. The autoscale policy adjusts the number of instances in your autoscale group in response to changes in the metric.
For example, if you set a target tracking policy to maintain an average CPU utilization of 50%, autoscaling automatically adjusts the number of instances to keep CPU utilization near that target, the scale or in need.
What is the difference between target scaling and step scaling?
Step scaling and target scaling are two different scaling policies in AWS Auto scaling. Target scaling adjusts the number of instances proportionally to the metric value to reach and maintain the target value.
In contrast, step scaling adds or removes instances in larger increments (steps) based on predefined thresholds and scaling adjustments. Target scaling is smoother and adjusts more gradually, while step scaling can result in more abrupt changes in instruction counts based on metric thresholds.
What is the target tracking policy cooldown?
Target Tracking Policy Cooldown is a feature that prevents autoscaling from launching or terminating more instances before the previous scaling activity takes effect.
This cooldown period helps stabilize the instance count and avoid rapid fluctuations caused by consecutive scaling events. The duration of the cooldown period can be configured based on your application requirements and the time it takes for new instances to start and become fully operational.
Step scaling in AWS Auto Scaling adjusts the number of instances in your auto-scaling group based on the size of the alarm violation. When a CloudWatch alarm is triggered due to a metric violation, stage scaling performs scaling activities based on the predefined scaling adjustments.
For example, if CPU usage exceeds a threshold, stage scaling can add a fixed number of instances to handle increased load. It works in discrete increments (steps) rather than adjusting continuously like target tracking, making it suitable for applications with more predictable scaling needs.
Autoscaling in AWS automates the process of adjusting the number of EC2 instances in an autoscaling group based on user-defined policies, ensuring optimal performance and cost efficiency.
It monitors metrics such as CPU usage, network traffic, and custom metrics to dynamically adjust the number of instances. Autoscaling can scale instances in or out, replace unhealthy instances, and distribute instances across multiple availability zones to improve fault tolerance and availability. This elasticity allows applications to handle diverse workloads without manual intervention, improving both performance and cost-effectiveness in cloud environments.
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