Request Based AutoScaling using AWS Target Tracking Scaling Policies. As the documentation explains, “With target tracking scaling policies, you select a metric and set a target value. Amazon ECS Service Auto Scaling creates and manages the CloudWatch alarms that trigger the scaling policy and calculates the scaling adjustment based on the metric and the target value.”
Traditionally, we have always known about autoscaling policies which help scale our infrastructure horizontally. These policies are generally configured to perform the scaling activities based on CloudWatch alarms. These alarms are generally known to trigger based on CPU and/or memory utilization.
However, in the microservices world especially for high and unpredicted traffic applications, these two metrics are just not enough to be able to confidently scale the services up and down. There are so many factors that come into play such as:
With such challenges, organizations tend to build their own custom scaling policies, as the defacto infrastructure metrics based scaling becomes less relevant.
In 2017 AWS launched TargetTracking scaling policies which allow users to use metrics such as requests per target, custom metrics, and a lot more than just the traditional CPU and Memory. The aim of this article is to demonstrate how these can be used on applications running in ECS (Fargate).
Our PoC application diagram
As shown in the above image, our application is a simple httpd container that is running on ECS Fargate. The aim of this PoC is to demonstrate how we can use request based scaling in combination with TargetTracking scaling policies to scale out our application based on the number of requests per container.
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