Ruminating on AWS Fargate Autoscaling

Amazon Fargate is a serverless compute engine that allows you to run containers without having to provision or manage servers. One of its powerful features is automatic scaling, which enables your application to adjust its capacity based on demand. This ensures optimal performance and cost efficiency.

Target Tracking and Step Scaling: A Dynamic Duo

Fargate automatic scaling primarily relies on two strategies: target tracking and step scaling. Let's delve into how these mechanisms work together to maintain desired application performance.

Target Tracking:

• Defining a Metric: You specify a metric that represents your application's performance or resource utilization. This could be CPU utilization, memory usage, or a custom metric.
• Setting a Target Value: You establish the desired target value for the metric. For instance, you might set a target CPU utilization of 70%.
• Continuous Monitoring: Fargate continuously monitors the actual metric value and compares it to the target.
• Scaling Actions: If the actual value deviates significantly from the target, Fargate triggers scaling actions to adjust the number of tasks.

Step Scaling: 

• Step Adjustments: Step scaling involves increasing or decreasing the number of tasks by a predefined step size.
• Scaling Policies: You define scaling policies that specify:
• Step size: The number of tasks to add or remove in each scaling action.
• Cooldown period:The minimum time between scaling actions to prevent excessive fluctuations.
• Thresholds:The deviation from the target metric that triggers scaling.

How They Work Together:

• Target Tracking: Fargate monitors the specified metric and determines if it's deviating from the target.
• Step Scaling: If the deviation exceeds the defined thresholds, Fargate applies the corresponding scaling policy.
• Adjustment: The number of tasks is increased or decreased by the step size.
• Evaluation: Fargate continues to monitor the metric and adjusts the number of tasks as needed to maintain the target value.

Imagine a web application/API that experiences sudden traffic spikes during peak hours. By using target tracking and step scaling, you can configure Fargate to automatically increase the number of tasks when demand surges, ensuring optimal performance for your users.