Introduction to AWS ECS and the Need for Cost Optimization

Amazon Elastic Container Service (ECS) is a powerful, fully managed container orchestration service that allows organizations to quickly run and scale containerized applications. While ECS provides the flexibility to deploy and manage containers, cost optimization is crucial, particularly for non-production environments like development, staging, or testing. Non-production workloads often have fluctuating usage, making them perfect candidates for dynamic scaling to save costs. One effective way to achieve this is by using AWS Lambda to automate ECS scaling, ensuring that ECS services only run when necessary.

Setting Up AWS Lambda for ECS Scaling

AWS Lambda is a serverless computing service that runs your code in response to events and automatically manages computing resources. To optimize ECS scaling for non-production environments, Lambda can be set up to start or stop ECS services based on predefined criteria, such as schedules, resource utilization, or custom logic.

Here are the steps to set up AWS Lambda for ECS scaling:

  1. Create a Lambda function in the AWS Management Console.
  2. Select your Lambda function’s runtime environment, such as Python or Node.js.
  3. Write the function code interacting with the ECS API to start, stop, or scale your services.
  4. Configure Lambda triggers using AWS EventBridge to automate scaling based on specific events (e.g., at the start of a workday or when a build is triggered).

Crafting IAM Policies for Secure Lambda Execution

Security is a priority when automating infrastructure operations. You must ensure that Lambda has the necessary permissions to interact securely with ECS services. Crafting well-defined IAM (Identity and Access Management) policies is critical for securing Lambda execution.

Here’s how to create a secure IAM policy for Lambda:

  1. Define permissions: The IAM role associated with the Lambda function must have the permissions to describe, start, and stop ECS services, as well as any additional permissions required for associated resources like EC2 instances.
  2. Least privilege principle: Only provide the permissions necessary for Lambda to perform the required actions. For example:
    {

  “Version”: “2012-10-17”,

  “Statement”: [

    {

      “Effect”: “Allow”,

      “Action”: [

        “ecs:UpdateService”,

        “ecs:DescribeServices”,

        “ecs:ListClusters”

      ],

      “Resource”: “*”

    }

  ]

}

  1. Attach the IAM policy to the Lambda function’s execution role, ensuring it has secure and limited access.

Implementing Lambda Functions for Scaling ECS Services

The core logic of scaling ECS services through Lambda involves interacting with the ECS API. Lambda functions can trigger actions like increasing or decreasing the desired number of ECS tasks, starting or stopping services, or even adjusting the CPU and memory configurations.

For example, a simple Python function to scale an ECS service may look like this:

import boto3

def scale_ecs_service(cluster_name, service_name, desired_count):

    ecs = boto3.client(‘ecs’)

    response = ecs.update_service(

        cluster=cluster_name,

        service=service_name,

        desiredCount=desired_count

    )

    return response

def lambda_handler(event, context):

    cluster_name = “my-cluster”

    service_name = “my-ecs-service”

    desired_count = 2  # Scale to 2 tasks

    scale_ecs_service(cluster_name, service_name, desired_count)

When triggered, this function scales an ECS service to the desired number of tasks.

Automating ECS Scaling with AWS EventBridge

AWS EventBridge enables you to create rules that automatically trigger Lambda functions based on specific events. In the case of optimizing non-production ECS costs, you can schedule scaling actions or trigger them based on external events, such as builds or application updates.

To automate ECS scaling:

  1. Create an EventBridge rule: Define a schedule, such as scaling up ECS services at the beginning of the workday and scaling them down after hours.
  2. Target your Lambda function: Ensure that the rule targets the Lambda function you created for scaling the ECS service.

For example, to scale ECS services during business hours (9 AM to 6 PM):

{

  “source”: [“aws.events”],

  “detail-type”: [“Scheduled Event”],

  “schedule”: “cron(0 9 * * ? *)”  // Runs at 9 AM UTC

}

Optimizing Costs Through Scheduled Scaling

Scheduled scaling is an effective way to control costs in non-production environments where services are not required 24/7. By setting schedules for scaling ECS services up and down, you ensure that resources are only used when necessary.

Steps to implement scheduled scaling:

  1. Identify peak usage times: Determine when your development, testing, or staging teams need ECS services.
  2. Set EventBridge schedules: Create schedules to start ECS services during peak hours and stop them during off-hours.
  3. Review usage regularly: Monitor ECS and Lambda metrics using Amazon CloudWatch to ensure that scaling rules are effective and further optimized based on actual usage patterns.

Conclusion: Leveraging AWS Lambda for Efficient ECS Management

By integrating AWS Lambda with ECS, you can achieve dynamic and efficient scaling of your non-production environments, resulting in significant cost savings. Automating ECS scaling with AWS EventBridge and Lambda allows you to scale services based on schedules or usage patterns, ensuring that resources are only used when needed. With well-crafted IAM policies and scheduled scaling strategies, AWS Lambda becomes a powerful tool for managing ECS services while optimizing operational costs.

References

Optimize Cost by Automating the Start/Stop of Resources in Non-Production Environments

Optimizing your AWS Lambda costs