Distributed Load Testing on AWS

Distributed Load Testing on AWS helps you automate performance testing of your software applications at scale to identify bottlenecks before you release your application. This solution simulates thousands of connected users generating HTTP requests at a sustained rate without the need to provision servers.

• Deploy Amazon ECS on AWS Fargate containers that run independently to test the load capacity of your application.
• Simulate tens of thousands of concurrent users across multiple AWS Regions generating requests at a continuous pace.
• Customize your application tests using JMeter, K6, Locust test scripts, or simple HTTP endpoint configuration.
• Schedule load tests to run immediately, at a future date and time, or on a recurring schedule.
• Run multiple load tests concurrently across different scenarios and regions.

The intended audience for using this solution's features and capabilities in their environment includes IT infrastructure architects, administrators, and DevOps professionals who have practical experience architecting in the AWS Cloud. As a result, developers can understand the behavior of their applications at scale and at load to identify any bottleneck problems before they deploy to Production. Launch in the AWS Console

On this Page

• Architecture Overview
• Deployment
• Source Code
• Local Development
• Testing
• Creating a custom build

Architecture Overview

Deploying this solution with the default parameters deploys the following components in your AWS account.

Note: AWS CloudFormation resources are created from AWS Cloud Development Kit (AWS CDK) constructs.

The high-level process flow for the solution components deployed with the AWS CloudFormation template is as follows:

1. A distributed load tester API leverages Amazon API Gateway to invoke the solution's microservices (AWS Lambda functions).

2. The microservices provide the business logic to manage test data and run the tests.

3. These microservices interact with Amazon Simple Storage Service (Amazon S3), Amazon DynamoDB, and AWS Step Functions to store test scenario details and results and to orchestrate test execution.

4. An Amazon Virtual Private Cloud (Amazon VPC) network topology deploys containing the solution's Amazon Elastic Container Service (Amazon ECS) containers running on AWS Fargate.

5. The containers use an Amazon Linux 2023 base image with the Taurus load testing framework installed. Taurus is an open-source test automation framework that supports JMeter, K6, Locust, and other testing tools. The container image is Open Container Initiative (OCI) compliant and hosted by AWS in an Amazon Elastic Container Registry (Amazon ECR) public repository. For more information, refer to Container image customization.

6. A web console powered by AWS Amplify deploys into an S3 bucket configured for static web hosting.

7. Amazon CloudFront provides secure, public access to the solution's website bucket contents.

8. During initial configuration, the solution creates a default administrator role (IAM role) and sends an access invite to a customer-specified user email address.

9. An Amazon Cognito user pool manages user access to the console, the distributed load tester API, and the MCP Server.

10. After you deploy this solution, you can use the web console or APIs to create and run test scenarios that define a series of tasks.

11. The microservices use this test scenario to run ECS tasks on Fargate in the specified Regions.

12. When the test completes, the solution stores results in S3 and DynamoDB and logs output in Amazon CloudWatch.

13. If you enable the live data option, the solution sends CloudWatch logs from the Fargate tasks to a Lambda function during the test for each Region where the test runs.

14. The Lambda function publishes the data to the corresponding topic in AWS IoT Core in the Region where the main stack was deployed. The web console subscribes to the topic and displays real-time data while the test runs.

MCP Server Integration (Optional)

Note: The following steps describe the optional MCP Server integration for AI-assisted load testing analysis. This component is only deployed if you select the MCP Server option during solution deployment.

15. An MCP client (AI development tool) connects to the AWS AgentCore Gateway endpoint to access the Distributed Load Testing solution's data through the Model Context Protocol. AgentCore Gateway validates the user's Cognito authentication token to ensure authorized access to the MCP server.

16. Upon successful authentication, AgentCore Gateway forwards the MCP tool request to the DLT MCP Server Lambda function. The Lambda function returns the structured data to AgentCore Gateway, which sends it back to the MCP client for AI-assisted analysis and insights.

17. The Lambda function processes the request and queries the appropriate AWS resources (DynamoDB tables, S3 buckets, or CloudWatch logs) to retrieve the requested load testing data.

Deployment

The solution can be deployed using either CloudFormation or AWS CDK:

CloudFormation Deployment: The solution is deployed using a CloudFormation template. To simulate users from regions other than the region the solution is initially deployed in, a regional template must be deployed within the other desired regions. For details on deploying the solution, see the implementation guide.

AWS CDK Deployment: For custom deployments and direct infrastructure-as-code management, the solution's infrastructure is developed with AWS CDK v2. See the infrastructure README for detailed CDK deployment instructions including prerequisites, bootstrapping, and both primary and regional stack deployment.

Source Code

source/api-services
A NodeJS Lambda function for the API microservices. Integrated with Amazon API Gateway, used to manage test scenarios.

source/build-tools
Custom build scripts and utilities used for building and packaging the solution.

source/custom-resource
A NodeJS Lambda function used as a CloudFormation custom resource for sending operational metrics to AWS, configuration for regional testing infrastructure, and iot configuration.

source/infrastructure
A Typescript AWS Cloud Development Kit (AWS CDK) v2 package that defines the infrastructure resources to run the Distributed Load Testing on AWS solution.

It also uses the AWS Solutions Constructs aws-cloudfront-s3 package to define the CloudFront distribution and the S3 bucket that stores the content that makes up the UI.

source/integration-tests
Integration tests for the solution, including API tests and end-to-end console tests using Cypress. Includes comprehensive test suites for validating deployed solution functionality.

source/metrics-utils
A TypeScript package containing utilities for operational metrics collection and reporting used across the solution.

source/real-time-data-publisher
A NodeJS Lambda function used to publish the real time load test data to an IoT topic.

source/results-parser
A NodeJS Lambda function used to write the xml output from the docker images to Amazon DynamoDB and generate the final results for each test.

source/solution-utils
A NodeJS package that contains commonly used functionality that is imported by other packages in this solution.

source/task-canceler
A NodeJS Lambda function used to stop tasks for a test that has been cancelled.

source/task-runner
A NodeJS Lambda function that runs the Amazon ECS task definition for each test.

source/task-status-checker
A NodeJS Lambda function that checks if the Amazon ECS tasks are running or not.

source/webui
React-based single page application that provides the web console interface for the solution. Built with Cloudscape Design System, Redux for state management, and AWS Amplify for authentication. Authenticated through Amazon Cognito, this dashboard allows users to create tests and view results.

source/mcp-server
A TypeScript package that implements the Model Context Protocol (MCP) server for AI-assisted load testing analysis. Provides structured access to DLT data through AWS AgentCore Gateway.

Local Development

For developers working on the solution locally, this section provides step-by-step instructions for setting up and deploying DLT from source code.

Prerequisites

• Node.js 24.x or later
• AWS CLI configured with appropriate credentials
• Docker (for container image builds)

Environment Setup

1. Create environment configuration:

   cp .env.example .env
   

2. Configure your .env file with your specific values:

   # CDK Parameters
   TARGET_REGION=us-east-1                    # Your primary deployment region
   MAIN_STACK_NAME=distributed-load-testing-on-aws
   REGIONAL_STACK_NAME=distributed-load-testing-on-aws-regional
   
   # Stack Parameters
   ADMIN_NAME=your_username                   # Admin username (no spaces)
   ADMIN_EMAIL=your-email@example.com         # Admin email address
   
   # Deployed Stack Parameters (update after deployment)
   DLT_CONSOLE_BUCKET=                        # Will be populated after main stack deployment
   

Main Stack Deployment

1. Install dependencies:

   make install-deps
   

2. Build the web application:

   # optional, must build web app assets to update the CloudFront distribution
   # alternatively, you can run the web app locally
   make build-web-app
   

3. Deploy the main stack:

   make deploy-stack
   

4. Update your .env file with the deployed stack outputs:

   • Copy the ScenariosBucket output value to DLT_CONSOLE_BUCKET

5. Run the web app

   make run-web-app
   

Regional Stack Management

To enable load testing from multiple regions, you can deploy regional stacks that contain the testing infrastructure (ECS clusters, VPC, etc.).

1. Bootstrap a region (required before first deployment to a region):

   make bootstrap-region REGION=us-west-2
   

2. Deploy regional stack:

   make deploy-regional-stack REGION=us-west-2
   

3. Deploy to multiple regions:

   make bootstrap-region REGION=eu-west-1
   make deploy-regional-stack REGION=eu-west-1
   
   make bootstrap-region REGION=ap-southeast-1
   make deploy-regional-stack REGION=ap-southeast-1
   

Available Make Targets

• make install-deps - Install all project dependencies
• make build-web-app - Build the React web application
• make deploy-stack - Deploy the main DLT stack
• make diff-stack - Show differences for the main stack
• make create-changeset - Create a changeset for the main stack
• make run-web-app - Run the web application locally
• make bootstrap-region REGION=<region> - Bootstrap CDK in a specific region
• make deploy-regional-stack REGION=<region> - Deploy regional testing infrastructure

Development Workflow

1. Initial Setup:

   cp .env.example .env
   # Edit .env with your values
   make install-deps
   make build-web-app
   make deploy-stack
   # Update .env with stack outputs
   

2. Add Regional Testing Capacity:

   make bootstrap-region REGION=us-west-2
   make deploy-regional-stack REGION=us-west-2
   

3. Local Development:

   make run-web-app  # Starts local development server
   

Notes

• Regional stacks automatically register themselves with the main stack and will appear in the web console
• Each regional stack creates independent testing infrastructure in the specified region
• The main stack must be deployed before any regional stacks
• Regional stacks can be deployed to the same region as the main stack or different regions

Testing

The solution includes comprehensive testing capabilities:

Unit Tests: Run unit tests for all components using the provided script:

cd deployment
chmod +x ./run-unit-tests.sh
./run-unit-tests.sh

Creating a custom build

The solution can be deployed through the CloudFormation template available on the solution home page: Distributed Load Testing.

To make changes to the solution, download or clone this repository, update the source code and then run the deployment/build-s3-dist.sh script to deploy the updated Lambda code to an Amazon S3 bucket in your account.

Prerequisites

• Node.js 24.x or later
• S3 bucket that includes the AWS region as a suffix in the name. For example, my-bucket-us-east-1. The bucket and CloudFormation stack must be in the same region. The solution's CloudFormation template will expect the source code to be located in a bucket matching that name.

Running unit tests for customization

• Clone the repository and make the desired code changes.

git clone https://github.com/aws-solutions/distributed-load-testing-on-aws.git
cd distributed-load-testing-on-aws
export BASE_DIRECTORY=$PWD

• Run unit tests to make sure the updates pass the tests.

cd $BASE_DIRECTORY/deployment
chmod +x ./run-unit-tests.sh
./run-unit-tests.sh

Building distributable for customization

• Configure the environment variables.

export REGION=aws-region-code # the AWS region to launch the solution (e.g. us-east-1)
export BUCKET_PREFIX=my-bucket-name # prefix of the bucket name without the region code
export BUCKET_NAME=$BUCKET_PREFIX-$REGION # full bucket name where the code will reside
export SOLUTION_NAME=my-solution-name
export VERSION=my-version # version number for the customized code
export PUBLIC_ECR_REGISTRY=public.ecr.aws/aws-solutions # replace with the container registry and image if you want to use a different container image
export PUBLIC_ECR_TAG=v4.0_stable # replace with the container image tag if you want to use a different container image

• Build the distributable.

cd $BASE_DIRECTORY/deployment
chmod +x ./build-s3-dist.sh
./build-s3-dist.sh $BUCKET_PREFIX $SOLUTION_NAME $VERSION

Note: The build-s3-dist script expects the bucket name without the region suffix as one of its parameters.

• Deploy the distributable to the Amazon S3 bucket in your account.

  • Make sure you are uploading the files in deployment/global-s3-assets and deployment/regional-s3-assets to $BUCKET_NAME/$SOLUTION_NAME/$VERSION.

• Get the link of the solution template uploaded to your Amazon S3 bucket.

• Deploy the solution to your account by launching a new AWS CloudFormation stack using the link of the solution template in Amazon S3.

Creating a custom container build

This solution uses a public Amazon Elastic Container Registry (Amazon ECR) image repository managed by AWS to store the solution container image that is used to run the configured tests. If you want to customize the container image, you can rebuild and push the image into an ECR image repository in your own AWS account. For details on how to customize the container image, please see the Container image customization section of the implementation guide.

Collection of operational metrics

This solution sends operational metrics to AWS (the “Data”) about the use of this solution. We use this Data to better understand how customers use this solution and related services and products. AWS’s collection of this Data is subject to the AWS Privacy Notice.

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SPDX-License-Identifier: Apache-2.0