| Title | Description | Owner of the Project | Contributor | Mail To | Tags | Created | Modified |
|---|---|---|---|---|---|---|---|
cloud-based-load-testing |
This project is owned by HCL Tech System Software team to support the Cloud based Load & Performance Testing on AWS. |
HCL Tech System Software Team |
HCL Tech System Software Team |
Load Testing, Performance Testing, Cloud based Load Testing, Cloud based Performance Testing, Cloud based Load & Performance Testing, AWS, Cloud, Infrastructure, Capacity |
2020 Jun 09 |
2020 Jun 16 |
Load and Performance tests are needed to validate the size of the actual or required hardware for deployment of Web Application(s) or HTTP(s) based API. Applications which are running over internet will give the minimum CPU and memory utilization if in case single user is using. In case of concurrent user conditions this looks totally different and will give too high system resources utilization and has an adverse impact over the response time. Companies are cutting their IT infrastructure costs as oversized hardware resources are too much expensive and difficult to afford. To achieve this Load and Performance tests will help to find the actual or appropriate size of the required hardware resources.
From an operational perspective, it’s essential to understand how and when a service or application starts to fail under certain workload situations. There are spiky Black Friday load scenarios or permanent high usage figures which could lead to serious issues. The companies need more temporary IT capacity, while the latter has different demands and requires a permanent increase in capacity. Load and Performance tests are the only measure which gives insights into these critical scenarios.
Performance testing is the name for tests that check how the system behaves and performs. Performance testing examines responsiveness, stability, scalability, reliability, speed and resource usage of the software (like Web Application(s) or HTTP(s) based API) and infrastructure. Performance testing comes in picture when there is the need to check the Web Application(s) or HTTP(s) based API performance, as well as servers, databases, networks, etc. Load testing is a subset of Performance testing that checks how systems function under a heavy number of concurrent virtual users performing transactions over a certain period of time.
Load and Performance tests are an excellent investment, primarily because they help build trust in the IT services and gives confidence to the organizations that the new or changed system performs within the agreed boundaries.
Main goal of Load testing is to verify that the web server can work correctly with certain number of concurrent users. During load test number of virtual users will access the application to produce heavy load and checks whether the server works fine with these number of users.
The purpose of web server load testing is to simulate real-life conditions for the application under test and determine how the application behaves when it is working under a massive load. Web server load testing can give answers to the following typical questions:
Does my server support N concurrent users?
How many users can simultaneously work with the web server without a perceptible slowdown?
How does the server response time change if you increase or decrease the number of users?
During a load test, we can find out how the website, app, or any other digital product behaves when a lot of users try to access it at the same time. In the particular test, we can focus on specific parts, for example the checkout process for a shopping cart.
A load test is normally performed before the release of product, or before a traffic-heavy event like Black Friday or a big TV commercial where a lot of traffic is expected to follow. On a website performance test, the performance of the server, database, and network of a digital product is checked. The response time of the website is especially important and is an area where we quickly might discover bottlenecks. It’s helpful to test the performance of the digital product regularly to make sure code changes don’t have a negative impact.
This project can be used to determine the performance of request(s) traffic coming to the application(s) which are running over internet in form of Web Application(s) or HTTP(s) based API. Based on the outcome metrics the system architect can guide or help the developers to optimize their application code so the response time can be reduced (if possible).
This project provides the platform where AWS services and Taurus (i.e. Open source Docker image for Load and Performance test) will create the virtual traffic and perform the test to identify the gaps or performance issues inside the application(s). Using this project, we can easily understand how our application(s) will be performing at scale and at load and identifying the bottlenecks before releasing the application(s) to production.
The following AWS services have been used in this project:
• Elastic Container Registry • Elastic Container Service • Fargate • Lambda • Simple Storage Service • DynamoDB • Simple Queue Service • Simple Notification Service • CloudWatch • API Gateway • Cognito • CloudFront • Virtual Private Cloud • CodePipeline • CodeBuild • Identity and Access Management • Amplify • CloudFormation
Please note:
The published code is in compiled form and it is only supported with AWS. It will be deployed on AWS with the help of CloudFormation.
The following steps will be required to deploy this project over AWS:
Step1: Create a new bucket or use a existing bucket from S3.
Step2: Clone the repository to local system using git command or download the zip version from the Github.
git clone https://github.com/HCLTech-SSW/cloud-based-load-testing.git
Step3: Using AWS CLI or Amazon console upload the code to existing or newly created S3 bucket (created or specified in Step1 above).
Step4: Edit the “stack-deploy.yaml” for the below set of Italics, and re-upload the updated file to S3 bucket (created or specified in Step1 above).
S3Bucket: name of source bucket where the cloned git repo will be uploaded/
KeyPrefixHelperService: folder name where the cloned git repo will be uploaded in S3/function-as-service/helper-service
KeyPrefixBackendService: folder name where the cloned git repo will be uploaded in S3/function-as-service/backend-service
KeyPrefixTestResultParser: folder name where the cloned git repo will be uploaded in S3/function-as-service/test-result-parser
KeyPrefixTestRunner: folder name where the cloned git repo will be uploaded in S3/function-as-service/test-runner
KeyPrefixContainer: folder name where the cloned git repo will be uploaded in S3/container
KeyPrefixUserInterface: folder name where the cloned git repo will be uploaded in S3/user-interface
Step5: From Amazon console go to Amazon CloudFormation and click “Create stack” and select “With new resources (standard)” option.
Step6: Specify the Amazon S3 URL for template file (location of “stack-deploy.yaml” file in S3 bucket) and click Next.
Step7: Specify the mandatory parameters like “Stack name”, “Console Administrator Name” and “Console Administrator Email”. Also please review the “AWS Fargate VPC cidr block”, “AWS Fargate Subnet A cidr block”, “AWS Fargate Subnet B cidr block” and “AWS Fargate SecurityGroup cidr block”.
Step8: The stack creation process now starts, and it will deploy the stack on AWS in 10-15 minutes.
Step9: Once the creation process is successfully completed, the confirmation email will be send to the email specified in “Console Administrator Email”. That email provides the link of Dashboard, UserID and temporary Password.
Step10: Click the Dashboard link, and specify “User Name” and “Password”, it will login to Dashboard.
Step1: Click CREATE TEST, and specify the values required for creating test, as shown in the following screenshot (currently filled with sample values).
Please Note:
- Specify the Web Application URLs/HTTP(s) endpoints with HTTP request method from GET, PUT, POST, and DELETE and specify additional information like HTTP Headers and Body Payload data in json format.
- The confirmation mail will be send to the email id specified in the Valid Email to Notify, please confirm the subscription so the test results can be notified over the email (the email subscription is mandatory).
Step2: The control will be routed to Dashboard, where the Test entry has been listed in RUNNING status.
Step3: Click the link under Details column, the detailed information related to that Test will be displayed on the screen.
Step4: Wait for the Test status to change from RUNNING to COMPLETED, post completion the status mail will be send to specified email covering the Test Results.
Stpe5: After the test status is COMPLETED the generated traffic graph and test metrics provides the information for load & performance responses (parameters details described in “About Test Metrics” section).
Step6: Test can be modified by clicking on UPDATE, where the Test related information can be modified.
Step7: Test can be deleted by clicking on DELETE and will need the confirmation for deletion.
• concurrency: This parameter gives the average number of Virtual Users running HTTP requests.
• throughput: This parameter provides information about the total count of all sample requests.
• succ: This parameter provides information about the total count of not-failed sample requests.
• fail: This parameter provides information about the total count of failed requests.
• avg_rt: This parameter signifies the average response time in getting the response from the HTTP(s)/Web URLs
Endpoints for serving the requests.
• stdev_rt: This parameter provides information about the standard deviation of response time from all the incoming
requests.
• avg_ct: This parameter provides information about the average connect time if present in any HTTP request.
• avg_lt: This parameter gives information about the average latency if present in any HTTP request.
• rc_200: This parameter provides the counts for specific response codes here it is HTTP status code 200.
• perc_0.0 .. perc_100.0: This parameter defines the percentile levels for response time, 0 is also minimum response
time, 100 is maximum.
• Bytes: This parameter denotes the total download size for the packets sent or received over HTTP.
Load Testing Limits: The maximum number of tasks that can be running in Amazon Elastic Container Service (Amazon ECS) using the AWS Fargate launch type is 50 per AWS Region, per account. For more information, see Amazon ECS Service Limits. For instructions on how to request an increase, see AWS Service Limits in the AWS General Reference Guide.
The Taurus load testing Docker image can generate up to 200 concurrent connections per task which means that the maximum load for a single test is 10,000 concurrent requests (200 connections * 50 running tasks at a time). The maximum execution time for a single test is four hours.
Concurrent Tests: This solution includes an Amazon CloudWatch dashboard that displays the combined output of all tasks running in the Amazon ECS cluster in real time. Only one CloudWatch log group can be assigned to an ECS cluster. This solution’s web console supports one running test at a time. If you run more than one test at a time, the dashboard will show the combined results from all tests.
Amazon EC2 Testing Policy: You do not need approval from AWS to run load tests using this solution as long as your network traffic stays below 1 Gbps. If your test will generate more than 1 Gbps, contact AWS. For more information, see the Amazon EC2 Testing Policy.