Streamlining Infrastructure Management and Enhancing End User Location Identification.

1. Introduction: This report outlines the architecture and implementation details of a prototype for hosting an application on AWS ECS (Elastic Container Service) with the Fargate launch type. The prototype integrates a Continuous Integration and Continuous Deployment (CI/CD) pipeline using AWS services like CodePipeline, Codebuild to automate the deployment process. ECR (Elastic Container Registry), ECS, and Secrets Manager are the AWS Services used for containerizing application and parameterizing credentials. AWS Lambda, Cloudfront and S3 bucket are used for end user geolocation identification.

2. Architecture Overview: The architecture includes the following components: GitHub Repository: Stores the source code of the application. CodePipeline: Manages the CI/CD workflow. AWS ECR: Hosts Docker images of the application. AWS ECS (Fargate): Provides a serverless container management service. AWS Secrets Manager: Stores and manages the necessary credentials securely. Cloudfront: Provides application with low latency to the end users. AWS Lambda: Serverless compute to extract and store the geo location of end users. S3 Bucket: Stores cloudfront logs, build artifacts and end user geolocation.

3. CI/CD Workflow: The CI/CD pipeline consists of two main stages: Build and Deploy.

Build Stage: CodePipeline detects changes in the GitHub repository. Initiates the build stage. Fetches the latest code from the repository. Builds a Docker image using the fetched code. Pushes the Docker image to the AWS ECR repository. Utilizes credentials from AWS Secrets Manager for authentication during the image push process.

Deploy Stage: Upon successful completion of the build stage, the deploy stage is triggered. CodePipeline deploy stage retrieves the latest Docker image from the build artifact. The ECS service, configured with the Fargate launch type, is updated with the latest Docker image. ECS manages the deployment of the containerized application in a serverless manner.

4. Geolocation Workflow: Cloudfront logs the end user headers into S3 when the application is accessed. On event trigger AWS Lambda (Python) extracts ip address from the cloudfront logs and hits the geolocation api. Map Reduce is performed on the response of api and geolocation details are stored in S3 bucket.

5. Implementation Details: GitHub Repository Setup: Configure the repository with appropriate branches and CI/CD triggers. CodePipeline Configuration: Define pipeline stages, source provider (GitHub), build settings (Docker), and deployment settings (ECS). AWS ECR Repository Creation: Create a repository in ECR to store Docker images. AWS ECS Cluster and Service Setup: Provision an ECS cluster and service with the Fargate launch type. AWS Secrets Manager Configuration: Securely store and manage necessary credentials in Secrets Manager, granting CodePipeline access. IAM Roles and Policies: Configure IAM roles and policies to allow CodePipeline to interact securely with AWS services. AWS Cloudfront: Delivers the application and stores the headers. AWS Lambda: To execute the python script that identifies and stores geolocation. S3 Bucket: To store the artifacts and outputs of AWS Services.

6. Benefits: Serverless Deployment: ECS Fargate eliminates the need to manage server infrastructure, allowing for efficient and cost-effective deployment. Automation: The CI/CD pipeline automates the deployment process, reducing manual intervention and enhancing reliability. Scalability: Fargate and Lambda automatically scale resources based on demand, ensuring optimal performance. Security: Secrets Manager ensures secure credential management, enhancing overall security posture. Content Delivery: CloudFront delivers content to users with low latency and high transfer speeds by caching content at edge locations. Serverless Execution: Lambda allows you to run code without provisioning or managing servers, reducing operational overhead. Object Storage: S3 provides a highly durable and scalable object storage solution for storing artifacts, logs, and other data generated by AWS services. Integrates seamlessly with other AWS services.

7. Conclusion: The prototype demonstrates an effective CI/CD implementation and geolocation identification for hosting an application on AWS ECS Fargate. By leveraging AWS services like CodePipeline, ECR, ECS, Secrets Manager, AWS Lambda, S3 and Cloudfront the deployment process is streamlined, automated, and secure. This approach enhances developer productivity, improves deployment reliability, and facilitates scalability for future growth.

8. Future Plans: Implement Infrastructure as Code using tools like AWS CloudFormation or Terraform to define and manage the AWS resources. Develop custom geolocation api. Implement monitoring and alerting using AWS CloudWatch Alarms or third-party tools to track performance metrics, resource utilization, and application health. Set up cross-region replication for critical data stored in S3 buckets to ensure data durability and disaster recovery capabilities.

9. References: AWS Documentation: https://docs.aws.amazon.com/ GitHub Documentation: https://docs.github.com/