IoT Healthcare Platform - AWS ECS/Fargate Migration and Cloud Operations

Business Problem

The platform needed a structured AWS migration path with secure networking, container deployment, managed data services, monitoring, disaster recovery thinking, and operational handover.

Architecture

flowchart TB
  Users[Users] --> R53[Route 53]
  R53 --> CF[CloudFront]
  CF --> WAF[AWS WAF]
  WAF --> S3[S3 Frontend Assets]
  WAF --> ALB[Application Load Balancer]
  subgraph VPC[AWS VPC]
    subgraph Public[Public Subnets]
      ALB
      NAT[NAT Gateway]
    end
    subgraph Private[Private Subnets]
      ECS[ECS Fargate Services]
      RDS[(RDS PostgreSQL)]
      DOC[(DocumentDB)]
      REDIS[(ElastiCache Redis)]
    end
  end
  ECR[Amazon ECR] --> ECS
  ECS --> Secrets[Secrets Manager]
  ECS --> CW[CloudWatch]
  ECS --> RDS
  ECS --> DOC
  ECS --> REDIS
  IAM[IAM Roles] --> ECS
  SG[Security Groups] --> ALB
  SG --> ECS
  SG --> RDS

My Responsibilities

• Planned ECS/Fargate hosting patterns for backend services.
• Documented VPC, public/private subnet, ALB, Route 53, CloudFront, WAF, ECR, S3, and Secrets Manager usage.
• Prepared database connectivity and migration notes for managed AWS data services.
• Defined CI/CD, environment configuration, monitoring, DR, and decommission handover documentation.

Implementation

• Used ALB for service entry and private subnets for containerized workloads.
• Designed secrets and configuration handling around managed AWS services.
• Mapped frontend delivery through S3 and CloudFront where applicable.
• Captured CloudWatch log and metric review steps for production support.

Challenges

• Migration planning had to balance security, cost, operations, and future scaling.
• Multiple managed data services required clear ownership and connectivity rules.
• Production support needed documentation beyond the initial deployment.

Solution

The solution combined automation, clear infrastructure boundaries, operational validation, and documentation-first handover. The design focuses on traceable releases, least-privilege access, predictable runtime behavior, and observable production systems.

Code Snippet

Representative public-safe snippet showing the type of implementation pattern used.

resource "aws_security_group_rule" "rds_from_ecs" {
  type                     = "ingress"
  from_port                = 5432
  to_port                  = 5432
  protocol                 = "tcp"
  security_group_id        = aws_security_group.rds.id
  source_security_group_id = aws_security_group.ecs_tasks.id
}

Deployment Flow

1. 1

   AWS foundation planning

2. 2

   Container runtime design

3. 3

   Data service mapping

4. 4

   CI/CD and monitoring

5. 5

   DR and handover

Validation

• Verified deployment health before routing user traffic.
• Reviewed logs, metrics, events, and runtime configuration.
• Documented rollback and post-deployment checks for support teams.

Monitoring and Security

• Monitoring focused on logs, runtime status, health checks, alarms, and deployment evidence.
• Security guidance covered IAM roles, security groups, secrets handling, and private network boundaries where applicable.
• Operational notes included backup, restore, rollback, and incident response expectations.

Outcome and Impact

• Produced a clear AWS migration and support model.
• Improved security posture through private networking and managed secrets patterns.
• Improved operational clarity for monitoring, DR, and handover.

Lessons Learned

• Reliable DevOps delivery depends on validation, not only deployment automation.
• Runbooks and handover notes reduce operational risk during production support.
• Health checks, logs, access boundaries, and rollback steps should be designed before go-live.

Related Incident Experience

• Kubernetes deployment issue
• Storage lifecycle recovery
• Service startup failure
• Pipeline rollback

Pipeline Screenshots Placeholder