Container Services: ECS vs EKS Decision Guide

Week 4 - AWS Container Orchestration Mastery

Introduction

Container orchestration has become the backbone of modern cloud applications, enabling organizations to deploy, manage, and scale containerized workloads efficiently. Amazon Web Services offers two primary container orchestration services: Amazon Elastic Container Service (ECS) and Amazon Elastic Kubernetes Service (EKS).

Choosing between these services can significantly impact your application architecture, operational complexity, and long-term scalability. This comprehensive guide will help you understand the nuances of each service and make an informed decision based on your specific requirements.

Container Orchestration Overview

What is Container Orchestration?

Container orchestration is the automated deployment, management, scaling, and networking of containers. It solves critical challenges in containerized environments:

• Service Discovery: How containers find and communicate with each other

• Load Distribution: Distributing traffic across multiple container instances

• Scaling: Automatically adding or removing containers based on demand

• Health Management: Monitoring container health and replacing failed instances

• Rolling Updates: Deploying new versions without downtime

• Resource Management: Efficiently allocating CPU, memory, and storage

Why AWS for Container Orchestration?

AWS provides several advantages for container workloads:

• Deep Integration: Seamless integration with other AWS services

• Managed Infrastructure: Reduced operational overhead

• Security: Built-in security features and compliance certifications

• Scalability: Auto-scaling capabilities based on various metrics

• Cost Optimization: Pay-per-use pricing models

Amazon ECS: AWS-Native Container Service

Amazon ECS is AWS's proprietary container orchestration service, designed to be simple, fast, and cost-effective for running Docker containers at scale.

Key Features of ECS

Task Definitions: JSON templates that describe how containers should run, including:

• Docker image specifications

• CPU and memory requirements

• Port mappings and environment variables

• IAM roles and security configurations

Services: Ensure desired number of tasks are running and healthy, with built-in load balancing and service discovery.

Clusters: Logical groupings of compute resources that can span multiple Availability Zones.

ECS Launch Types: Fargate vs EC2

ECS Fargate

Fargate is a serverless compute engine that removes the need to manage EC2 instances.

Advantages:

• Zero Infrastructure Management: No EC2 instances to provision or manage

• Right-Sizing: Pay only for the exact CPU and memory your tasks use

• Enhanced Security: Task-level isolation with dedicated compute environments

• Automatic Scaling: Built-in auto-scaling without capacity planning

• Simplified Operations: No patching, updating, or cluster management

Best Use Cases:

• Microservices with unpredictable traffic patterns

• Batch processing jobs

• Applications requiring strong isolation

• Teams wanting to focus on application development

Example Fargate Task Definition:

{
  "family": "web-app-fargate",
  "networkMode": "awsvpc",
  "requiresCompatibilities": ["FARGATE"],
  "cpu": "256",
  "memory": "512",
  "executionRoleArn": "arn:aws:iam::account:role/ecsTaskExecutionRole",
  "containerDefinitions": [
    {
      "name": "web-container",
      "image": "nginx:latest",
      "portMappings": [
        {
          "containerPort": 80,
          "protocol": "tcp"
        }
      ]
    }
  ]
}

ECS on EC2

Traditional model where you manage the underlying EC2 instances in your cluster.

Advantages:

• Cost Control: Better cost optimization for predictable workloads

• Resource Efficiency: Higher container density per compute resource

• Customization: Full control over instance types and configurations

• Persistent Storage: Support for EBS volumes and instance store

• GPU Support: Access to GPU instances for ML/AI workloads

Best Use Cases:

• Long-running applications with predictable resource needs

• Cost-sensitive workloads requiring maximum efficiency

• Applications needing specialized instance types

• Workloads requiring persistent local storage

Amazon EKS: Managed Kubernetes Service

Amazon EKS runs upstream Kubernetes and provides a managed control plane while giving you full access to Kubernetes APIs and ecosystem tools.

Key Features of EKS

Managed Control Plane: AWS manages the Kubernetes API server, etcd, and other control plane components across multiple AZs for high availability.

Node Groups: Managed groups of EC2 instances that serve as worker nodes, with support for:

• Auto Scaling Groups

• Multiple instance types

• Spot instances for cost optimization

• Custom AMIs and launch templates

Fargate Integration: Run Kubernetes pods on serverless Fargate infrastructure without managing worker nodes.

Add-ons: Managed installation and lifecycle of essential cluster components like:

• AWS Load Balancer Controller

• Amazon EBS CSI Driver

• CoreDNS

• kube-proxy

EKS Deployment Options

EKS with Managed Node Groups

apiVersion: v1
kind: Deployment
metadata:
  name: nginx-deployment
spec:
  replicas: 3
  selector:
    matchLabels:
      app: nginx
  template:
    metadata:
      labels:
        app: nginx
    spec:
      containers:
      - name: nginx
        image: nginx:1.21
        ports:
        - containerPort: 80
        resources:
          requests:
            memory: "64Mi"
            cpu: "250m"
          limits:
            memory: "128Mi"
            cpu: "500m"

EKS on Fargate

Fargate profiles determine which pods run on Fargate based on namespace and labels:

apiVersion: v1
kind: Pod
metadata:
  name: fargate-pod
  namespace: fargate-namespace
spec:
  containers:
  - name: app
    image: public.ecr.aws/nginx/nginx:1.21

Decision Matrix: ECS vs EKS

Choose ECS When:

Team Expertise

• Limited Kubernetes experience

• Preference for AWS-native solutions

• Small to medium-sized teams

Application Requirements

• Straightforward container orchestration needs

• AWS-centric architecture

• Quick time-to-market requirements

Operational Considerations

• Minimal operational overhead preferred

• Cost-sensitive projects

• Simple CI/CD pipelines

Choose EKS When:

Team Expertise

• Existing Kubernetes knowledge

• Multi-cloud or hybrid strategies

• DevOps-mature organizations

Application Requirements

• Complex orchestration needs

• Rich ecosystem tool requirements

• Advanced networking or storage needs

Operational Considerations

• Portability across cloud providers

• Extensive customization requirements

• Large-scale, complex deployments

Containers vs Serverless: When to Choose What

Container Orchestration (ECS/EKS) is Better For:

Long-Running Services

• Web applications and APIs

• Database services

• Message queues and streaming applications

Complex Applications

• Microservices architectures

• Applications with multiple interconnected components

• Services requiring persistent connections

Resource Optimization

• Applications with predictable resource usage

• High-throughput, low-latency requirements

• Custom runtime environments

Serverless (Lambda) is Better For:

Event-Driven Workloads

• File processing triggers

• API Gateway backends

• Schedule-based tasks

Unpredictable Traffic

• Infrequent workloads

• Highly variable traffic patterns

• Prototype and experimental applications

Simple Functions

• Single-purpose functions

• Short-duration tasks (< 15 minutes)

• Stateless operations

Container Security Considerations

ECS Security Best Practices

Task-Level Security

• Use task IAM roles for fine-grained permissions

• Implement secrets management with AWS Secrets Manager

• Enable container insights for monitoring

• Use VPC networking mode for network isolation

Image Security

{
  "containerDefinitions": [
    {
      "name": "secure-app",
      "image": "your-account.dkr.ecr.region.amazonaws.com/app:latest",
      "readonlyRootFilesystem": true,
      "user": "1000",
      "linuxParameters": {
        "capabilities": {
          "drop": ["ALL"]
        }
      }
    }
  ]
}

EKS Security Best Practices

Cluster Security

• Enable cluster logging

• Use IAM roles for service accounts (IRSA)

• Implement Pod Security Standards

• Regular cluster updates and patching

Network Security

apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: deny-all
spec:
  podSelector: {}
  policyTypes:
  - Ingress
  - Egress

RBAC Configuration

apiVersion: rbac.authorization.k8s.io/v1
kind: Role
metadata:
  name: app-reader
rules:
- apiGroups: [""]
  resources: ["pods", "services"]
  verbs: ["get", "list", "watch"]

Universal Security Practices

Image Scanning

• Use Amazon ECR vulnerability scanning

• Implement image signing and verification

• Regular base image updates

• Minimal base images (distroless, Alpine)

Runtime Security

• Container resource limits

• Non-root user execution

• Read-only root filesystems

• Capability dropping

Compliance and Governance

• AWS Config rules for compliance monitoring

• AWS Security Hub for centralized security findings

• Regular security assessments and penetration testing

Hands-On: Deploy a Containerized Application

Let's walk through deploying a sample web application on both ECS and EKS to demonstrate the practical differences.

Prerequisites

• AWS CLI configured

• Docker installed

• kubectl installed (for EKS)

• eksctl installed (for EKS)

Sample Application: Simple Node.js Web Server

Dockerfile

FROM node:16-alpine

WORKDIR /app

COPY package*.json ./
RUN npm ci --only=production

COPY . .

EXPOSE 3000

USER node

CMD ["node", "index.js"]

index.js

const express = require('express');
const app = express();
const port = 3000;

app.get('/', (req, res) => {
  res.json({
    message: 'Hello from containerized app!',
    timestamp: new Date().toISOString(),
    hostname: process.env.HOSTNAME
  });
});

app.get('/health', (req, res) => {
  res.status(200).json({ status: 'healthy' });
});

app.listen(port, () => {
  console.log(`App running on port ${port}`);
});

Deploy on ECS with Fargate

Step 1: Build and Push Image

# Create ECR repository
aws ecr create-repository --repository-name sample-web-app

# Get login command
aws ecr get-login-password --region us-west-2 | docker login --username AWS --password-stdin <account-id>.dkr.ecr.us-west-2.amazonaws.com

# Build and push
docker build -t sample-web-app .
docker tag sample-web-app:latest <account-id>.dkr.ecr.us-west-2.amazonaws.com/sample-web-app:latest
docker push <account-id>.dkr.ecr.us-west-2.amazonaws.com/sample-web-app:latest

Step 2: Create ECS Task Definition

{
  "family": "sample-web-app",
  "networkMode": "awsvpc",
  "requiresCompatibilities": ["FARGATE"],
  "cpu": "256",
  "memory": "512",
  "executionRoleArn": "arn:aws:iam::<account-id>:role/ecsTaskExecutionRole",
  "containerDefinitions": [
    {
      "name": "web-app",
      "image": "<account-id>.dkr.ecr.us-west-2.amazonaws.com/sample-web-app:latest",
      "portMappings": [
        {
          "containerPort": 3000,
          "protocol": "tcp"
        }
      ],
      "healthCheck": {
        "command": ["CMD-SHELL", "curl -f http://localhost:3000/health || exit 1"],
        "interval": 30,
        "timeout": 5,
        "retries": 3
      },
      "logConfiguration": {
        "logDriver": "awslogs",
        "options": {
          "awslogs-group": "/ecs/sample-web-app",
          "awslogs-region": "us-west-2",
          "awslogs-stream-prefix": "ecs"
        }
      }
    }
  ]
}

Step 3: Create ECS Service

# Create cluster
aws ecs create-cluster --cluster-name sample-cluster

# Register task definition
aws ecs register-task-definition --cli-input-json file://task-definition.json

# Create service with load balancer
aws ecs create-service \
  --cluster sample-cluster \
  --service-name sample-web-app \
  --task-definition sample-web-app:1 \
  --desired-count 2 \
  --launch-type FARGATE \
  --network-configuration "awsvpcConfiguration={subnets=[subnet-12345],securityGroups=[sg-12345],assignPublicIp=ENABLED}"

Deploy on EKS

Step 1: Create EKS Cluster

# Create cluster with eksctl
eksctl create cluster \
  --name sample-cluster \
  --region us-west-2 \
  --nodegroup-name standard-workers \
  --node-type m5.large \
  --nodes 2 \
  --nodes-min 1 \
  --nodes-max 4 \
  --ssh-access \
  --ssh-public-key my-key

Step 2: Deploy Application

apiVersion: apps/v1
kind: Deployment
metadata:
  name: sample-web-app
  labels:
    app: sample-web-app
spec:
  replicas: 2
  selector:
    matchLabels:
      app: sample-web-app
  template:
    metadata:
      labels:
        app: sample-web-app
    spec:
      containers:
      - name: web-app
        image: <account-id>.dkr.ecr.us-west-2.amazonaws.com/sample-web-app:latest
        ports:
        - containerPort: 3000
        resources:
          requests:
            memory: "256Mi"
            cpu: "250m"
          limits:
            memory: "512Mi"
            cpu: "500m"
        livenessProbe:
          httpGet:
            path: /health
            port: 3000
          initialDelaySeconds: 30
          periodSeconds: 10
        readinessProbe:
          httpGet:
            path: /health
            port: 3000
          initialDelaySeconds: 5
          periodSeconds: 5
---
apiVersion: v1
kind: Service
metadata:
  name: sample-web-app-service
spec:
  selector:
    app: sample-web-app
  ports:
    - protocol: TCP
      port: 80
      targetPort: 3000
  type: LoadBalancer

Step 3: Deploy and Verify

# Apply configuration
kubectl apply -f deployment.yaml

# Check status
kubectl get deployments
kubectl get pods
kubectl get services

# Get external IP
kubectl get service sample-web-app-service

Performance and Cost Comparison

Performance Characteristics

ECS Fargate

• Cold start: ~10-30 seconds

• Scaling: 1-2 minutes for new tasks

• Resource overhead: Minimal (serverless)

ECS on EC2

• Cold start: ~5-15 seconds

• Scaling: 30 seconds to few minutes

• Resource overhead: Higher (managed instances)

EKS

• Cold start: ~10-60 seconds (depending on configuration)

• Scaling: 30 seconds to few minutes

• Resource overhead: Moderate to high

Cost Optimization Strategies

ECS Cost Optimization

• Use Fargate Spot for fault-tolerant workloads

• Right-size CPU and memory allocations

• Implement auto-scaling policies

• Use reserved capacity for predictable workloads

EKS Cost Optimization

• Mix of On-Demand and Spot instances

• Cluster autoscaler for dynamic scaling

• Vertical Pod Autoscaler for right-sizing

• Use cheaper storage classes where appropriate

Conclusion

Both ECS and EKS are powerful container orchestration platforms with distinct advantages:

Choose ECS if you want simplicity, are primarily AWS-focused, have limited Kubernetes expertise, or need faster time-to-market with lower operational overhead.

Choose EKS if you need Kubernetes ecosystem compatibility, have multi-cloud requirements, require extensive customization, or have complex orchestration needs.

The decision ultimately depends on your team's expertise, application requirements, operational preferences, and long-term strategic goals. Many organizations successfully run both services for different use cases, leveraging each platform's strengths.

Remember that container orchestration is not just about the technology choice—it's about building a sustainable, secure, and scalable application delivery platform that serves your business objectives effectively.

Next week, we'll dive deep into AWS networking fundamentals, exploring VPCs, subnets, and security groups to build secure and scalable network architectures.