ECS service auto scaling in AWS - Mini Project: Build & Apply

ECS Service Auto Scaling Setup

📖 Scenario: You manage a web application running on AWS ECS (Elastic Container Service). To handle varying traffic, you want the ECS service to automatically adjust the number of running tasks.This project guides you to create an ECS service with auto scaling configured to increase or decrease the number of tasks based on CPU usage.

🎯 Goal: Build an ECS service with auto scaling enabled that adjusts the number of tasks between 1 and 4 based on CPU utilization thresholds.

📋 What You'll Learn

Create an ECS cluster named my-ecs-cluster

Create an ECS service named my-ecs-service running a sample task

Configure auto scaling for the ECS service with minimum 1 and maximum 4 tasks

Set a target tracking scaling policy to maintain CPU utilization at 50%

💡 Why This Matters

🌍 Real World

Auto scaling ECS services is common in real applications to save costs and maintain performance during traffic changes.

💼 Career

Cloud engineers and DevOps professionals often configure ECS auto scaling to ensure applications run smoothly and efficiently.

1

Create ECS Cluster

Create an ECS cluster named my-ecs-cluster using AWS CLI syntax.

AWS

# Create ECS cluster named my-ecs-cluster
# Your code here

Hint

Use the AWS CLI command aws ecs create-cluster with the --cluster-name option.

2

Create ECS Service

Create an ECS service named my-ecs-service in cluster my-ecs-cluster with desired count 1 and a sample task definition sample-task.

AWS

aws ecs create-cluster --cluster-name my-ecs-cluster
# Create ECS service named my-ecs-service with desired count 1 and task definition sample-task
# Your code here

Hint

Use aws ecs create-service with --cluster, --service-name, --task-definition, and --desired-count options.

3

Configure Auto Scaling Target

Create an Application Auto Scaling target for the ECS service my-ecs-service in cluster my-ecs-cluster with minimum capacity 1 and maximum capacity 4.

AWS

aws ecs create-cluster --cluster-name my-ecs-cluster
aws ecs create-service --cluster my-ecs-cluster --service-name my-ecs-service --task-definition sample-task --desired-count 1
# Register scalable target with min capacity 1 and max capacity 4
# Your code here

Hint

Use aws application-autoscaling register-scalable-target with the correct --resource-id and capacity limits.

4

Create Target Tracking Scaling Policy

Create a target tracking scaling policy named cpu-utilization-policy for the ECS service my-ecs-service in cluster my-ecs-cluster to maintain CPU utilization at 50%.

AWS

aws ecs create-cluster --cluster-name my-ecs-cluster
aws ecs create-service --cluster my-ecs-cluster --service-name my-ecs-service --task-definition sample-task --desired-count 1
aws application-autoscaling register-scalable-target --service-namespace ecs --resource-id service/my-ecs-cluster/my-ecs-service --scalable-dimension ecs:service:DesiredCount --min-capacity 1 --max-capacity 4
# Create target tracking scaling policy to maintain CPU utilization at 50%
# Your code here

Hint

Create a JSON file named policy.json with the target tracking configuration and use it with put-scaling-policy.

Practice

1. What is the main purpose of ECS service auto scaling?

easy

A. Store container logs for analysis

B. Manually start and stop containers

C. Automatically adjust the number of running containers based on demand

D. Create new ECS clusters automatically

Solution

1. Step 1: Understand ECS service auto scaling function

   ECS service auto scaling changes the number of containers automatically to match workload.

2. Step 2: Compare options to this function

   Only Automatically adjust the number of running containers based on demand describes automatic adjustment of container count based on demand.

3. Final Answer:

   Automatically adjust the number of running containers based on demand -> Option C

4. Quick Check:

   Auto scaling adjusts container count = Automatically adjust containers [OK]

Hint: Auto scaling means automatic container count changes [OK]

Common Mistakes:

• Confusing auto scaling with manual container management
• Thinking auto scaling creates clusters
• Believing auto scaling stores logs

2. Which of the following is the correct way to specify a target tracking scaling policy for CPU utilization in ECS service auto scaling?

easy

A. "TargetValue": 50, "PredefinedMetricSpecification": {"PredefinedMetricType": "ECSServiceAverageCPUUtilization"}

B. "TargetValue": "high", "PredefinedMetricSpecification": {"PredefinedMetricType": "CPUUsage"}

C. "TargetValue": 100, "MetricName": "MemoryUtilization"

D. "TargetValue": 30, "PredefinedMetricSpecification": {"PredefinedMetricType": "NetworkIn"}

Solution

1. Step 1: Identify correct metric type for CPU utilization

   The predefined metric for ECS CPU is "ECSServiceAverageCPUUtilization" with a numeric target value.

2. Step 2: Check option syntax and metric correctness

   "TargetValue": 50, "PredefinedMetricSpecification": {"PredefinedMetricType": "ECSServiceAverageCPUUtilization"} uses correct metric type and numeric target value 50, which is valid.

3. Final Answer:

   "TargetValue": 50, "PredefinedMetricSpecification": {"PredefinedMetricType": "ECSServiceAverageCPUUtilization"} -> Option A

4. Quick Check:

   Correct metric and numeric target = "TargetValue": 50, "PredefinedMetricSpecification": {"PredefinedMetricType": "ECSServiceAverageCPUUtilization"} [OK]

Hint: Use numeric target and correct predefined metric name [OK]

Common Mistakes:

• Using string instead of number for TargetValue
• Using wrong metric names like CPUUsage
• Mixing memory or network metrics for CPU scaling

3. Given this ECS service auto scaling configuration snippet:

{"MinCapacity": 2, "MaxCapacity": 5, "TargetValue": 60}

What happens when CPU utilization stays at 70% for a long time?

medium

A. The service scales up to 5 containers to reduce CPU load

B. The service scales down to 2 containers to save cost

C. The service keeps running 2 containers without change

D. The service crashes due to high CPU

Solution

1. Step 1: Understand scaling triggers from CPU target

   TargetValue 60 means ECS tries to keep CPU near 60%. If CPU is 70%, it needs more containers.

2. Step 2: Apply min and max capacity limits

   Min is 2, max is 5. Scaling up is allowed up to 5 containers to reduce CPU load.

3. Final Answer:

   The service scales up to 5 containers to reduce CPU load -> Option A

4. Quick Check:

   CPU above target scales up containers = The service scales up to 5 containers to reduce CPU load [OK]

Hint: CPU above target scales up, below scales down [OK]

Common Mistakes:

• Thinking service scales down when CPU is high
• Ignoring max capacity limit
• Assuming service crashes on high CPU

4. You configured ECS service auto scaling but the service never scales out even when CPU is high. What is a likely cause?

medium

A. The service has no load balancer attached

B. The target CPU utilization is set to 100%

C. The ECS cluster has no running tasks

D. The maximum capacity is set equal to the minimum capacity

Solution

1. Step 1: Analyze scaling limits

   If max capacity equals min capacity, no room to scale out exists.

2. Step 2: Check other options

   Target 100% means scaling triggers only at max CPU, but scaling can still happen. No running tasks or load balancer affect service but not scaling limits directly.

3. Final Answer:

   The maximum capacity is set equal to the minimum capacity -> Option D

4. Quick Check:

   Max equals min blocks scaling out = The maximum capacity is set equal to the minimum capacity [OK]

Hint: Max capacity must be greater than min capacity to scale out [OK]

Common Mistakes:

• Ignoring max and min capacity settings
• Assuming load balancer affects scaling triggers
• Confusing target CPU with scaling limits

5. You want to configure ECS service auto scaling to keep container count between 3 and 10, scaling based on memory usage targeting 70%. Which configuration is correct?

hard

A. MinCapacity: 10, MaxCapacity: 3, TargetValue: 70, PredefinedMetricType: ECSServiceAverageMemoryUtilization

B. MinCapacity: 3, MaxCapacity: 10, TargetValue: 70, PredefinedMetricType: ECSServiceAverageMemoryUtilization

C. MinCapacity: 3, MaxCapacity: 10, TargetValue: 70, PredefinedMetricType: ECSServiceAverageCPUUtilization

D. MinCapacity: 3, MaxCapacity: 10, TargetValue: 30, PredefinedMetricType: ECSServiceAverageMemoryUtilization

Solution

1. Step 1: Set correct min and max container counts

   MinCapacity must be less than MaxCapacity, so 3 and 10 is valid, 10 and 3 is invalid.

2. Step 2: Use correct metric and target value for memory

   Memory scaling uses ECSServiceAverageMemoryUtilization with target 70% to trigger scaling.

3. Step 3: Verify options

   MinCapacity: 3, MaxCapacity: 10, TargetValue: 70, PredefinedMetricType: ECSServiceAverageMemoryUtilization matches all requirements: correct min/max, target 70, memory metric.

4. Final Answer:

   MinCapacity: 3, MaxCapacity: 10, TargetValue: 70, PredefinedMetricType: ECSServiceAverageMemoryUtilization -> Option B

5. Quick Check:

   Correct min/max and memory target = MinCapacity: 3, MaxCapacity: 10, TargetValue: 70, PredefinedMetricType: ECSServiceAverageMemoryUtilization [OK]

Hint: Min < Max and match metric to resource (memory or CPU) [OK]

Common Mistakes:

• Setting max less than min capacity
• Using CPU metric for memory scaling
• Setting wrong target value for scaling