ArchView: AWS Auto Scaling Pattern | Aws Cloud Native Architecture Diagram

Introduction to AWS Auto Scaling Pattern

The AWS Auto Scaling Pattern enables dynamic scaling of compute resources like EC2, ECS, EKS, and Lambda based on CloudWatch metrics. It uses Auto Scaling Groups for EC2 and Application Auto Scaling for other services, adjusting capacity in response to demand. This ensures high availability, performance, and cost optimization by scaling out during peak loads and scaling in during low demand, with seamless integration into the AWS ecosystem.

Auto Scaling leverages CloudWatch metrics to maintain optimal resource utilization with minimal manual intervention.

Auto Scaling Architecture Diagram

The diagram illustrates the auto scaling pattern: CloudWatch monitors metrics (e.g., CPU, request count) from EC2, ECS, EKS, and Lambda. Based on alarms, Auto Scaling Groups or Application Auto Scaling adjust resource capacity. Arrows are color-coded: blue for metric collection, orange for scaling actions, and green for resource interactions.

graph TD %% Styling for nodes classDef monitor fill:#42a5f5,stroke:#1e88e5,stroke-width:2px,rx:5,ry:5; classDef resource fill:#ff6f61,stroke:#c62828,stroke-width:2px,color:#ffffff,rx:5,ry:5; classDef scaler fill:#2ecc71,stroke:#1b5e20,stroke-width:2px,color:#ffffff,rx:5,ry:5; %% Flow A[(CloudWatch)] -->|Metrics| B[EC2 Instances] A -->|Metrics| C[ECS Tasks] A -->|Metrics| D[EKS Pods] A -->|Metrics| E[Lambda Functions] A -->|Alarms| F[Auto Scaling Group] A -->|Alarms| G[Application Auto Scaling] F -->|Scale| B G -->|Scale| C G -->|Scale| D G -->|Scale| E %% Subgraphs for grouping subgraph Monitoring A end subgraph Compute Resources B C D E end subgraph Scaling Services F G end %% Apply styles class A monitor; class B,C,D,E resource; class F,G scaler; %% Annotations linkStyle 0,1,2,3 stroke:#405de6,stroke-width:2.5px; linkStyle 4,5 stroke:#ff6f61,stroke-width:2.5px; linkStyle 6,7,8,9 stroke:#2ecc71,stroke-width:2.5px;

CloudWatch drives scaling decisions for diverse AWS compute services through Auto Scaling mechanisms.

Key Components

The auto scaling pattern relies on the following AWS components:

CloudWatch: Collects metrics (e.g., CPU, memory, request count) and triggers alarms for scaling actions.
Auto Scaling Groups: Manages EC2 instance scaling based on policies and CloudWatch alarms.
Application Auto Scaling: Scales ECS tasks, EKS pods, and Lambda concurrency based on metrics.
EC2 Instances: Virtual servers scaled via Auto Scaling Groups for web or application hosting.
ECS Tasks: Containerized workloads scaled using Application Auto Scaling for microservices.
EKS Pods: Kubernetes workloads scaled via Application Auto Scaling or Cluster Autoscaler.
Lambda Functions: Serverless compute scaled automatically or via Application Auto Scaling for provisioned concurrency.
IAM: Secures scaling operations with fine-grained permissions for resource interactions.
Elastic Load Balancer (ELB): Distributes traffic across scaled EC2 or ECS resources for high availability.
CloudFormation: Automates infrastructure provisioning for scalable architectures.

Benefits of AWS Auto Scaling Pattern

The auto scaling pattern offers significant advantages:

High Availability: Automatically adjusts capacity to maintain performance during traffic spikes.
Cost Optimization: Scales in during low demand to reduce resource costs.
Dynamic Scaling: Responds to real-time metrics for seamless performance tuning.
Operational Simplicity: Reduces manual intervention with automated scaling policies.
Flexibility: Supports diverse workloads (EC2, ECS, EKS, Lambda) with unified scaling mechanisms.
Reliability: Integrates with ELB and health checks to ensure healthy instances.
Observability: CloudWatch provides detailed metrics and logs for scaling decisions.
Extensibility: Combines with AWS services like RDS or DynamoDB for end-to-end scalability.

Implementation Considerations

Implementing auto scaling requires addressing key considerations:

Scaling Policies: Define target tracking, step scaling, or scheduled scaling based on workload needs.
Metric Selection: Choose relevant CloudWatch metrics (e.g., CPU, latency) for accurate scaling triggers.
Cooldown Periods: Configure cooldowns to prevent rapid scaling oscillations.
Health Checks: Use ELB or custom health checks to terminate unhealthy instances.
Security Practices: Apply least-privilege IAM roles and encrypt data in transit and at rest.
Cost Monitoring: Use Cost Explorer to track scaling-related expenses and optimize configurations.
Capacity Planning: Set minimum and maximum capacity to balance cost and availability.
Testing Approach: Simulate load with tools like AWS Load Testing to validate scaling behavior.
Cluster Autoscaler for EKS: Combine with Application Auto Scaling for pod and node scaling.
Compliance Requirements: Enable CloudTrail for auditability and adhere to standards like PCI DSS.

Proper metric selection and scaling policies ensure efficient and reliable auto scaling.

Example Configuration: EC2 Auto Scaling Group

Below is a Terraform configuration to provision an EC2 Auto Scaling Group with CloudWatch alarms.

provider "aws" {
  region = "us-west-2"
}

resource "aws_launch_template" "web_server" {
  name_prefix   = "web-server-"
  image_id      = "ami-0c55b159cbfafe1f0" # Amazon Linux 2 AMI
  instance_type = "t3.micro"
  user_data     = base64encode("#!/bin/bash\necho 'Hello World' > /var/www/html/index.html")
}

resource "aws_autoscaling_group" "web_asg" {
  desired_capacity     = 2
  min_size             = 1
  max_size             = 4
  vpc_zone_identifier  = ["subnet-12345678", "subnet-87654321"]
  launch_template {
    id      = aws_launch_template.web_server.id
    version = "$Latest"
  }
  health_check_type = "ELB"
  tag {
    key                 = "Environment"
    value               = "production"
    propagate_at_launch = true
  }
}

resource "aws_cloudwatch_metric_alarm" "high_cpu" {
  alarm_name          = "high-cpu-usage"
  comparison_operator = "GreaterThanThreshold"
  evaluation_periods  = 2
  metric_name         = "CPUUtilization"
  namespace           = "AWS/EC2"
  period              = 300
  statistic           = "Average"
  threshold           = 70
  alarm_actions       = [aws_autoscaling_policy.scale_out.arn]
  dimensions = {
    AutoScalingGroupName = aws_autoscaling_group.web_asg.name
  }
}

resource "aws_autoscaling_policy" "scale_out" {
  name                   = "scale-out"
  scaling_adjustment     = 1
  adjustment_type        = "ChangeInCapacity"
  cooldown               = 300
  autoscaling_group_name = aws_autoscaling_group.web_asg.name
}

resource "aws_cloudwatch_metric_alarm" "low_cpu" {
  alarm_name          = "low-cpu-usage"
  comparison_operator = "LessThanThreshold"
  evaluation_periods  = 2
  metric_name         = "CPUUtilization"
  namespace           = "AWS/EC2"
  period              = 300
  statistic           = "Average"
  threshold           = 30
  alarm_actions       = [aws_autoscaling_policy.scale_in.arn]
  dimensions = {
    AutoScalingGroupName = aws_autoscaling_group.web_asg.name
  }
}

resource "aws_autoscaling_policy" "scale_in" {
  name                   = "scale-in"
  scaling_adjustment     = -1
  adjustment_type        = "ChangeInCapacity"
  cooldown               = 300
  autoscaling_group_name = aws_autoscaling_group.web_asg.name
}

resource "aws_iam_role" "asg_role" {
  name = "asg-role"
  assume_role_policy = jsonencode({
    Version = "2012-10-17"
    Statement = [
      {
        Action = "sts:AssumeRole"
        Effect = "Allow"
        Principal = {
          Service = "autoscaling.amazonaws.com"
        }
      }
    ]
  })
}

resource "aws_iam_role_policy" "asg_policy" {
  name = "asg-policy"
  role = aws_iam_role.asg_role.id
  policy = jsonencode({
    Version = "2012-10-17"
    Statement = [
      {
        Effect = "Allow"
        Action = [
          "cloudwatch:PutMetricData",
          "ec2:DescribeInstances",
          "ec2:TerminateInstances"
        ]
        Resource = "*"
      }
    ]
  })
}

This Terraform configuration sets up an EC2 Auto Scaling Group with CloudWatch-driven scaling policies.

Example Configuration: ECS Application Auto Scaling

Below is a Terraform configuration for scaling ECS tasks based on CPU utilization.

provider "aws" {
  region = "us-west-2"
}

resource "aws_ecs_cluster" "my_cluster" {
  name = "my-cluster"
}

resource "aws_ecs_task_definition" "my_task" {
  family                   = "my-task"
  network_mode             = "awsvpc"
  requires_compatibilities = ["FARGATE"]
  cpu                      = "256"
  memory                   = "512"
  container_definitions    = jsonencode([
    {
      name  = "my-container"
      image = "amazon/amazon-ecs-sample"
      essential = true
      portMappings = [
        {
          containerPort = 80
          hostPort      = 80
        }
      ]
    }
  ])
}

resource "aws_ecs_service" "my_service" {
  name            = "my-service"
  cluster         = aws_ecs_cluster.my_cluster.id
  task_definition = aws_ecs_task_definition.my_task.arn
  desired_count   = 2
  launch_type     = "FARGATE"
  network_configuration {
    subnets         = ["subnet-12345678", "subnet-87654321"]
    security_groups = ["sg-12345678"]
  }
}

resource "aws_appautoscaling_target" "ecs_target" {
  max_capacity       = 4
  min_capacity       = 1
  resource_id        = "service/${aws_ecs_cluster.my_cluster.name}/${aws_ecs_service.my_service.name}"
  scalable_dimension = "ecs:service:DesiredCount"
  service_namespace  = "ecs"
}

resource "aws_appautoscaling_policy" "ecs_policy" {
  name               = "scale-ecs-cpu"
  policy_type        = "TargetTrackingScaling"
  resource_id        = aws_appautoscaling_target.ecs_target.resource_id
  scalable_dimension = aws_appautoscaling_target.ecs_target.scalable_dimension
  service_namespace  = aws_appautoscaling_target.ecs_target.service_namespace
  target_tracking_scaling_policy_configuration {
    predefined_metric_specification {
      predefined_metric_type = "ECSServiceAverageCPUUtilization"
    }
    target_value = 70
  }
}

resource "aws_iam_role" "ecs_role" {
  name = "ecs-role"
  assume_role_policy = jsonencode({
    Version = "2012-10-17"
    Statement = [
      {
        Action = "sts:AssumeRole"
        Effect = "Allow"
        Principal = {
          Service = "application-autoscaling.amazonaws.com"
        }
      }
    ]
  })
}

resource "aws_iam_role_policy" "ecs_policy" {
  name = "ecs-scaling-policy"
  role = aws_iam_role.ecs_role.id
  policy = jsonencode({
    Version = "2012-10-17"
    Statement = [
      {
        Effect = "Allow"
        Action = [
          "ecs:DescribeServices",
          "ecs:UpdateService",
          "cloudwatch:DescribeAlarms"
        ]
        Resource = "*"
      }
    ]
  })
}

This Terraform configuration enables CPU-based auto scaling for ECS tasks on Fargate.