Scaling policies (target tracking, step, simple) in AWS - Mini Project: Build & Apply

Scaling policies (target tracking, step, simple)

📋 What You'll Learn

💡 Why This Matters

🌍 Real World

Auto Scaling Groups help keep web applications responsive and cost-efficient by automatically adjusting the number of servers based on demand.

💼 Career

Understanding scaling policies is essential for cloud engineers and DevOps professionals to maintain application performance and optimize resource usage.

1

Create the Auto Scaling Group and Launch Configuration

Create a launch configuration called MyAppLaunchConfig with image ID ami-12345678 and instance type t3.micro. Then create an Auto Scaling Group called MyAppASG using this launch configuration with minimum size 1, maximum size 5, and desired capacity 2.

AWS

# Your code here to create launch configuration and Auto Scaling Group

Hint

Use aws_launch_configuration and aws_autoscaling_group resources. Set the exact names and values as specified.

2

Add Target Tracking Scaling Policy

Add a target tracking scaling policy named CPU50Percent to MyAppASG that keeps the average CPU utilization at 50%. Use the predefined metric ASGAverageCPUUtilization.

AWS

resource "aws_launch_configuration" "MyAppLaunchConfig" {
  name          = "MyAppLaunchConfig"
  image_id      = "ami-12345678"
  instance_type = "t3.micro"
}

resource "aws_autoscaling_group" "MyAppASG" {
  name                      = "MyAppASG"
  launch_configuration      = aws_launch_configuration.MyAppLaunchConfig.name
  min_size                  = 1
  max_size                  = 5
  desired_capacity          = 2
  vpc_zone_identifier       = ["subnet-abc12345"]
}

# Add target tracking scaling policy here

Hint

Use aws_autoscaling_policy with policy_type = "TargetTrackingScaling" and set target_value to 50.

3

Add Step Scaling Policy to Increase Capacity

Add a step scaling policy named ScaleUpHighCPU to MyAppASG that increases capacity by 2 when CPU usage exceeds 70%. Use a CloudWatch alarm named HighCPUAlarm that triggers when average CPU is above 70%.

AWS

resource "aws_launch_configuration" "MyAppLaunchConfig" {
  name          = "MyAppLaunchConfig"
  image_id      = "ami-12345678"
  instance_type = "t3.micro"
}

resource "aws_autoscaling_group" "MyAppASG" {
  name                      = "MyAppASG"
  launch_configuration      = aws_launch_configuration.MyAppLaunchConfig.name
  min_size                  = 1
  max_size                  = 5
  desired_capacity          = 2
  vpc_zone_identifier       = ["subnet-abc12345"]
}

resource "aws_autoscaling_policy" "CPU50Percent" {
  name                   = "CPU50Percent"
  autoscaling_group_name = aws_autoscaling_group.MyAppASG.name
  policy_type            = "TargetTrackingScaling"

  target_tracking_configuration {
    predefined_metric_specification {
      predefined_metric_type = "ASGAverageCPUUtilization"
    }
    target_value = 50.0
  }
}

# Add step scaling policy and CloudWatch alarm here

Hint

Create a CloudWatch alarm for CPU > 70%. Then create a step scaling policy linked to this alarm that increases capacity by 2.

4

Add Simple Scaling Policy to Decrease Capacity

Add a simple scaling policy named ScaleDownLowCPU to MyAppASG that decreases capacity by 1 when CPU usage falls below 30%. Use a CloudWatch alarm named LowCPUAlarm that triggers when average CPU is below 30%.

AWS

resource "aws_launch_configuration" "MyAppLaunchConfig" {
  name          = "MyAppLaunchConfig"
  image_id      = "ami-12345678"
  instance_type = "t3.micro"
}

resource "aws_autoscaling_group" "MyAppASG" {
  name                      = "MyAppASG"
  launch_configuration      = aws_launch_configuration.MyAppLaunchConfig.name
  min_size                  = 1
  max_size                  = 5
  desired_capacity          = 2
  vpc_zone_identifier       = ["subnet-abc12345"]
}

resource "aws_autoscaling_policy" "CPU50Percent" {
  name                   = "CPU50Percent"
  autoscaling_group_name = aws_autoscaling_group.MyAppASG.name
  policy_type            = "TargetTrackingScaling"

  target_tracking_configuration {
    predefined_metric_specification {
      predefined_metric_type = "ASGAverageCPUUtilization"
    }
    target_value = 50.0
  }
}

resource "aws_cloudwatch_metric_alarm" "HighCPUAlarm" {
  alarm_name          = "HighCPUAlarm"
  comparison_operator = "GreaterThanThreshold"
  evaluation_periods  = 1
  metric_name         = "CPUUtilization"
  namespace           = "AWS/EC2"
  period              = 300
  statistic           = "Average"
  threshold           = 70
  alarm_description   = "Alarm when CPU exceeds 70%"
  dimensions = {
    AutoScalingGroupName = aws_autoscaling_group.MyAppASG.name
  }
}

resource "aws_autoscaling_policy" "ScaleUpHighCPU" {
  name                   = "ScaleUpHighCPU"
  autoscaling_group_name = aws_autoscaling_group.MyAppASG.name
  policy_type            = "StepScaling"
  adjustment_type        = "ChangeInCapacity"
  cooldown               = 300
  metric_aggregation_type = "Average"

  step_adjustment {
    metric_interval_lower_bound = 0
    scaling_adjustment          = 2
  }

  alarm_names = [aws_cloudwatch_metric_alarm.HighCPUAlarm.alarm_name]
}

# Add simple scaling policy and CloudWatch alarm here

Hint

Create a CloudWatch alarm for CPU < 30%. Then create a simple scaling policy linked to this alarm that decreases capacity by 1.

Practice

1. What is the main purpose of a target tracking scaling policy in AWS Auto Scaling?

easy

A. To automatically adjust resources to keep a metric close to a target value

B. To manually increase or decrease resources based on user input

C. To schedule scaling actions at fixed times regardless of load

D. To disable scaling during peak hours

Solution

1. Step 1: Understand target tracking policy function

   Target tracking automatically adjusts resources to keep a metric (like CPU usage) near a set target value.

2. Step 2: Compare with other options

   Manual scaling and scheduled scaling do not automatically adjust based on metrics, and disabling scaling is unrelated.

3. Final Answer:

   To automatically adjust resources to keep a metric close to a target value -> Option A

4. Quick Check:

   Target tracking = automatic metric-based scaling [OK]

Hint: Target tracking = auto adjust to keep metric near target [OK]

Common Mistakes:

• Confusing target tracking with manual scaling
• Thinking scheduled scaling is target tracking
• Assuming target tracking disables scaling

2. Which of the following is the correct JSON snippet to define a simple scaling policy in AWS Auto Scaling?

easy

A. {"PolicyName": "ScaleOut", "PolicyType": "TargetTrackingScaling", "TargetValue": 50}

B. {"PolicyName": "ScaleOut", "PolicyType": "StepScaling", "StepAdjustments": [{"MetricIntervalLowerBound": 0, "ScalingAdjustment": 1}]}

C. {"PolicyName": "ScaleOut", "PolicyType": "SimpleScaling", "AdjustmentType": "ChangeInCapacity", "ScalingAdjustment": 2}

D. {"PolicyName": "ScaleOut", "PolicyType": "ScheduledScaling", "Schedule": "cron(0 12 * * ? *)"}

Solution

1. Step 1: Identify simple scaling policy syntax

   Simple scaling uses PolicyType "SimpleScaling" with AdjustmentType and ScalingAdjustment fields.

2. Step 2: Verify JSON options

   {"PolicyName": "ScaleOut", "PolicyType": "SimpleScaling", "AdjustmentType": "ChangeInCapacity", "ScalingAdjustment": 2} matches simple scaling syntax; others are for target tracking, step scaling, or scheduled scaling.

3. Final Answer:

   {"PolicyName": "ScaleOut", "PolicyType": "SimpleScaling", "AdjustmentType": "ChangeInCapacity", "ScalingAdjustment": 2} -> Option C

4. Quick Check:

   Simple scaling = PolicyType "SimpleScaling" [OK]

Hint: Simple scaling uses PolicyType "SimpleScaling" with adjustment fields [OK]

Common Mistakes:

• Using TargetTrackingScaling type for simple scaling
• Confusing step scaling syntax with simple scaling
• Including schedule fields in simple scaling

3. Given this step scaling policy snippet:

{
  "PolicyType": "StepScaling",
  "StepAdjustments": [
    {"MetricIntervalLowerBound": 0, "ScalingAdjustment": 1},
    {"MetricIntervalLowerBound": 10, "ScalingAdjustment": 2}
  ]
}

If the metric increases by 12 units, how many instances will be added?

medium

A. 0

B. 1

C. 3

D. 2

Solution

1. Step 1: Understand step adjustments

   Each step defines scaling for metric intervals. For metric increase of 12, check which intervals apply.

2. Step 2: Find matching step

   12 is >=10, so the second step applies with ScalingAdjustment 2.

3. Final Answer:

   2 -> Option D

4. Quick Check:

   Metric 12 >= 10 -> add 2 instances [OK]

Hint: Metric value matches highest lower bound step [OK]

Common Mistakes:

• Adding adjustments from both steps
• Choosing the first step only
• Ignoring metric intervals

4. You configured a step scaling policy but it never triggers scaling actions even when the metric crosses thresholds. What is the most likely cause?

medium

A. The scaling adjustment values are negative

B. The metric alarms are not properly linked to the scaling policy

C. The target tracking policy is overriding the step scaling policy

D. The Auto Scaling group has no instances

Solution

1. Step 1: Check alarm linkage

   Step scaling policies rely on CloudWatch alarms to trigger. If alarms are not linked, no scaling occurs.

2. Step 2: Evaluate other options

   Target tracking does not override step scaling; negative adjustments cause scale-in, not no action; no instances means no scaling needed.

3. Final Answer:

   The metric alarms are not properly linked to the scaling policy -> Option B

4. Quick Check:

   Missing alarm linkage = no scaling trigger [OK]

Hint: Step scaling needs alarms linked to trigger [OK]

Common Mistakes:

• Assuming target tracking disables step scaling
• Ignoring alarm configuration
• Confusing negative adjustment with no scaling

5. You want to keep your application's CPU usage at 50% using AWS Auto Scaling. You also want to add 2 instances if CPU usage goes above 70%, and remove 1 instance if it drops below 30%. Which combination of scaling policies should you use?

hard

A. Use a target tracking policy for 50% CPU and step scaling for adding/removing instances at 70% and 30%

B. Use only a simple scaling policy with fixed adjustments

C. Use only a target tracking policy with target 50%

D. Use scheduled scaling to add and remove instances at fixed times

Solution

1. Step 1: Understand target tracking role

   Target tracking keeps CPU near 50% automatically adjusting capacity.

2. Step 2: Understand step scaling role

   Step scaling defines specific actions when CPU crosses 70% (add 2) or 30% (remove 1).

3. Step 3: Combine policies

   Use target tracking for steady state and step scaling for aggressive changes beyond thresholds.

4. Final Answer:

   Use a target tracking policy for 50% CPU and step scaling for adding/removing instances at 70% and 30% -> Option A

5. Quick Check:

   Target tracking + step scaling = precise control [OK]

Hint: Combine target tracking for steady and step scaling for thresholds [OK]

Common Mistakes:

• Using only one policy type for all needs
• Confusing scheduled scaling with metric-based scaling
• Ignoring removal scaling actions