Scaling policies (target tracking, step, simple) in AWS - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a simple scaling policy in AWS Auto Scaling?

A simple scaling policy adds or removes a fixed number of instances when a specific CloudWatch alarm triggers. It reacts directly to a threshold being crossed.

intermediate

How does a step scaling policy differ from a simple scaling policy?

Step scaling adjusts the number of instances in steps based on how much a metric deviates from a threshold. Larger deviations cause bigger scaling actions.

beginner

What is target tracking scaling in AWS Auto Scaling?

Target tracking scaling automatically adjusts capacity to keep a metric, like CPU usage, close to a target value you set, similar to a thermostat keeping temperature steady.

intermediate

Why is target tracking scaling often preferred over simple or step scaling?

Because it automatically adjusts capacity to maintain a target metric, it requires less manual tuning and responds smoothly to changes in demand.

beginner

What role do CloudWatch alarms play in simple and step scaling policies?

CloudWatch alarms monitor metrics and trigger scaling actions when thresholds are crossed, acting as the signal for scaling up or down.

Which scaling policy automatically adjusts capacity to keep a metric near a target value?

A Simple scaling

B Step scaling

C Manual scaling

D Target tracking scaling

Target tracking scaling adjusts capacity automatically to maintain a specified metric target.

In step scaling, what determines the size of the scaling adjustment?

A Fixed number of instances every time

B How far the metric is from the threshold

C Time of day

D Random selection

Step scaling changes capacity in steps based on how much the metric deviates from the threshold.

What triggers scaling actions in simple and step scaling policies?

A User manual input

B Fixed schedule

C CloudWatch alarms

D Random events

CloudWatch alarms monitor metrics and trigger scaling when thresholds are crossed.

Which scaling policy is most like a thermostat keeping temperature steady?

A Target tracking scaling

B Step scaling

C Simple scaling

D Manual scaling

Target tracking scaling maintains a metric near a target value, like a thermostat.

Which scaling policy requires the least manual tuning?

A Target tracking scaling

B Step scaling

C Simple scaling

D Manual scaling

Target tracking scaling automatically adjusts capacity to maintain the target metric, reducing manual tuning.

Explain the differences between simple, step, and target tracking scaling policies in AWS Auto Scaling.

Think about how each policy decides when and how much to scale.

Describe how CloudWatch alarms interact with scaling policies in AWS Auto Scaling.

Consider the role of alarms as signals for scaling.

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