Scaling policies (target tracking, step, simple) in AWS - Step-by-Step Execution

Process Flow - Scaling policies (target tracking, step, simple)

Start

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Monitor Metric

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Compare Metric to Target

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Target Tracking Policy

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Adjust Capacity to Match Target

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Update Resources

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Step Scaling Policy

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Check Metric Thresholds

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Scale Up/Down by Steps

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Update Resources

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Simple Scaling Policy

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Metric Breaches Threshold?

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Add or Remove Fixed Capacity

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Update Resources

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End

The system monitors a metric and applies one of three scaling policies: target tracking adjusts capacity to keep the metric near a target; step scaling changes capacity in steps based on thresholds; simple scaling adds or removes fixed capacity when thresholds are crossed.

Execution Sample

AWS

Metric = CPUUtilization
Target = 50%
If Metric > Target + 5%: Scale Out
If Metric < Target - 5%: Scale In

This example shows a target tracking policy that scales out if CPU usage is above 55% and scales in if below 45%.

Process Table

Step	Metric Value	Policy Type	Condition Checked	Action Taken	New Capacity
1	40%	Target Tracking	40% < 50% - 5%	Scale In	Capacity - 1
2	48%	Target Tracking	48% within target range	No Change	No Change
3	56%	Target Tracking	56% > 50% + 5%	Scale Out	Capacity + 1
4	70%	Step Scaling	70% > High Threshold (60%)	Scale Out by 2	Capacity + 2
5	30%	Step Scaling	30% < Low Threshold (40%)	Scale In by 1	Capacity - 1
6	55%	Simple Scaling	55% > Threshold (50%)	Scale Out by 1	Capacity + 1
7	45%	Simple Scaling	45% < Threshold (50%)	Scale In by 1	Capacity - 1
8	50%	All Policies	Metric at Target	No Change	No Change

💡 Metric stabilizes near target; no further scaling needed.

Status Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	After Step 4	After Step 5	After Step 6	After Step 7	After Step 8
Metric Value	N/A	40%	48%	56%	70%	30%	55%	45%	50%
Capacity	10	9	9	10	12	11	12	11	11

Key Moments - 3 Insights

Why does the capacity not change at step 2 even though the metric is close to the target?

At step 2, the metric is within the target range (48% is between 45% and 55%), so the target tracking policy decides no scaling action is needed, as shown in execution_table row 2.

How does step scaling decide how much to scale?

Step scaling uses defined thresholds to decide scaling amount. For example, at step 4, metric 70% exceeds the high threshold 60%, so it scales out by 2 units, as seen in execution_table row 4.

What is the difference between simple scaling and target tracking in scaling decisions?

Simple scaling adds or removes a fixed amount when a threshold is crossed, without aiming to maintain a target metric. Target tracking adjusts capacity continuously to keep the metric near a target, as shown by comparing steps 1-3 (target tracking) and steps 6-7 (simple scaling).

Visual Quiz - 3 Questions

Look at the execution table, what is the capacity after step 4?

A 10

B 12

C 11

D 9

💡 Hint

Check the 'New Capacity' column at step 4 in the execution_table.

At which step does the metric first cause a scale in action?

A Step 1

B Step 3

C Step 5

D Step 7

💡 Hint

Look for 'Scale In' in the 'Action Taken' column in the execution_table.

If the metric at step 6 was 45% instead of 55%, what would the simple scaling action be?

A Scale Out by 1

B No Change

C Scale In by 1

D Scale Out by 2

💡 Hint

Refer to the simple scaling logic in execution_table rows 6 and 7.

Concept Snapshot

Scaling policies adjust resource capacity based on metrics.
Target tracking keeps metrics near a target by adjusting capacity dynamically.
Step scaling changes capacity in steps when thresholds are crossed.
Simple scaling adds or removes fixed capacity on threshold breaches.
Each policy monitors metrics and updates capacity accordingly.

Full Transcript

Scaling policies help cloud resources adjust automatically to demand. Target tracking policies watch a metric like CPU usage and add or remove resources to keep it near a set target. Step scaling policies check if the metric crosses certain thresholds and then scale up or down by defined steps. Simple scaling policies add or remove a fixed number of resources when a threshold is crossed. This visual trace shows metric values, policy decisions, and how capacity changes step by step, helping beginners understand how each policy works in practice.

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