Predictive scaling overview in AWS - Mini Project: Build & Apply

Predictive Scaling Overview with AWS Auto Scaling

📖 Scenario: You manage a web application hosted on AWS. Traffic to your app changes predictably during the day. You want to prepare your servers to handle these changes automatically without manual intervention.

🎯 Goal: Build a simple AWS Auto Scaling configuration that uses predictive scaling to adjust the number of servers ahead of traffic changes.

📋 What You'll Learn

Create an Auto Scaling group with a launch template

Define a predictive scaling policy for the Auto Scaling group

Set a target tracking scaling policy for comparison

Verify the Auto Scaling group has predictive scaling enabled

💡 Why This Matters

🌍 Real World

Predictive scaling helps cloud applications automatically adjust resources before traffic spikes, improving performance and cost efficiency.

💼 Career

Cloud engineers and DevOps professionals use predictive scaling to optimize infrastructure and maintain application availability.

1

Create an Auto Scaling group with a launch template

Write AWS CLI commands to create a launch template named MyLaunchTemplate with the latest Amazon Linux 2 AMI and instance type t3.micro. Then create an Auto Scaling group named MyAutoScalingGroup using this launch template, with minimum size 1, maximum size 5, and desired capacity 1.

AWS

# Your AWS CLI commands to create launch template and Auto Scaling group here

Hint

Use aws ec2 create-launch-template with --launch-template-data JSON to specify AMI and instance type. Then use aws autoscaling create-auto-scaling-group referencing the launch template.

2

Define a predictive scaling policy for the Auto Scaling group

Write an AWS CLI command to create a predictive scaling policy named MyPredictiveScalingPolicy for the Auto Scaling group MyAutoScalingGroup. Set the policy type to PredictiveScaling and configure it to maintain a target capacity of 70 percent of the forecasted load.

AWS

aws ec2 create-launch-template --launch-template-name MyLaunchTemplate --version-description v1 --launch-template-data '{"ImageId":"ami-0c02fb55956c7d316","InstanceType":"t3.micro"}'
aws autoscaling create-auto-scaling-group --auto-scaling-group-name MyAutoScalingGroup --launch-template LaunchTemplateName=MyLaunchTemplate,Version=1 --min-size 1 --max-size 5 --desired-capacity 1 --vpc-zone-identifier subnet-12345678
# Your AWS CLI command to create predictive scaling policy here

Hint

Use aws autoscaling put-scaling-policy with --policy-type PredictiveScaling and specify --predictive-scaling-configuration JSON with TargetCapacity.

3

Add a target tracking scaling policy for comparison

Write an AWS CLI command to create a target tracking scaling policy named MyTargetTrackingPolicy for the Auto Scaling group MyAutoScalingGroup. Set the target value to 50 percent CPU utilization.

AWS

aws ec2 create-launch-template --launch-template-name MyLaunchTemplate --version-description v1 --launch-template-data '{"ImageId":"ami-0c02fb55956c7d316","InstanceType":"t3.micro"}'
aws autoscaling create-auto-scaling-group --auto-scaling-group-name MyAutoScalingGroup --launch-template LaunchTemplateName=MyLaunchTemplate,Version=1 --min-size 1 --max-size 5 --desired-capacity 1 --vpc-zone-identifier subnet-12345678
aws autoscaling put-scaling-policy --auto-scaling-group-name MyAutoScalingGroup --policy-name MyPredictiveScalingPolicy --policy-type PredictiveScaling --predictive-scaling-configuration '{"TargetCapacity":70}'
# Your AWS CLI command to create target tracking scaling policy here

Hint

Use aws autoscaling put-scaling-policy with --policy-type TargetTrackingScaling and specify --target-tracking-configuration JSON with CPU utilization metric and target value.

4

Verify the Auto Scaling group has predictive scaling enabled

Write an AWS CLI command to describe the scaling policies for MyAutoScalingGroup and confirm that MyPredictiveScalingPolicy exists with type PredictiveScaling.

AWS

aws ec2 create-launch-template --launch-template-name MyLaunchTemplate --version-description v1 --launch-template-data '{"ImageId":"ami-0c02fb55956c7d316","InstanceType":"t3.micro"}'
aws autoscaling create-auto-scaling-group --auto-scaling-group-name MyAutoScalingGroup --launch-template LaunchTemplateName=MyLaunchTemplate,Version=1 --min-size 1 --max-size 5 --desired-capacity 1 --vpc-zone-identifier subnet-12345678
aws autoscaling put-scaling-policy --auto-scaling-group-name MyAutoScalingGroup --policy-name MyPredictiveScalingPolicy --policy-type PredictiveScaling --predictive-scaling-configuration '{"TargetCapacity":70}'
aws autoscaling put-scaling-policy --auto-scaling-group-name MyAutoScalingGroup --policy-name MyTargetTrackingPolicy --policy-type TargetTrackingScaling --target-tracking-configuration '{"PredefinedMetricSpecification":{"PredefinedMetricType":"ASGAverageCPUUtilization"},"TargetValue":50}'
# Your AWS CLI command to describe scaling policies here

Hint

Use aws autoscaling describe-policies with the Auto Scaling group name to list all policies.

Practice

1. What is the main purpose of AWS Predictive Scaling?

easy

A. To prepare servers ahead of traffic changes using machine learning

B. To manually add servers when traffic increases

C. To reduce server count only after traffic drops

D. To monitor server health without changing capacity

Solution

1. Step 1: Understand Predictive Scaling's goal

   Predictive Scaling uses machine learning to forecast traffic and adjust server capacity before changes happen.

2. Step 2: Compare options to this goal

   Only To prepare servers ahead of traffic changes using machine learning describes preparing servers ahead of traffic changes using machine learning.

3. Final Answer:

   To prepare servers ahead of traffic changes using machine learning -> Option A

4. Quick Check:

   Predictive Scaling = Prepare servers ahead [OK]

Hint: Predictive means it acts before traffic changes [OK]

Common Mistakes:

• Thinking it reacts only after traffic changes
• Confusing manual scaling with predictive scaling
• Believing it only monitors without scaling

2. Which AWS service feature enables Predictive Scaling?

easy

A. Server health monitoring

B. Manual server adjustment

C. Static server allocation

D. Machine learning forecasting

Solution

1. Step 1: Identify the technology behind Predictive Scaling

   Predictive Scaling uses machine learning to forecast future traffic and adjust capacity.

2. Step 2: Match options to this technology

   Only Machine learning forecasting mentions machine learning forecasting, which is the key feature.

3. Final Answer:

   Machine learning forecasting -> Option D

4. Quick Check:

   Predictive Scaling uses machine learning [OK]

Hint: Look for machine learning in options [OK]

Common Mistakes:

• Choosing manual or static options
• Confusing monitoring with forecasting
• Ignoring the predictive aspect

3. Given a sudden traffic spike predicted at 3 PM, what will Predictive Scaling do?

medium

A. Add servers after 3 PM when traffic spikes

B. Increase server capacity before 3 PM to handle traffic

C. Remove servers before 3 PM to save costs

D. Keep server count constant regardless of traffic

Solution

1. Step 1: Understand timing of scaling actions

   Predictive Scaling acts before traffic changes to prepare resources.

2. Step 2: Analyze options for timing and action

   Only Increase server capacity before 3 PM to handle traffic describes increasing capacity before the predicted spike.

3. Final Answer:

   Increase server capacity before 3 PM to handle traffic -> Option B

4. Quick Check:

   Predictive Scaling acts before spikes [OK]

Hint: Predictive means before event, not after [OK]

Common Mistakes:

• Choosing to add servers after spike
• Thinking it reduces servers before traffic
• Assuming no change in server count

4. You configured Predictive Scaling but notice servers are not added before traffic spikes. What is a likely cause?

medium

A. Predictive Scaling is disabled or misconfigured

B. Traffic never changes in your app

C. Manual scaling overrides predictive scaling

D. Server health checks are failing

Solution

1. Step 1: Identify why predictive scaling might not act

   If servers are not added before spikes, predictive scaling may be off or set up incorrectly.

2. Step 2: Evaluate other options

   Traffic changes and manual scaling do not prevent predictive scaling from working if enabled; health checks affect server health but not scaling triggers.

3. Final Answer:

   Predictive Scaling is disabled or misconfigured -> Option A

4. Quick Check:

   Misconfiguration stops predictive scaling [OK]

Hint: Check if predictive scaling is enabled first [OK]

Common Mistakes:

• Blaming traffic patterns
• Ignoring configuration settings
• Confusing health checks with scaling triggers

5. How does Predictive Scaling help balance cost and performance in AWS?

hard

A. By adding servers only after traffic increases to avoid waste

B. By permanently running maximum servers to handle any traffic

C. By forecasting traffic and adjusting capacity before changes to keep apps fast and save money

D. By disabling scaling during low traffic periods

Solution

1. Step 1: Understand cost and performance goals

   Good scaling keeps apps fast and avoids paying for unused servers.

2. Step 2: Match Predictive Scaling behavior

   It forecasts traffic and adjusts capacity before changes, balancing speed and cost.

3. Step 3: Evaluate options

   Only By forecasting traffic and adjusting capacity before changes to keep apps fast and save money describes forecasting and proactive adjustment to balance cost and performance.

4. Final Answer:

   By forecasting traffic and adjusting capacity before changes to keep apps fast and save money -> Option C

5. Quick Check:

   Forecasting balances cost and speed [OK]

Hint: Forecast first, then scale to save money and stay fast [OK]

Common Mistakes:

• Thinking scaling happens only after traffic rises
• Assuming always max servers run
• Believing scaling disables during low traffic