Target tracking scaling is an Auto Scaling policy type in AWS that automatically adjusts the number of EC2 instances in your Auto Scaling group to maintain a specified target metric value. This approach simplifies capacity management by allowing you to define a target, and AWS handles the scaling l…Target tracking scaling is an Auto Scaling policy type in AWS that automatically adjusts the number of EC2 instances in your Auto Scaling group to maintain a specified target metric value. This approach simplifies capacity management by allowing you to define a target, and AWS handles the scaling logic to keep your application performing optimally.
With target tracking scaling, you select a predefined or custom CloudWatch metric and set a target value. Common predefined metrics include Average CPU Utilization, Average Network In/Out, and Application Load Balancer Request Count Per Target. For example, you might configure your Auto Scaling group to maintain average CPU utilization at 50%.
When the metric rises above the target, Auto Scaling adds instances to handle increased load. When the metric falls below the target, Auto Scaling removes instances to reduce costs. The service continuously monitors and adjusts capacity, making it ideal for applications with variable workloads.
Key benefits for reliability and business continuity include:
1. **Automatic Response**: The system reacts to changing demand patterns, ensuring your application remains responsive during traffic spikes.
2. **Cost Optimization**: Resources scale down during low-demand periods, preventing over-provisioning.
3. **Simplified Configuration**: Unlike step scaling or simple scaling policies, you only need to specify the target value rather than defining multiple scaling thresholds.
4. **Cooldown Periods**: Target tracking includes scale-in cooldown periods to prevent rapid fluctuations in instance count.
Best practices include setting appropriate cooldown periods, using multiple metrics for comprehensive scaling decisions, and combining target tracking with scheduled scaling for predictable traffic patterns. You can also create multiple target tracking policies for the same Auto Scaling group using different metrics.
For the SysOps exam, understand that target tracking provides a proportional scaling approach, adjusting capacity proportionally to metric deviations from the target, ensuring consistent application performance and high availability.
Target Tracking Scaling
What is Target Tracking Scaling?
Target tracking scaling is an Auto Scaling policy type in AWS that automatically adjusts the number of EC2 instances in an Auto Scaling group to maintain a specified target value for a specific CloudWatch metric. Think of it like a thermostat for your infrastructure - you set a desired temperature (target value), and the system automatically adjusts to maintain it.
Why is Target Tracking Scaling Important?
Target tracking scaling is crucial for several reasons:
• Simplified Scaling Management: You only need to specify the target metric value, and AWS handles the complex scaling logic • Cost Optimization: Automatically scales in when demand decreases, reducing unnecessary costs • Improved Application Performance: Maintains consistent performance by scaling out when demand increases • Reduced Operational Overhead: Eliminates the need to manually define scaling thresholds and adjustment values • Dynamic Response: Continuously adjusts capacity based on actual demand patterns
How Target Tracking Scaling Works
1. Define a Target Metric: Choose a predefined metric (CPU utilization, network in/out, ALB request count per target) or create a custom metric
2. Set Target Value: Specify the value you want to maintain (e.g., 50% CPU utilization)
3. AWS Creates Alarms: Auto Scaling automatically creates and manages two CloudWatch alarms - one for scaling out and one for scaling in
4. Continuous Adjustment: The scaling policy continuously calculates the required capacity and adjusts instances to keep the metric at or near the target value
Key Configuration Options: • Target Value: The metric value to maintain • Warmup Period: Time for new instances to warm up before contributing to metrics • Disable Scale-In: Option to prevent the policy from terminating instances • Cooldown Period: Managed automatically but can be influenced by instance warmup settings
Best Practices
• Choose metrics that change proportionally with load • Set realistic target values (avoid extremes like 100% CPU) • Use appropriate warmup periods to prevent premature scaling decisions • Consider combining with scheduled scaling for predictable demand patterns • Monitor scaling activities to fine-tune target values
Exam Tips: Answering Questions on Target Tracking Scaling
Key Points to Remember:
• Target tracking is the simplest and most recommended scaling policy type for most use cases • AWS automatically creates and manages the CloudWatch alarms - you do not need to create them manually • Multiple target tracking policies can be attached to an Auto Scaling group, and Auto Scaling will use the policy that provides the largest capacity • You cannot modify the CloudWatch alarms created by target tracking policies • Scale-in can be disabled while keeping scale-out enabled - useful when using multiple policies • The ALBRequestCountPerTarget metric requires an Application Load Balancer to be associated with the Auto Scaling group
Common Exam Scenarios:
• When asked about maintaining a specific metric value (like 70% CPU), target tracking is typically the answer • Questions mentioning simplest approach or least operational overhead for scaling often point to target tracking • If a scenario requires scaling based on custom application metrics, remember that target tracking supports custom CloudWatch metrics • For questions about combining scaling policies, remember that Auto Scaling uses the policy providing maximum capacity during scale-out events
Watch Out For:
• Do not confuse target tracking with step scaling or simple scaling policies • Questions may try to trick you into thinking you need to manually create alarms - you do not for target tracking • Remember that target tracking is not suitable for metrics that do not change proportionally with capacity