What are Scaling Policies?

Scaling Policies in an Auto Scaling Group define the rules for adding or removing EC2 instances based on changing demand. They automate how your application adjusts resources, ensuring you have enough instances during high demand and save costs during low demand.

Types of Scaling Policies

1. Target Tracking Scaling:
   • This policy adjusts the number of instances to maintain a target metric, like keeping CPU usage around 50%.
   • It’s easy to set up and automatically scales up or down to keep the metric close to the target.
2. Step Scaling:
   • Step Scaling adds or removes instances in increments (or "steps") based on specific metric thresholds.
   • For example, if CPU usage goes above 70%, ASG might add 2 instances; if it exceeds 85%, it adds 3 more.
   • Step Scaling is good for gradual scaling based on metric thresholds.
3. Simple Scaling:
   • Simple Scaling increases or decreases instances when a specific threshold is reached.
   • It’s an older method, where you set one rule (like adding an instance when CPU usage is over 70%).
   • It can be slower and less responsive than Target Tracking or Step Scaling but is straightforward to set up.

Important Points

• Cooldown Period:
  • After scaling up or down, ASG has a cooldown period to let instances stabilize before scaling again.
  • This helps prevent rapid, unnecessary scaling.
• Metrics-Based:
  • Policies often rely on Amazon CloudWatch metrics like CPU, memory, or request rate to determine when to scale.
• Custom Metrics:
  • You can also use custom metrics (like queue length in a messaging app) to create more tailored scaling rules.

Best Practices

• Choose the Right Policy for Your App:
  • Use Target Tracking for general, automatic scaling; Step Scaling if you need more control over scaling increments; and Simple Scaling for basic setups.
• Avoid Over-Scaling:
  • Set conservative thresholds and cooldowns to avoid too many scale-ups and scale-downs in a short time.
• Combine with Scheduled Scaling:
  • If your app has predictable traffic patterns, consider Scheduled Scaling to increase capacity before high-demand times.