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Auto Scaling Patterns

Automatically scale compute resources based on demand to optimize cost and performance.

Auto Scaling Patterns diagram

Auto scaling architecture with Application Load Balancer and target groups

Auto scaling ensures your application can handle varying loads while minimizing costs during low-demand periods. We implement scaling strategies that balance responsiveness with cost optimization.

Key scaling approaches:

  • Target Tracking - Maintain specific metrics (CPU, request count) at target values
  • Step Scaling - Granular scaling actions based on metric thresholds
  • Scheduled Scaling - Predictive scaling for known traffic patterns
  • ECS Service Auto Scaling - Container-aware scaling for microservices

Key Concepts

  • Scale out quickly, scale in gradually
  • Health checks ensure only healthy instances receive traffic
  • Warm pools reduce scale-out latency
  • Mixed instance policies for cost optimization
  • Integration with CloudWatch alarms and metrics

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