AWS Case Study

Amazon EC2 Auto Scaling

Use this page as a service-specific case-study starter for architecture, simulation, and optimization scenarios in pinpole.cloud.

Why this service matters

EC2 Auto Scaling automatically adjusts capacity.

Power:
- Target tracking, step, and scheduled scaling
- Predictive scaling with ML
- Instance refresh for rolling updates
- Mixed instances (Spot + On-Demand)
- Warm pools for faster scaling

Important workflows

  • Design - Configure service behavior for your workload.

Configuration sections

  • Why Auto Scaling (Power + Limits)
  • Auto Scaling Settings
  • Service Quotas

Key configuration points

PointDefault / ValueCategory
EnabledtrueAuto Scaling Settings
Min Size1Auto Scaling Settings
Max Size10Auto Scaling Settings
Desired Capacity2Auto Scaling Settings
Scaling Policytarget-trackingAuto Scaling Settings
Warm PoolfalseAuto Scaling Settings
Auto Scaling groups per region500Service Quotas
Launch configurations per region200Service Quotas
Scaling policies per group50Service Quotas
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