Autoscaling Configuration
Table of Contents
Overview
Implement autoscaling strategies to automatically adjust resource capacity based on demand, ensuring cost efficiency while maintaining performance and availability.
When to Use
- Traffic-driven workload scaling
- Time-based scheduled scaling
- Resource utilization optimization
- Cost reduction
- High-traffic event handling
- Batch processing optimization
- Database connection pooling
Quick Start
Minimal working example:
yaml# hpa-configuration.yaml apiVersion: autoscaling/v2 kind: HorizontalPodAutoscaler metadata: name: myapp-hpa namespace: production spec: scaleTargetRef: apiVersion: apps/v1 kind: Deployment name: myapp minReplicas: 2 maxReplicas: 20 metrics: - type: Resource resource: name: cpu target: type: Utilization averageUtilization: 70 - type: Resource resource: name: memory target: type: Utilization // ... (see reference guides for full implementation)
Reference Guides
Detailed implementations in the references/ directory:
| Guide | Contents |
|---|---|
| Kubernetes Horizontal Pod Autoscaler | Kubernetes Horizontal Pod Autoscaler |
| AWS Auto Scaling | AWS Auto Scaling |
| Custom Metrics Autoscaling | Custom Metrics Autoscaling |
| Autoscaling Script | Autoscaling Script |
| Monitoring Autoscaling | Monitoring Autoscaling |
Best Practices
✅ DO
- Set appropriate min/max replicas
- Monitor metric aggregation window
- Implement cooldown periods
- Use multiple metrics
- Test scaling behavior
- Monitor scaling events
- Plan for peak loads
- Implement fallback strategies
❌ DON'T
- Set min replicas to 1
- Scale too aggressively
- Ignore cooldown periods
- Use single metric only
- Forget to test scaling
- Scale below resource needs
- Neglect monitoring
- Deploy without capacity tests
