SKILL.md
Autoscaling Configuration
Table of Contents
- [Overview](#overview)
- [When to Use](#when-to-use)
- [Quick Start](#quick-start)
- [Reference Guides](#reference-guides)
- [Best Practices](#best-practices)
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:
# 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