Autoscaling Configuration

Table of Contents

• Overview
• When to Use
• Quick Start
• Reference Guides
• 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