Autoscaling Configuration

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.yamlapiVersion: autoscaling/v2kind: HorizontalPodAutoscalermetadata:  name: myapp-hpa  namespace: productionspec:  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:

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