autoscaling-configuration

SKILL.md

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

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