/autoscaling-configuration

name: autoscaling-configuration
description: >
  Configure autoscaling for Kubernetes, VMs, and serverless workloads based on
  metrics, schedules, and custom indicators.

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](references/kubernetes-horizontal-pod-autoscaler.md) | Kubernetes Horizontal Pod Autoscaler | | [AWS Auto Scaling](references/aws-auto-scaling.md) | AWS Auto Scaling | | [Custom Metrics Autoscaling](references/custom-metrics-autoscaling.md) | Custom Metrics Autoscaling | | [Autoscaling Script](references/autoscaling-script.md) | Autoscaling Script | | [Monitoring Autoscaling](references/monitoring-autoscaling.md) | 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