Configuring Auto-Scaling Policies

Overview

Configure auto-scaling policies for cloud workloads across AWS Auto Scaling Groups, GCP Managed Instance Groups, Azure VMSS, and Kubernetes Horizontal Pod Autoscaler (HPA). Generate scaling configurations based on CPU, memory, request rate, or custom metrics with appropriate thresholds, cooldown periods, and scale-in protection.

Prerequisites

• Cloud provider CLI installed and authenticated (aws, gcloud, or az)
• For Kubernetes HPA: kubectl configured with cluster access and metrics-server deployed
• Baseline performance data for the target workload (average CPU, memory, request rate)
• Understanding of traffic patterns (steady, bursty, scheduled)
• IAM permissions to create/modify scaling policies and CloudWatch/Stackdriver alarms

Instructions

1. Identify the scaling target: EC2 Auto Scaling Group, GCP MIG, Azure VMSS, or Kubernetes Deployment
2. Analyze current workload metrics to establish baseline utilization and peak patterns
3. Define scaling boundaries: minimum instances/pods, maximum instances/pods, desired count
4. Select scaling metric(s): CPU utilization, memory, request count, queue depth, or custom metrics