AKS Node Pool Automation: Scaling Beyond Limits

Overview In this deep dive, we'll explore implementing intelligent node pool scaling with custom metrics and cost optimization strategies for Azure Kubernetes Service.

The Challenge Manual node scaling during traffic spikes was causing: - Service degradation during peak hours - Increased operational overhead - Suboptimal resource utilization - Higher infrastructure costs

Solution Architecture

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: aks-autoscaler
  namespace: kube-system
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: cluster-autoscaler
  minReplicas: 1
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70

Implementation Steps

1. Custom Metrics Setup First, we configured Azure Monitor to expose custom metrics:

# Configure Azure Monitor for custom metrics
az monitor metrics alert create \
  --name "NodePoolScaling" \
  --resource-group "aks-rg" \
  --scopes "/subscriptions/.../resourceGroups/aks-rg" \
  --condition "avg Percentage CPU > 80" \
  --description "Trigger node pool scaling"

2. Scaling Logic The autoscaler uses a combination of: - CPU utilization (primary metric) - Memory pressure (secondary) - Pending pod count (tertiary)

3. Cost Optimization Implemented intelligent scheduling to: - Prefer spot instances for non-critical workloads - Scale down during off-peak hours - Use mixed instance types for better cost efficiency

Results After implementation: - 40% reduction in infrastructure costs - 99.9% uptime during traffic spikes - 60% faster scaling response times - Zero manual interventions required

Key Learnings 1. **Custom metrics are essential** - Default CPU/memory metrics aren't enough 2. **Gradual scaling prevents thrashing** - Implement proper cooldown periods 3. **Monitor scaling events** - Comprehensive logging is crucial for debugging

Incident Response Integration The autoscaler integrates with our incident response system:

# Emergency scaling command
kubectl patch hpa aks-autoscaler -p '{"spec":{"maxReplicas":20}}'

Conclusion Intelligent node pool automation is crucial for maintaining service reliability while optimizing costs. The key is finding the right balance between responsiveness and stability.

--- *This post is part of our SRE Engineering Logs series. For more insights, check out our other technical deep-dives.*