Scaling Kubernetes Pods with Watermark Pod Autoscaler for Cost Management

Understanding Kubernetes Autoscaling Challenges

Traditional Kubernetes autoscalers, like the Horizontal Pod Autoscaler (HPA), primarily react to metrics such as CPU or memory utilization to scale pods up or down. While effective for general scaling, this reactive approach can sometimes lead to 'thrashing' (frequent scaling events) or cost inefficiencies due to over-provisioning during low-demand periods, or slow reaction to spikes. Designing a robust autoscaling strategy is critical for performance and cost control in dynamic microservice architectures.

Introducing the Watermark Pod Autoscaler (WPA)

The Watermark Pod Autoscaler (WPA) offers a more nuanced approach by introducing 'low' and 'high' watermarks for metrics. Instead of scaling based on a single threshold, WPA uses these ranges to determine when to scale up or down. This mechanism provides a buffer, reducing unnecessary scaling events and allowing for more stable resource allocation. This is particularly beneficial for applications with predictable spikes or troughs, enabling a more proactive scaling strategy.

Architectural Benefits for Cost Optimization

From a system design perspective, WPA's configurable watermarks allow architects to fine-tune scaling behavior to align with application-specific requirements and cost targets. By preventing rapid scale-down (due to a low watermark) and ensuring adequate buffer before scaling up (due to a high watermark), WPA helps in: 1) Reducing cloud infrastructure costs by minimizing over-provisioning, 2) Improving application stability by preventing rapid oscillations in pod counts, and 3) Providing more predictable performance by maintaining a suitable number of ready pods.

• Configurability: Define distinct low and high watermarks for better control.
• Stability: Reduces 'thrashing' and unnecessary scaling operations.
• Cost-efficiency: Optimizes resource utilization by avoiding rapid over-provisioning or under-provisioning.
• Predictability: More stable pod counts lead to more predictable application performance.

apiVersion: datadoghq.com/v1alpha1
kind: WatermarkPodAutoscaler
metadata:
  name: my-app-wpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: my-app
  minReplicas: 2
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      high: 80
      low: 40