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Predictive Pod Autoscaler (PPA)

Predictive Pod Autoscaler (PPA) is an AI-driven horizontal pod autoscaling solution for Kubernetes. It leverages Facebook's Prophet for time-series forecasting to proactively scale your workloads based on historical CPU and memory usage patterns.

Why PPA?

Standard Kubernetes HPA and KEDA scalers are reactive—they scale up when a threshold is breached. PPA is proactive—it anticipates traffic spikes and scales your application before the load hits, ensuring better performance and availability.

Key Features

  • 🧠 Predictive Scaling: Uses Prophet for accurate time-series forecasting.
  • ⚡ KEDA Integration: Works as an External Scaler for KEDA, allowing you to use it alongside other KEDA scalers.
  • 🔄 Automated Training: Hourly retraining of models using Argo Workflows ensures your scaling logic adapts to changing patterns.
  • 🚀 Cloud-Native Architecture: Built on top of KServe, MLServer, and Argo Workflows.
  • 🛠️ Declarative Management: Define your predictive scaling models using a simple Custom Resource (Model).

How it Works

  1. Fetch: PPA fetches historical metrics (CPU/Memory) from Prometheus.
  2. Train: An Argo Workflow trains a Prophet model on the collected data.
  3. Serve: The trained model is deployed as a KServe InferenceService.
  4. Scale: The PPA Scaler queries the InferenceService and tells KEDA how many replicas are needed.