Kubernetes Horizontal Pod Autoscaler (HPA) is reactive, scaling only when resource usage crosses predefined thresholds. This approach leads to delayed scaling decisions and resource inefficiencies.
This project introduces a predictive autoscaling model that leverages machine learning on historical resource usage data to proactively determine the optimal number of replicas for a deployment. By forecasting future CPU and memory consumption, this model ensures efficient autoscaling while reducing the risk of over-provisioning and under-provisioning.
Predictive Scaling – Anticipates resource demands instead of reacting late
Machine Learning Model – Uses Linear Regression to estimate the required number of replicas
Real-time Kubernetes Integration – Fetches live metrics and adjusts scaling dynamically
Improved Resource Utilization – Reduces unnecessary replica allocation and cost overhead
Minimal Latency – Quick decision-making for optimal autoscaling
Below is the flow diagram illustrating how metrics are collected, processed, and used for predictive scaling:
