Enhanced Vertical Pod Auto Scaling with Decision Tree Regressor-Max in Kubernetes

Abstract

Modern containerized applications deployed on Kubernetes demand efficient resource scaling to adapt to varying workloads. Auto-scaling reduces cloud infrastructure costs, increases application stability and improves the Quality of Service from user perspective. This paper introduces an innovative approach for Kubernetes vertical pod autoscaling. The proposed model DTR-Max takes in consideration maximum resources limit of pods allocation using predictive Decision Tree Regression (DTR) policy. The system leverages predictive resource management, enabling proactive adjustments to pod resource requests based on historical utilization patterns and pods resources limit range. In experimental simulations, the system effectively manages resource scaling decisions and demonstrates its potential to adapt Kubernetes pods to dynamic workloads efficiently.