An AI-Assisted VMware Resource Management System

Abstract

Virtualized data centers running on VMware struggle with resource management. Traditional, reactive allocation strategies trigger high operational expenses and performance bottlenecks. To address this, we developed an "AI-Assisted VMware Resource Management System" designed to establish a predictive, autonomous operational framework. The architecture pairs machine learning for resource demand forecasting with Deep Reinforcement Learning (DRL) for real-time allocation decisions. Operating via VMware vSphere APIs, the platform directly controls virtual machine workloads without human intervention. We validated a functional prototype in a simulated environment, benchmarking its impact on cost reduction and resource utilization against static allocation baselines. CPU costs dropped by nearly 87%; RAM costs by half. Thus, the resulting framework confirms that integrating predictive and reinforcement models substantially improves virtualized IT infrastructure efficiency, cost-effectiveness, and stability.