AI-based load balancing for energy-efficient cloud data centers

Abstract

Cloud data centers are the backbone of modern computing, supporting everything from social media platforms to enterprise applications. However, their rapid growth has led to massive electricity consumption, driving up operational costs and contributing significantly to global carbon emissions. Traditional load balancing algorithms such as Round Robin and Least Connection focus primarily on performance and fairness but often neglect energy efficiency. Artificial Intelligence (AI) introduces adaptive, predictive, and dynamic approaches that optimize resource utilization while reducing energy consumption. This paper explores AIbased load balancing techniques, reviews existing literature, and presents a methodology for evaluating their effectiveness in simulated environments. Results demonstrate that AI-driven approaches can achieve 15–20% energy savings, improve response times by 10–12%, and enhance overall system reliability. The findings suggest that AI-based load balancing is a promising solution for building sustainable, cost-effective, and high-performance cloud infrastructures, contributing to green computing and reduced carbon footprint