AI Techniques for Load Balancing and Resource Management in Distributed High-Performance Data Processing Systems

Abstract

This paper examines the challenges associated with load balancing and resource management in large-scale systems, including heterogeneous computing resources, fluctuating workloads, and dynamic resource demands. AI techniques such as reinforcement learning, deep learning-based optimization, and evolutionary algorithms are analyzed for their potential to autonomously adapt and predict the best allocation strategies based on real-time performance data and system behavior. The study also highlights the importance of intelligent resource scheduling, fault tolerance, and energy efficiency in optimizing system performance. AI-driven solutions, such as predictive load balancing and automated resource provisioning, are discussed as ways to ensure efficient utilization of both on-premise and cloud-based infrastructures while minimizing response times and avoiding overloading. Through real-world case studies from sectors like cloud computing, financial services, and scientific computing, the paper demonstrates the practical impact of AI in solving real-time load balancing challenges and improving resource utilization. It concludes by offering recommendations for organizations looking to integrate AI-powered techniques into their distributed high-performance data processing systems, paving the way for more efficient and adaptive architectures in the face of growing data volumes and computational complexity.