Load Balancing

Load balancing aims to distribute workloads efficiently across multiple resources, optimizing resource utilization and minimizing delays. Current research emphasizes adaptive algorithms, particularly those leveraging reinforcement learning and multi-agent systems, to handle dynamic workloads and heterogeneous resources in diverse settings like cloud computing, federated learning, and mobile edge computing. These advancements are crucial for improving the performance and scalability of various systems, from large-scale data centers to decentralized networks and even optimizing athlete performance through load management strategies. The development of robust and efficient load balancing techniques is driving significant improvements in the performance and efficiency of numerous applications.