Workload Distribution

Workload distribution research focuses on efficiently and fairly allocating tasks across multiple processing units, aiming to optimize resource utilization and performance while meeting specific constraints like deadlines or fairness criteria. Current research explores diverse approaches, including Markov models, evolutionary algorithms, and multi-agent reinforcement learning, often tailored to specific application domains such as federated learning, database query optimization, and edge computing. These advancements are crucial for improving the efficiency and sustainability of various systems, from data centers and cloud computing to distributed machine learning and educational resource management. The ultimate goal is to develop robust and adaptive strategies that can handle dynamic workloads and heterogeneous resources effectively.