Cluster Scheduling

Cluster scheduling optimizes the allocation of computational resources (like GPUs) across multiple jobs in a high-performance computing environment, aiming to minimize job completion times and maximize resource utilization. Recent research focuses on integrating advanced techniques like reinforcement learning to create more efficient and adaptable scheduling policies, addressing challenges such as network contention and the need for interpretable models. These improvements are crucial for accelerating computationally intensive tasks, particularly in deep learning and large-scale scientific simulations, leading to faster research and development cycles and enhanced productivity in various fields.