Promoting Resource Utilization in HPC via Scheduling

The increasing complexity of supercomputing workloads poses challenges to efficient resource management, especially in balancing computational and I/O demands. Shared burst buffers, as high‐speed intermediate storage, offer a promising avenue to mitigate I/O bottlenecks. However, naive job scheduling strategies often neglect the potential of burst buffers, relying on heuristic methods with limited adaptability. To address this, we propose an innovative burst‐buffer‐aware scheduling framework that integrates burst buffer capacity into job scheduling as a key resource. Through multi‐objective optimization, the framework intelligently balances trade‐offs among job waiting time, slowdown, and completion time, surpassing the rigidity of conventional approaches. Leveraging real‐world workload traces, the framework dynamically adapts to varying windows and job characteristics, combining computation and burst buffer demands to optimize scheduling decisions. Experimental results reveal that the proposed framework enhances scheduling efficiency and system adaptability, establishing a smarter and more effective approach to supercomputing job scheduling. This work underscores the importance of burst‐buffer‐aware strategies in advancing high‐performance computing, offering novel insights into intelligent resource management.