Throughput Enhancement through Selective Time Sharing and Dynamic Grouping

Space sharing approaches are widely used in job scheduling for HPC systems. The main drawback of these approaches is the blocking of short jobs, which results in low throughput. The research on gang scheduling has shown the potential of time sharing in improving throughput. However, traditional gang scheduling adds jobs for time sharing without selection, which may cause a higher performance degradation of existing running jobs than the performance gain of waiting jobs. Moreover, gang scheduling often adopts a contiguous buddy allocation scheme which has problems of fragmentation and low resource utilization. We design a selective time sharing technique that allows waiting jobs to be co-scheduled with existing running jobs only if the overall throughput can be improved. To alleviate the fragmentation problem, we present a dynamic grouping resource allocation mechanism that relaxes the contiguous allocation requirement imposed on gang scheduling. By integrating these techniques, our new job co-scheduling algorithm is able to simultaneously take system throughput and resource utilization into consideration. The experimental results demonstrate that our approach significantly outperforms both EASY backfilling and traditional gang scheduling in terms of both average turnaround time and bounded slowdown.