Performance analysis of job scheduling policies in parallel supercomputing environments

In this paper we analyze three general classes of scheduling policies under a workload of large-scale scientific computing. These policies differ int eh manner in which processors are partitioned among the jobs as well as the way in which jobs are prioritized for execution on the partitions. Our results indicate that existing static schemes do not perform well under varying workloads. Adaptive policies tend to make better scheduling decisions, but their ability to adjust to workload changes is limited. Dynamic partitioning policies, on the other hand, yield the best performance and can be tuned to provide desired performance differences among jobs with varying resource demands.