Performance of Hierarchical Processor Scheduling in Shared-Memory Multiprocessor Systems

Processor scheduling policies can be broadly divided into space-sharing and time-sharing policies. Space-sharing policies partition system processors and each partition is allocated exclusively to a job. In time-sharing policies, processors are temporally shared by jobs (e.g., in a round robin fashion). Space-sharing policies can be either static (processor allocation remains constant during the lifetime of a job) or dynamic (processor allocation changes in response to changes in job parallelism). Equipartition is a dynamic space-sharing policy that has been proposed and studied extensively. Among the time-sharing policies, job-based round robin policy (RRJob) has been shown to be a very good policy. Performance analysis of these two policies suggests that Equipartition policy performs well at low to moderate system loads and is extremely sensitive to system overheads and variance in service demand of jobs. RRJob performs better when there is a high variance in service demand and at high system loads. Furthermore, these policies have been proposed for small-scale shared-memory systems and require a central run queue and/or central scheduler. The central queue/scheduler poses serious scalability problems for large-scale multiprocessor systems. In this paper we propose a new multiprocessor scheduling policy that combines the merits of space-sharing and time-sharing policies while eliminating the contention for the central queue/scheduler. The new policy — called hierarchical scheduling policy (HSP) — uses a hierarchical run queue organization to take advantage of both temporal and spatial partitioning to allocate processing power amongst jobs waiting for service. We show that the HSP policy is considerably better than the purely space-sharing and purely time-sharing policies over a wide range of system parameters.