Job Scheduling in the Presence of Multiple Resource Requirements.

Abstract

Recent parallel processing systems, such as the SUN E10000, are made up of pools of independently allocatable hardware and software resources such a shared memory, large disk farms, distinct I/O channels, and software licenses. In order to make efficient use of all the available system resources, the scheduling algorithm must be able to maintain a job working set which fully utilizes all of the resources. Previous work in scheduling multiple resources focused on coordinating the allocation of CPUs and memory, using ad-hoc methods for generating good schedules. We provide new job selection heuristics based on "resource balancing" which support the construction of generalized K-resource scheduling algorithms. We show through simulation that performance gains of up to 50% in average response time are achievable over classical scheduling methods such as First-Come-First-Served with First-Fit Backfilling.