Scheduling parallel jobs with CPU and I/O resource requirements in cluster computing systems

Abstract

This paper addresses the problem of an on-line coordinated allocation of processor and I/O resources in large-scale shared heterogeneous cluster computing systems. Most research in job scheduling study has focused solely on the allocation of processors to jobs. However, since I/O is also a critical resource for many jobs, the allocation of processor and I/O resources must be coordinated to allow the system to operate most effectively. To this end, we present an efficient job scheduling policy and study its performance under various system and workload parameters. We also compared the performance of the proposed policy with static space time sharing policy. The results show that the proposed policy performs substantially better than the static space time sharing policy.