HRF

Abstract

Moldable jobs, which allow the number of allocated processors to be adjusted before running in clusters, have attracted increasing concern in parallel job scheduling research. Compared with traditional rigid jobs where the number of allocated processors is fixed, moldable jobs are more flexible and therefore have more potential for improving their average turnaround time (a crucial metric to describe performance of jobs in a cluster). Average turnaround time of moldable jobs depends greatly on resource allocation schemes. Unfortunately, existing schemes do not perform well in reducing average turnaround time, either because they only consider a single job's turnaround time instead of the average turnaround time of all jobs, or because they just aim at fairness between short and long jobs instead of their average turnaround time. In this paper, we investigate how resource allocation affects the average turnaround time of moldable jobs in clusters, and propose a scheme named HRF (highest revenue first), which allocates processors according to the highest revenue of shortening runtime. In our simulations, experimental results show that HRF can reduce average turnaround time up to 71% when compared with state-of-the-art schemes.