Fine-Grain Cycle Stealing for Networks of Workstations

Studies have shown that a significant fraction of the time, workstations are idle. In this paper we present a new scheduling policy called Linger-Longer that exploits the fine-grained availability of workstations to run sequential and parallel jobs. We present a two-level workload characterization study and use it to simulate a cluster of workstations running our new policy. We compare two variations of our policy to two previous policies: Immediate- Eviction and Pause-and-Migrate. Our study shows that the Linger-Longer policy can improve the throughput of foreign jobs on cluster by 60% with only a 0.5% slowdown of foreground jobs. For parallel computing, we showed that the Linger-Longer policy outperforms reconfiguration strategies when the processor utilization by the local process is 20% or less in both synthetic bulk synchronous and real data-parallel applications