Exploiting fine-grained idle periods in networks of workstations

Studies have shown that for a significant fraction of the time, workstation s 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 characterizat ion 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: I mmediate-Eviction and Pause-and-Migrate. Our study shows that the Linger-Lo nger policy can improve the throughput of foreign jobs on a cluster by 60 p ercent with only a 0.5 percent slowdown of local jobs. For parallel computi ng, we show that the Linger-Longer policy outperforms reconfiguration strat egies when the processor utilization by the local process is 20 percent or less in both synthetic bulk synchronous and real data-parallel applications .