Gang Scheduling for Distributed Memory Systems

Abstract

Time-sliced gang scheduling improves the average response time of the jobs in a typical job stream. Recent research has shown that time-slicing is most effective when the jobs admitted for execution fit entirely into physical memory. We investigate two techniques for improving the performance of gang scheduling in the presence of memory pressure: 1) a novel backfill approach which improves memory utilization, and 2) an adaptive multi-programming level which balances processor/memory utilization with job response time performance. Our simulations show that these techniques reduce the slow-down performance metrics over naive FCFS methods on a distributed memory parallel system.