Network-aware selective job checkpoint and migration to enhance co-allocation in multi-cluster systems

Multi-site parallel job schedulers can improve average job turn-around time by making use of fragmented node resources available throughout the grid. By mapping jobs across potentially many clusters, jobs that would otherwise wait in the queue for local resources can begin execution much earlier; thereby improving system utilization and reducing average queue waiting time. Recent research in this area of scheduling leverages user-provided estimates of job communication characteristics to more effectively partition the job across system resources. In this paper, we address the impact of inaccuracies in these estimates on system performance and show that multi-site scheduling techniques benefit from these estimates, even in the presence of considerable inaccuracy. While these results are encouraging, there are instances where these errors result in poor job scheduling decisions that cause network over-subscription. This situation can lead to significantly degraded application performance and turnaround time. Consequently, we explore the use of job checkpointing, termination, migration, and restart (CTMR) to selectively stop offending jobs to alleviate network congestion and subsequently restart them when (and where) sufficient network resources are available. We then characterize the conditions and the extent to which the process of CTMR improves overall performance. We demonstrate that this technique is beneficial even when the overhead of doing so is costly. Copyright c © 2009 John Wiley & Sons, Ltd.