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We propose a novel job scheduling approach for homogeneous cluster computingplatforms. Its key feature is the use of virtual machine technology to sharefractional node resources in a precise and controlled manner. Other VM-basedscheduling approaches have focused primarily on technical issues or onextensions to existing batch scheduling systems, while we take a moreaggressive approach and seek to find heuristics that maximize an objectivemetric correlated with job performance. We derive absolute performance boundsand develop algorithms for the online, non-clairvoyant version of ourscheduling problem. We further evaluate these algorithms in simulation againstboth synthetic and real-world HPC workloads and compare our algorithms tostandard batch scheduling approaches. We find that our approach improves overbatch scheduling by orders of magnitude in terms of job stretch, while leadingto comparable or better resource utilization. Our results demonstrate thatvirtualization technology coupled with lightweight online scheduling strategiescan afford dramatic improvements in performance for executing HPC workloads.

Dynamic Fractional Resource Scheduling vs. Batch Scheduling