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The Resource Usage Aware Backfilling

机译：资源使用意识回填

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Job scheduling policies for HPC centers have been extensively studied in the last few years, especially backfilling based policies. Almost all of these studies have been done using simulation tools. All the existent simulators use the runtime (either estimated or real) provided in the workload as a basis of their simulations. In our previous work we analyzed the impact on system performance of considering the resource sharing (memory bandwidth) of running jobs including a new resource model in the Alvio simulator. Based on this studies we proposed the LessConsume and LessConsume Threshold resource selection policies. Both are oriented to reduce the saturation of the shared resources thus increasing the performance of the system. The results showed how both resource allocation policies shown how the performance of the system can be improved by considering where the jobs are finally allocated.rnUsing the LessConsume Threshold Resource Selection Policy, we propose a new backfilling strategy : the Resource Usage Aware Backfilling job scheduling policy. This is a backfilling based scheduling policy where the algorithms which decide which job has to be executed and how jobs have to be backfilled are based on a different Threshold configurations. This backfilling variant that considers how the shared resources are used by the scheduled jobs. Rather than backfilling the first job that can moved to the run queue based on the job arrival time or job size, it looks ahead to the next queued jobs, and tries to allocate jobs that would experience lower penalized runtime caused by the resource sharing saturation.rnIn the paper we demostrate how the exchange of scheduling information between the local resource manager and the scheduler can improve substantially the performance of the system when the resource sharing is considered. We show how it can achieve a close response time performance that the shorest job first Backfilling with First Fit (oriented to improve the start time for the allocated jobs) providing a qualitative improvement in the number of killed jobs and in the percentage of penalized runtime.
机译：过去几年中，对HPC中心的作业调度策略进行了广泛的研究，尤其是基于回填的策略。几乎所有这些研究都是使用仿真工具完成的。所有现有的模拟器都使用工作负载中提供的运行时（估计的或实际的）作为其模拟的基础。在我们之前的工作中，我们考虑了正在运行的作业的资源共享（内存带宽），包括Alvio模拟器中的新资源模型，从而分析了对系统性能的影响。基于此研究，我们提出了LessConsume和LessConsume Threshold资源选择策略。两者都旨在降低共享资源的饱和度，从而提高系统的性能。结果表明两种资源分配策略如何通过考虑最终分配作业的位置来显示如何提高系统性能。rn使用LessConsume阈值资源选择策略，我们提出了一种新的回填策略：资源使用感知回填作业调度策略。这是基于回填的调度策略，其中决定必须执行哪个作业以及如何回填作业的算法基于不同的阈值配置。此回填变量考虑了计划作业如何使用共享资源。它没有根据作业到达时间或作业大小回填可以移动到运行队列中的第一个作业，而是前瞻下一个排队的作业，并尝试分配因资源共享饱和而遭受较低的运行时间损失的作业。在本文中，我们演示了在考虑资源共享时，本地资源管理器与调度程序之间的调度信息交换如何能够显着改善系统的性能。我们展示了最先完成的作业首先使用First Fit进行回填（旨在改善分配的作业的开始时间）如何在关闭的作业数量和受罚的运行时间百分比方面实现质量上的改善，从而实现接近的响应时间性能。

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