Scheduling Grid workloads on multicore clusters to minimize energy and maximize performance
Scheduling Grid workloads on multicore clusters to minimize energy and maximize performance
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在多核集群上调度网格工作负载，以最大限度地减少能源并最大限度地提高性能
DOI：
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
D. Thain
中科院分区：
文献类型：
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作者：
Michael Lammie;P. Brenner;D. Thain
能源是大型计算设施运行成本的一个重要且不断增长的组成部分。由在数千个处理器上运行的数百万个作业组成的网格工作负载可能会消耗数百万千瓦时的电力。然而，由于网格工作负载通常由许多独立的顺序进程组成，因此我们可以调整其执行以满足能量约束。通过改变可用处理器的数量和频率，调度程序可以在能量与性能之间进行权衡。在本文中，我们探讨了大型集群上网格工作负载调度中的能源和性能权衡。我们以之前的工作为基础，展示了多核集群上智能作业分配、自动节点扩展和频率扩展的交互。一个意想不到的结果是，尽管低频是运行单个节点的最有效模式，但仔细应用频率缩放实际上可以通过减少开机节点数量来进一步降低总体能耗。
Energy is a significant and growing component of the cost of running a large computing facility. A grid workload consisting of millions of jobs running on thousands of processors may consume millions of kilowatt hours of electricity. However, because a grid workload generally consists of many independent sequential processes, we may shape its execution to satisfy energy constraints. By varying the number and frequency of processors available, a scheduler may trade off energy against performance. In this paper, we explore energy and performance tradeoffs in the scheduling of grid workloads on large clusters. We build upon previous work by showing the interaction of intelligent job assignment, automated node scaling, and frequency scaling on multicore clusters. An unexpected result is that, even though low frequency is the most efficient mode of operating a single node, the careful application of frequency scaling can actually reduce overall energy consumption even further by reducing the number of nodes powered on.