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首页> 外文期刊>Computers, IEEE Transactions on >EAD and PEBD: Two Energy-Aware Duplication Scheduling Algorithms for Parallel Tasks on Homogeneous Clusters
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EAD and PEBD: Two Energy-Aware Duplication Scheduling Algorithms for Parallel Tasks on Homogeneous Clusters

机译：EAD和PEBD：均质集群上并行任务的两种能量感知复制调度算法

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摘要

High-performance clusters have been widely deployed to solve challenging and rigorous scientific and engineering tasks. On one hand, high performance is certainly an important consideration in designing clusters to run parallel applications. On the other hand, the ever increasing energy cost requires us to effectively conserve energy in clusters. To achieve the goal of optimizing both performance and energy efficiency in clusters, in this paper, we propose two energy-efficient duplication-based scheduling algorithmsȁ4;Energy-Aware Duplication (EAD) scheduling and Performance-Energy Balanced Duplication (PEBD) scheduling. Existing duplication-based scheduling algorithms replicate all possible tasks to shorten schedule length without reducing energy consumption caused by duplication. Our algorithms, in contrast, strive to balance schedule lengths and energy savings by judiciously replicating predecessors of a task if the duplication can aid in performance without degrading energy efficiency. To illustrate the effectiveness of EAD and PEBD, we compare them with a nonduplication algorithm, a traditional duplication-based algorithm, and the dynamic voltage scaling (DVS) algorithm. Extensive experimental results using both synthetic benchmarks and real-world applications demonstrate that our algorithms can effectively save energy with marginal performance degradation.
机译：高性能集群已被广泛部署，以解决具有挑战性的严谨科学和工程任务。一方面，在设计集群以运行并行应用程序时，高性能无疑是重要的考虑因素。另一方面，不断增长的能源成本要求我们有效地节约集群能源。为了达到优化集群性能和能效的目标，本文提出了两种基于节能复制的调度算法ȁ4：能量感知复制（EAD）调度和性能-能量平衡复制（PEBD）调度。现有的基于复制的调度算法可复制所有可能的任务，以缩短调度长度，而不会减少由复制引起的能耗。相比之下，我们的算法会通过明智地复制任务的前任者（如果复制可以在不降低能源效率的情况下提高性能）来努力平衡计划长度和节能。为了说明EAD和PEBD的有效性，我们将它们与非复制算法，传统的基于复制的算法以及动态电压缩放（DVS）算法进行了比较。使用综合基准和实际应用的大量实验结果表明，我们的算法可以有效地节省能源，同时降低性能。

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