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Energy-aware scheduling using slack reclamation for cluster systems

机译:使用Slack Checlamation进行集群系统的能量感知调度

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Reducing energy consumption in processors has become a critical issue in cluster systems. The dynamic voltage and frequency scaling (DVFS) is one of the effective techniques to minimize energy consumption in processors when executing parallel applications. These applications may consist of many interrelated tasks that may be computed when their precedence constraints are satisfied. The scheduling of these precedence-constrained tasks on to processors, to optimize makespan or energy consumption or both, has been studied as an NP-complete problem. Thus, many algorithms have been given by the researchers and newer algorithms keep coming in. In the parallel applications, there exist some tasks which may have slacks due to the dependencies between tasks. The EASLA (energy aware service level agreement) is one of the well-known energy-aware task scheduling algorithms which utilize the underused slack of tasks. In this paper, we present an improved version of its sub-algorithm, NCM (Not Changing Makespan), for heterogeneous cluster systems that is used to downscale frequencies when schedule length does not change. The presented algorithm also makes use of a fast and low complexity algorithm called PEFT (Predict Earliest Finish Time) to compute the schedule length of the application. We performed experiments for randomly generated graphs, and the results illustrate that the improved version of the NCM algorithm achieves better energy saving compared to the NCM algorithm.
机译:降低处理器中的能量消耗已成为集群系统的关键问题。动态电压和频率缩放(DVFS)是在执行并行应用时最小化处理器中的能量消耗的有效技术之一。这些应用程序可以包括许多相互关联的任务,当满足其优先约束时可以计算。这些优先级受约束的任务对处理器的调度,以优化MakEspan或能源消耗或两者,已被研究作为NP完整问题。因此,研究人员已经给出了许多算法和较新的算法。在并行应用程序中,存在一些任务,这可能由于任务之间的依赖性而具有松弛。 EASLA(能量感知服务级别协议)是利用未被发救的任务的众所周知的能量感知任务调度算法之一。在本文中,我们介绍了其子算法的改进版本,NCM(不改变Makespan),用于在时间表长度不改变时用于低速频率的异构群集系统。呈现的算法还利用了一种名为PEFT(预测最早结束时间)的快速和低复杂度算法来计算应用程序的调度长度。我们对随机生成的图表进行了实验,结果说明了与NCM算法相比,NCM算法的改进版本实现了更好的节能。

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