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首页> 外文期刊>Journal of supercomputing >Reliability-aware task scheduling for energy efficiency on heterogeneous multiprocessor systems
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Reliability-aware task scheduling for energy efficiency on heterogeneous multiprocessor systems

机译:异构多处理器系统能效的可靠性感知任务调度

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摘要

Recent studies mainly focus on high performance or low power consumption for task scheduling on heterogeneous multiprocessor systems (HMSs). Dynamic voltage and frequency scaling (DVFS) is an important energy reduction technique, which adjusts the voltage and frequency of the processor while the task is executing. However, some studies have shown that reducing the voltage of processor increases the transient failure rate, which reduces system reliability. In this paper, we aim at addressing the scheduling problem of optimizing energy under makespan and reliability constraints on HMSs with DVFS. We first propose an improved whale optimization algorithm (WOA) deploying opposition-based learning and individual selection strategy, which can balance the exploration and exploitation ability. To maintain population diversity, we then apply a constrained rank-based method which retains some infeasible individuals in the population. Finally, we reschedule the Critical Path Nodes (CPNs) to further improve the performance of improved WOA. The main difference between our work and most previous works is that we study a new scheduling problem, and utilize an improved WOA algorithm integrating with rescheduling CPNs and a constrained rank-based method. Extensive experiments are conducted to evaluate our proposed algorithm, and the evaluation results show that our proposed algorithm is compelling in comparison with the state-of-the-art algorithms.
机译:最近的研究主要集中在异构多处理器系统(HMSS)上的任务调度的高性能或低功耗。动态电压和频率缩放(DVFS)是一种重要的能量减少技术,其在执行任务时调整处理器的电压和频率。然而,一些研究表明,降低处理器的电压会增加瞬态故障率,这降低了系统可靠性。在本文中,我们的目的是解决优化MakEspan和DVFS的HMSS的可靠性限制的调度问题。我们首先提出了一种改进的鲸鱼优化算法(WOA)部署基于反对派的学习和个人选择策略,这可以平衡勘探和利用能力。为了维持人口多样性,我们将采用受约束的基于级别的方法,该方法保留了人口中的一些不可行的个体。最后,我们重新安排关键路径节点(CPNS),以进一步提高改进的WOA的性能。我们的工作与最先前作品之间的主要区别在于我们研究了一个新的调度问题,并利用了与重新安排CPNS和基于约束的秩的改进的WOA算法。进行了广泛的实验以评估我们所提出的算法,评价结果表明,与最先进的算法相比,我们所提出的算法是引人注目的。

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