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A parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems

机译:用于云计算系统的能量感知调度的并行双目标混合元启发式方法

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In this paper, we investigate the problem of scheduling precedence-constrained parallel applications on heterogeneous computing systems (HCSs) like cloud computing infrastructures. This kind of application was studied and used in many research works. Most of these works propose algorithms to minimize the completion time (makespan) without paying much attention to energy consumption. We propose a new parallel bi-objective hybrid genetic algorithm that takes into account, not only makespan, but also energy consumption. We particularly focus on the island parallel model and the multi-start parallel model. Our new method is based on dynamic voltage scaling (DVS) to minimize energy consumption. In terms of energy consumption, the obtained results show that our approach outperforms previous scheduling methods by a significant margin. In terms of completion time, the obtained schedules are also shorter than those of other algorithms. Furthermore, our study demonstrates the potential of DVS.
机译:在本文中,我们研究了在诸如云计算基础架构之类的异构计算系统(HCS)上调度优先级受限的并行应用程序的问题。这种应用已被研究并用于许多研究工作中。这些工作大多数都提出了在不十分关注能源消耗的情况下最小化完成时间(制造时间)的算法。我们提出了一种新的并行双目标混合遗传算法,该算法不仅考虑了制造时间,还考虑了能耗。我们特别关注孤岛并行模型和多起点并行模型。我们的新方法基于动态电压缩放(DVS),以最大程度地降低能耗。在能源消耗方面,获得的结果表明,我们的方法在很大程度上优于以前的调度方法。在完成时间方面,获得的时间表也比其他算法的时间表短。此外,我们的研究证明了DVS的潜力。

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