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Efficient energy aware task scheduling for parallel workflow tasks on hybrids cloud environment

机译:Hybrids云环境上并行工作流任务的高效能量感知任务调度

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In this paper, the challenge of scheduling a parallel application on a cloud environment to achieve both time and energy efficiency is addressed. Two energy-aware task scheduling algorithms called the EHEFT and the ECPOP are proposed to address the challenge. These algorithms have the objective of trying to sustain the makespan and energy consumption at the same time. The concept is to use a metric that identify the inefficient processors and shut them down to reduce energy consumption. Then, the task is rescheduled to use fewer processors to obtain more energy efficiency. The experimental results from the simulation show that our enhanced algorithms not only reduce the energy consumption, but also maintain a good quality of the scheduling. This will enable the efficient use of the cloud system as a large scalable computing platform.
机译:在本文中,解决了在云环境上调度并行应用来实现时间和能效的挑战。提出了两个能量感知任务调度算法,称为EHeft和ECPOP以解决挑战。这些算法具有试图同时维持Mapspan和能源消耗的目标。该概念是使用标准来识别低效处理器并将其关闭以降低能量消耗。然后,重新安排任务以使用更少的处理器来获得更多能效。仿真的实验结果表明,我们的增强算法不仅降低了能源消耗,还保持了良好的调度质量。这将使云系统能够有效地使用作为一个大的可扩展计算平台。

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