As a new type of computing, cloud computing has led to a major computational change. Among many technologies in cloud computing, task scheduling has always been studied as a core issue by industry and academia. In the existing research, the main goal is completion time or load balancing. However, as the expansion of cluster size, energy consumption becomes a problem that must be faced. In this paper, the first of maximum loss scheduling algorithm is proposed. The algorithm is a low-power algorithm that can greatly reduce the energy consumption of cloud computing clusters through loss comparison rule. The effect of this method is more obvious as the cluster size and the number of tasks increase. Experimental simulation results show that the proposed method is significantly better than the Max–Min, Min–Min, Sufferage and E-HEFT algorithms. Compared to Min–Min, Max–Min, Sufferage and E-HEFT algorithms, average completion time of the algorithm reduces 16%, 12%, 8% and 14%, respectively. At the same time, the load balancing effect is also better than Min–Min and Sufferage algorithms.

中文翻译：

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一种面向异构云计算的低功耗任务调度算法

云计算作为一种新型的计算方式，引发了计算的重大变革。在云计算的众多技术中，任务调度一直是工业界和学术界研究的核心问题。在现有的研究中，主要目标是完成时间或负载平衡。然而，随着集群规模的扩大，能源消耗成为必须面对的问题。本文首先提出了最大损失调度算法。该算法是一种低功耗算法，通过损失比较规则可以大大降低云计算集群的能耗。这种方法的效果随着集群规模和任务数量的增加而更加明显。实验仿真结果表明，所提出的方法明显优于 Max-Min、Min-Min、Sufferage 和 E-HEFT 算法。与 Min-Min、Max-Min、Sufferage 和 E-HEFT 算法相比，该算法的平均完成时间分别减少了 16%、12%、8% 和 14%。同时负载均衡效果也优于Min-Min和Sufferage算法。