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Resource preprocessing and optimal task scheduling in cloud computing environments

机译:云计算环境中的资源预处理和最佳任务调度

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Cloud computing came into being and is currently an essential infrastructure of many commerce facilities.rnTo achieve the promising potentials of cloud computing, effective and efficient scheduling algorithms arernfundamentally important. However, conventional scheduling methodology encounters a number ofrnchallenges. During the tasks scheduling in cloud systems, how to make full use of resources and how torneffectively select resources are also important factors. At the same time, communication delay also playsrnan important role in cloud scheduling, which not only leads to waiting between tasks but also results in muchrnidle interval time between processing units. In this paper, a fuzzy clustering method is used to effectivelyrnpreprocess the cloud resources. Combining the list scheduling with the task duplication scheduling scheme,rna new directed acyclic graph based scheduling algorithm called earliest finish time duplication algorithm forrnheterogeneous cloud systems is presented. Earliest finish time duplication attempts to insert suitablernimmediate parent nodes of the current selected node in order to reduce its waiting time on the processor.rnThe case study and experimental results illustrate that the algorithm proposed in this paper is better thanrnthe popular heterogeneous earliest finish time algorithms.
机译:云计算应运而生,并且目前是许多商务设施中必不可少的基础设施。为了实现云计算的巨大潜力,有效而高效的调度算法至关重要。然而,常规调度方法遇到许多挑战。在云系统的任务调度中,如何充分利用资源以及如何有效地选择资源也是重要的因素。同时,通信延迟在云调度中也起着重要作用,这不仅导致任务之间的等待,而且导致处理单元之间的间隔时间更长。本文采用模糊聚类方法对云资源进行有效的预处理。将列表调度与任务复制调度方案相结合,提出了一种新的基于有向无环图的调度算法,称为异构云系统的最早完成时间复制算法。最早完成时间复制尝试插入当前所选节点的合适的直接父节点,以减少其在处理器上的等待时间。案例研究和实验结果表明,本文提出的算法比流行的异构最早完成时间算法更好。

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