Job Scheduling based on Harmonization between the Requested and Available Processing Power in the Cloud Computing Environment

Elhossiny Ibrahim, Nirmeen A. El-Bahnasawy, F. Omara
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引用次数: 8

Abstract

The Cloud Computing is a most recent computing paradigm where IT services are provided and delivered over the Internet on demand and pay as you go. On the other hands, the taskscheduling problem is considered one of the main challenges in the Cloud Computing environment, where a good mapping between the available resources and the users’s tasks is needed to reduce the execution time of the users’ tasks (i.e., reduce make-span), in the same time, increase the degree of capitalization from resources (i.e., increase resource utilization). In this paper, a new task scheduling algorithm has been proposed and implemented to reduce the make-span, as well as, increase the resources utilization by considering independent tasks. The proposed algorithm is based on calculating the total processing power of the available resources (i.e., VMs) and the total requested processing power by the users’ tasks,then allocating a group of users’ tasks to each VM according to the ratio of its needed power corresponding to the total processing power of all VMs. To evaluate the performance of the proposed algorithm, a comparative study has been done among the proposed algorithm, and the existed GA, and PSO algorithms. The experimental results show that the proposed algorithm outperforms other algorithms by reducing make-span and increasing the resources utilization.
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云计算环境下基于请求和可用处理能力协调的作业调度
云计算是一种最新的计算范式,其中IT服务是通过互联网按需提供和交付的,并按需付费。另一方面,任务调度问题被认为是云计算环境中的主要挑战之一,在云计算环境中,需要在可用资源和用户任务之间建立良好的映射,以减少用户任务的执行时间(即减少make-span),同时增加资源的资本化程度(即增加资源利用率)。本文提出并实现了一种新的任务调度算法,通过考虑独立任务来减小制作跨度,提高资源利用率。该算法基于计算可用资源(即虚拟机)的总处理能力和用户任务请求的总处理能力,然后根据其所需功率与所有虚拟机总处理能力的比值,将一组用户任务分配给每个虚拟机。为了评价所提算法的性能,将所提算法与已有的遗传算法、粒子群算法进行了比较研究。实验结果表明,该算法在减小make-span和提高资源利用率方面优于其他算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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Job Scheduling based on Harmonization between the Requested and Available Processing Power in the Cloud Computing Environment
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