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An Improved Genetic-Based Approach to Task Scheduling in Inter-cloud Environment

机译:一种改进的基于遗传的云间任务调度方法

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摘要

With the development of cloud computing, the number of cloud computing service providers has arisen rapidly. The research of task scheduling in cloud computing environment nearly enters a mature stage. But when there is a sharp increase in the amount of user tasks, a single cloud provider cannot meet user's needs. This phenomenon prompted the generation of Inter-cloud, and the task scheduling in which gradually gets everyone's attention. In this paper, we improve the genetic algorithm by adopting Gene Space Balance Strategy (GSBS), which optimizes the generation of initial population. On the basis of improved algorithm, we propose the multi-objective optimization task scheduling method in Inter-cloud. The scheduling goal is to minimize the completion time and cost. We can complete task scheduling according to the different QoS requirements of users. By performing simulation on cloud Sim, we demonstrate the effectiveness of improved algorithm. At the same time, we compare the scheduling results of single-cloud and Inter-cloud.
机译:随着云计算的发展,云计算服务提供商的数量迅速增加。云计算环境下的任务调度研究已进入成熟阶段。但是,当用户任务数量急剧增加时,单个云提供商将无法满足用户的需求。这种现象促使了Inter-cloud的产生,并且任务调度逐渐引起了所有人的关注。在本文中,我们通过采用基因空间平衡策略(GSBS)改进了遗传算法,从而优化了初始种群的产生。在改进算法的基础上,提出了云间多目标优化任务调度方法。计划目标是最大程度地减少完成时间和成本。我们可以根据用户的不同QoS要求完成任务调度。通过在云Sim上进行仿真,我们证明了改进算法的有效性。同时,我们比较了单云和跨云的调度结果。

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