期刊名称：International Journal of Grid and Distributed Computing
印刷版ISSN：2005-4262
出版年度：2016
卷号：9
期号：11
页码：309-322
出版社：SERSC
摘要：With the development of cloud computing technology, people not only want to pursue the shortest time to complete the tasks byusing cloud computing, but also hope to take into the runningcostsof machines. Existing task scheduling algorithm in the cloud computing environment has been unable to meet people's needs. As an extension and generalization of the model checking theory, probabilitymodel checking is also used in many fields, such as random distributed algorithm and other areas. Thetask scheduling algorithm based on the particle swarm optimization algorithm combined with probabilitymodel is proposed in this paper. The algorithm defines the fitness functions ofthe time cost and the running cost.The fitness functionscan improve the efficiency of the cloud computing platform. At the same time, the probability model can be used to analyze the running states of machines andthecomputing capability ofthe nodes in the cloud cluster. The probability,which iscalculated by the probability model,providesthe basis for changingparticle swarm algorithm’stheinertia factor and the learning factor, so as to solve the drawback that the inertia factor and the learning factor solely depend on the fixed value.
