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云环境下结合模糊商空间理论的资源调度算法 被引量:6

Task Scheduling Algorithm Based on Fuzzy Quotient Space Theory in Cloud Environment
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摘要 考虑到云计算商业化和虚拟化特点,针对云环境中的高效资源调度问题,提出一种基于模糊商空间理论的资源调度算法.在进行资源调度时,算法首先将虚拟机资源抽象为不同的属性信息粒,再根据用户任务QoS特征分层进行粒度融合,最后结合模糊商空间理论建立模糊等价类和距离函数,并据此进行资源匹配.实验结果分析表明,该算法能有效的满足用户任务QoS,提高资源利用率. Considered the commercialization and the virtualization characteristics of cloud computing, focusing on the problem of high efficiency and effectiveness resource scheduling, the paper proposed for the first time an algorithm of resource scheduling based on fuzzy quotient space theory. In the resource scheduling process, each virtual machine attribute is abstracted as an attribute information granulation at first. Then the multi-attribute information granulation according to their granular weight, which are defined by the user QoS requirement is studied. Combining with the theory of fuzzy quotient space, fuzzy equivalence partition and distance function are given at last. Based on this, the matching of tasks with resources in cloud environment is implemented. The experimental results show that the algorithm can effectively execute the user tasks and increase resource utilization rate.
作者 齐平 李龙澍
出处 《小型微型计算机系统》 CSCD 北大核心 2013年第8期1793-1797,共5页 Journal of Chinese Computer Systems
基金 国家自然科学基金项目(60273043)资助 安徽省自然科学基金项目(090412054) 安徽省科技攻关计划重大科技专项项目(08010201002)资助
关键词 资源调度 模糊商空间 粒度融合 云计算 resource scheduling fuzzy quotient space granular size fusion cloud computing
作者简介 齐平,男,1981年生,讲师,博士研究生,研究方向为不精确信息处理、云计算; 李龙澍,男,1956年生,教授,博士生导师,研究方向为智能软件、知识工程等.
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  • 1徐章艳,刘作鹏,杨炳儒,宋威.一个复杂度为max(O(|C||U|),O(|C^2|U/C|))的快速属性约简算法[J].计算机学报,2006,29(3):391-399. 被引量:234
  • 2Dawei Sun,Guiran Chang,Changsheng Miao,Xingwei Wang.Analyzing, modeling and evaluating dynamic adaptive fault tolerance strategies in cloud computing environments[J]. The Journal of Supercomputing . 2013 (1)
  • 3Bo Yang,Feng Tan,Yuan-Shun Dai.Performance evaluation of cloud service considering fault recovery[J]. The Journal of Supercomputing . 2013 (1)
  • 4Micah Dowty,Jeremy Sugerman.GPU virtualization on VMware’s hosted I/O architecture[J]. ACM SIGOPS Operating Systems Review . 2009 (3)
  • 5Pajorová E,Hluch L.Complicated simulation visualization based on grid andcloud computing. Cooperative Design, Visualization, and Engineering . 2010
  • 6Sean Marston,Zhi Li,Subhajyoti Bandyopadhyay,Juheng Zhang,Anand Ghalsasi.Cloud computing — The business perspective. Decision Support . 2011
  • 7Qian L,Luo Z G, et al.Cloud Computing:An Overview. Lecture Notes in Computer Notes . 2009
  • 8Asael Dror,Hao Zhang,B Anil Kumar,et al.Virtualized GPU in a Virtual Machine Environment. United States:US 20110102443A1 . 2011
  • 9Jose Duato,Francisco D.Igual,Rafael Mayo,et al.An Efficient Implementation of GPU Virtualization in High Performance Clusters. Lecture Notes in Computer Science . 2010
  • 10Sunit Parmar,Aniruddh Kurtkoti.An Approach To Graphics Passthrough In Cloud Virtual Machines. International Journal of Engineering Research&Technology . 2013

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