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Journal of Software:2017.28(2):457-472

支持随机服务请求的云虚拟机按需物理资源分配方法
曹洁,曾国荪,匡桂娟,张建伟,马海英,胡克坤,钮俊
(同济大学 计算机科学与技术系, 上海 200092;郑州轻工业学院 软件学院, 河南 郑州 450002;国家高性能计算机工程技术中心同济分中心, 上海 200092;同济大学 计算机科学与技术系, 上海 200092;国家高性能计算机工程技术中心同济分中心, 上海 200092;南通大学 计算机科学与技术学院, 江苏 南通 226019;宁波大学 计算机科学与技术系, 浙江 宁波 315211)
On-Demand Physical Resource Allocation Method for Cloud Virtual Machine to Support Random Service Requests
CAO Jie,ZENG Guo-Sun,KUANG Gui-Juan,ZHANG Jian-Wei,MA Hai-Ying,HU Ke-Kun,NIU Jun
(Department of Computer Science and Technology, Tongji University, Shanghai 200092, China;Software College, Zhengzhou Institute of Light Industry, Zhengzhou 450002, China;Tongji Branch, National Engineering and Technology Center of High Performance Computer, Shanghai 200092, China;Department of Computer Science and Technology, Tongji University, Shanghai 200092, China;Tongji Branch, National Engineering and Technology Center of High Performance Computer, Shanghai 200092, China;School of Computer Science and Technology, Nantong University, Nantong 226019, China;Department of Computer Science and Technology, Ningbo University, Ningbo 315211, China)
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Received:September 29, 2014    Revised:December 22, 2015
> 中文摘要: 针对云平台按负载峰值需求配置处理机资源、提供单一的服务应用和资源需求动态变化导致资源利用率低下的问题,采用云虚拟机中心来同时提供多种服务应用.利用灰色波形预测算法对未来时间段内到达虚拟机的服务请求量进行预测,给出兼顾资源需求和服务优先等级的虚拟机服务效用函数,以最大化物理机的服务效用值为目标,为物理机内的各虚拟机动态配置物理资源.通过同类虚拟机间的全局负载均衡和多次物理机内各虚拟机的物理资源再分配,进一步增加服务请求量较大的相应类型的虚拟机的物理资源分配量.最后,给出了虚拟机中心基于灰色波形预测的按需资源分配算法ODRGWF.模拟实验结果表明,该算法能够有效地提高云平台中处理机的资源利用率,对提高用户请求完成率以及服务质量都具有实际意义.
Abstract:Low resource utilization is becoming much more serious in cloud platform which allocates processor resources according to the peak load while providing single service application and facing dynamic variation of resource demand. To address the problem, this study uses cloud virtual machine (VM) center to provide a variety of reasonable service applications simultaneously. Gray wave forecasting algorithm is adopted to predict the future load of service requests and a VM service utility function is proposed by taking resource requirements and service priorities into account. Each VM inside a physical machine dynamically configures physical resources to maximize the service utility value of the physical machine. Besides, by applying the global load balancing and multi-time physical resource redistribution for each virtual machine in the same physical machine, the number of physical resources assigned to the VMs whose service request amount is much larger is further increased. In the end, on-demand resource reconfiguration algorithm ODRGWF based on grey wave forecasting is put forward. The simulation results show that the proposed algorithm can effectively improve processor resource utilization, which is of practical significance to improve user request completion rate and service quality.
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基金项目:国家高技术研究发展计划(863)(2009AA012201);国家自然科学基金(61402244);上海市优秀学科带头人计划(10X D1404400);华为创新研究计划(IRP-2013-12-03);高效能服务器和存储技术国家重点实验室开放基金(2014 HSSA10);河南省科技创新人才计划([2015]4);浙江省公益技术应用研究项目(2014C31059) 国家高技术研究发展计划(863)(2009AA012201);国家自然科学基金(61402244);上海市优秀学科带头人计划(10X D1404400);华为创新研究计划(IRP-2013-12-03);高效能服务器和存储技术国家重点实验室开放基金(2014 HSSA10);河南省科技创新人才计划([2015]4);浙江省公益技术应用研究项目(2014C31059)
Foundation items:National High-Tech R&D Program of China (2009AA012201); National Natural Science Foundation of China (61402244); Program of Shanghai Subject Chief Scientist (10XD1404400); Huawei Innovation Research Project (IRP-2013-12-03); Open Foundation of the State Key Laboratory of High-End Server and Storage Technology (2014HSSA10); He’nan Scientific and Technological Innovation Project ([2015]4); Zhejiang Provincial Public Technology Application Research Project (2014C31059)
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曹洁,曾国荪,匡桂娟,张建伟,马海英,胡克坤,钮俊.支持随机服务请求的云虚拟机按需物理资源分配方法.软件学报,2017,28(2):457-472

CAO Jie,ZENG Guo-Sun,KUANG Gui-Juan,ZHANG Jian-Wei,MA Hai-Ying,HU Ke-Kun,NIU Jun.On-Demand Physical Resource Allocation Method for Cloud Virtual Machine to Support Random Service Requests.Journal of Software,2017,28(2):457-472