云计算环境下虚拟机资源调度策略研究
|
摘要
云计算概念从网格计算、分布式计算和并行计算概念发展而来，是一种新型的计算模式。它改变了传统的服务模式，通过与互联网结合提供了一种新型的商业模式。当前云计算环境中的资源主要是虚拟机资源，具体是通过使用虚拟化技术对数据中心的各种硬件资源进行虚拟化，形成虚拟资源池后，动态部署虚拟机提供给用户透明使用。用户只知道任务在云中执行，但并不知道其具体的执行位置。资源调度与作业调度是云计算的两个关键技术，云计算具有的商业性质因为关注了用户QoS，其使用的虚拟化技术使得作业调度和资源调度与以往的并行分布式计算不同。
随着数据中心规模的不断扩大以及用户数量的持续增加，如何使云中的虚拟机资源能够被高效地利用以及使用户任务不必等待更长的时间，被快速有效地完成成为云计算资源调度的重要问题。因此研究云计算的虚拟机资源调度问题对于研究云资源管理具有重要意义。云计算虚拟机资源调度主要研究如何将云计算虚拟池中的虚拟机资源分配给用户任务使用，以保证能够满足用户QoS要求，从而达到提高云计算资源使用率，减少计算时间，不违反服务等级协议（Service LevelAgreement，SLA）的目的。
本文主要研究云计算环境下虚拟机资源调度策略，在总结前人工作的基础上，本文所做的主要工作和创新包括以下几点：
1.本文分析了当前云计算以及云计算环境下的虚拟机资源调度研究现状，系统地阐述了云计算的概念、分类、应用场景、关键技术、Map/Reduce分布式处理框架等云计算技术。通过仔细分析云计算资源特征，总结了云计算的虚拟机资源调度模型、虚拟机资源调度算法目标、特点以及传统的调度算法等。并介绍了一种云数据中心资源调度模拟系统CloudSim模拟器。
2.分析了Map/Reduce框架，在此基础上提出了基于蚁群优化算法的虚拟机资源调度算法（包括基于改进蚂蚁系统的虚拟机资源调度算法以及基于改进蚁群算法的虚拟机资源调度算法）。针对当前蚁群算法中蚂蚁只依靠信息素交流，蚂蚁之间缺少直接交流的特点，在基于改进蚁群算法的虚拟机资源调度算法中引入了蚂蚁相遇机制，利用蚂蚁之间的直接交流促进云计算环境中适合执行任务的虚拟机资源的快速发现。模拟实验表明基于改进蚁群算法的虚拟机资源调度算法与基于蚂蚁系统的虚拟机资源调度算法相比能够更快地寻找到适合任务执行的虚拟机资源，以保证用户服务等级协议。
3.分析了传统的基于信任驱动的TD Max-min算法、TD Min-min算法以及虚拟机节能调度算法。针对当前节能机制和信任驱动的资源调度机制相分离的特点，提出了一种节能及信任驱动的虚拟机资源调度算法。该算法不仅考虑了任务对于虚拟机资源的信任需求，还考虑了数据中心对节能的要求。该算法利用任务和虚拟机资源之间的信任机制对任务和虚拟机资源进行匹配，并利用虚拟机初始化算法对虚拟机部署，以及利用最小化迁移算法对虚拟机进行实时迁移，以保证用户任务的性能以及数据中心的节能。模拟实验表明了该算法与TD Max-min算法、TD Min-min算法相比不仅能够获得好的总信任效益值以及平均信任效益值，还能够获得较低的服务等级协议违反率以及较低的电能消耗。
The concept of cloud computing comes from the concept of gird computing, distributedcomputing and parallel computing. It changes the traditional serve mode and provides a newbusiness mode by combining with the Internet. Currently the resources of the cloud computingare the virtual machines. Cloud computing virtualizes different hardware resources in the datacenter by virtualization technology. When the hardware resources forms the virtual resource pool,the virtual machines are deployed for being used transparently. The users only know that thetasks are carried out in the cloud, but they don’t know exactly which the tasks are carried out at.Job scheduling and resource scheduling are two key technologies in the cloud computing. Ascloud computing’s commercial characteristic makes it focus on user’s Quality of Service,virtualization technology of the cloud computing makes job scheduling and resource schedulingsignificantly different from the parallel and the distributed computing.
With the expansion of the scale of data center and the increase of number of users, makingthe virtual machines in the cloud be used efficiently and making tasks not wait for long time forbeing finished rapidly become the important issues of cloud resource scheduling. Researchingthe scheduling of the virtual machines is significant for researching cloud resources management.The issues of scheduling of virtual machines in the cloud mainly researches how to allocate thevirtual machine to user’s tasks for meeting the user’s request of QoS, which improves cloudresources utilization rate, reduces computing time and assures the Service Level Agreement.
This paper has done a research in the scheduling of virtual machines in the cloud computing.Based on previous researches, major research works and innovative points in this paper are:
1. This paper analyzes the current status of cloud computing and the scheduling of virtualmachines in the cloud computing. It also systematically analyzes the conception, classification,application scenarios, major technologies and Map/Reduce distributed frame of cloud computing.With the careful analysis of the characteristic of cloud resources, author summarizes the model,the goal, characteristic, traditional scheduling algorithms of virtual machine scheduling. It alsointroduces a simulator called CloudSim, which is applied to the resource scheduling of clouddata center.
2. This paper analyses the Map/Reduce frame carefully. Based on the Map/Reduce frame,author proposes virtual machine scheduling algorithm based on ant colony optimization (virtualmachine scheduling algorithm based on improved ant system and virtual machine schedulingalgorithm based on improved ant algorithm). In most current ant algorithms ants don’t communicate with each other directly, only rely on the pheromone exchange. The proposedvirtual machine scheduling algorithm based on improved ant algorithm brings in ant-meetmechanism, which can be used for finding the virtual machines in the cloud quickly through thedirect communication between the ants. Simulation results demonstrate the proposed virtualmachine scheduling algorithm based on improved ant algorithm can find the virtual machinesfaster than the proposed virtual machine scheduling algorithm based on improved ant system andassures Service Level Agreement.
3. This paper analyses the traditional trust-driven TD Max-Min algorithm, trust-driven TDMin-Min algorithm and energy-aware virtual machine scheduling algorithm. At present existingscheduling algorithms of trust-driven ignore energy requirements. An energy-aware andtrust-driven virtual machine scheduling algorithm is proposed. The proposed algorithm meets therequests of energy-aware in data centers while ensures the user task performance. The proposedalgorithm maps virtual machine and tasks by trust-driven mechanism between task and virtualmachine, deploys the virtual machine by initialization algorithm of virtual machine and migratesthe virtual machines by minimize migration algorithm. Simulation results demonstrate theproposed algorithm outperforms the trust-driven TD Max-Min and TD Min-Min algorithms interms of the total trust utility, average trust utility and the level of Service Level Agreementviolation while consumes less energy.
随着数据中心规模的不断扩大以及用户数量的持续增加，如何使云中的虚拟机资源能够被高效地利用以及使用户任务不必等待更长的时间，被快速有效地完成成为云计算资源调度的重要问题。因此研究云计算的虚拟机资源调度问题对于研究云资源管理具有重要意义。云计算虚拟机资源调度主要研究如何将云计算虚拟池中的虚拟机资源分配给用户任务使用，以保证能够满足用户QoS要求，从而达到提高云计算资源使用率，减少计算时间，不违反服务等级协议（Service LevelAgreement，SLA）的目的。
本文主要研究云计算环境下虚拟机资源调度策略，在总结前人工作的基础上，本文所做的主要工作和创新包括以下几点：
1.本文分析了当前云计算以及云计算环境下的虚拟机资源调度研究现状，系统地阐述了云计算的概念、分类、应用场景、关键技术、Map/Reduce分布式处理框架等云计算技术。通过仔细分析云计算资源特征，总结了云计算的虚拟机资源调度模型、虚拟机资源调度算法目标、特点以及传统的调度算法等。并介绍了一种云数据中心资源调度模拟系统CloudSim模拟器。
2.分析了Map/Reduce框架，在此基础上提出了基于蚁群优化算法的虚拟机资源调度算法（包括基于改进蚂蚁系统的虚拟机资源调度算法以及基于改进蚁群算法的虚拟机资源调度算法）。针对当前蚁群算法中蚂蚁只依靠信息素交流，蚂蚁之间缺少直接交流的特点，在基于改进蚁群算法的虚拟机资源调度算法中引入了蚂蚁相遇机制，利用蚂蚁之间的直接交流促进云计算环境中适合执行任务的虚拟机资源的快速发现。模拟实验表明基于改进蚁群算法的虚拟机资源调度算法与基于蚂蚁系统的虚拟机资源调度算法相比能够更快地寻找到适合任务执行的虚拟机资源，以保证用户服务等级协议。
3.分析了传统的基于信任驱动的TD Max-min算法、TD Min-min算法以及虚拟机节能调度算法。针对当前节能机制和信任驱动的资源调度机制相分离的特点，提出了一种节能及信任驱动的虚拟机资源调度算法。该算法不仅考虑了任务对于虚拟机资源的信任需求，还考虑了数据中心对节能的要求。该算法利用任务和虚拟机资源之间的信任机制对任务和虚拟机资源进行匹配，并利用虚拟机初始化算法对虚拟机部署，以及利用最小化迁移算法对虚拟机进行实时迁移，以保证用户任务的性能以及数据中心的节能。模拟实验表明了该算法与TD Max-min算法、TD Min-min算法相比不仅能够获得好的总信任效益值以及平均信任效益值，还能够获得较低的服务等级协议违反率以及较低的电能消耗。
The concept of cloud computing comes from the concept of gird computing, distributedcomputing and parallel computing. It changes the traditional serve mode and provides a newbusiness mode by combining with the Internet. Currently the resources of the cloud computingare the virtual machines. Cloud computing virtualizes different hardware resources in the datacenter by virtualization technology. When the hardware resources forms the virtual resource pool,the virtual machines are deployed for being used transparently. The users only know that thetasks are carried out in the cloud, but they don’t know exactly which the tasks are carried out at.Job scheduling and resource scheduling are two key technologies in the cloud computing. Ascloud computing’s commercial characteristic makes it focus on user’s Quality of Service,virtualization technology of the cloud computing makes job scheduling and resource schedulingsignificantly different from the parallel and the distributed computing.
With the expansion of the scale of data center and the increase of number of users, makingthe virtual machines in the cloud be used efficiently and making tasks not wait for long time forbeing finished rapidly become the important issues of cloud resource scheduling. Researchingthe scheduling of the virtual machines is significant for researching cloud resources management.The issues of scheduling of virtual machines in the cloud mainly researches how to allocate thevirtual machine to user’s tasks for meeting the user’s request of QoS, which improves cloudresources utilization rate, reduces computing time and assures the Service Level Agreement.
This paper has done a research in the scheduling of virtual machines in the cloud computing.Based on previous researches, major research works and innovative points in this paper are:
1. This paper analyzes the current status of cloud computing and the scheduling of virtualmachines in the cloud computing. It also systematically analyzes the conception, classification,application scenarios, major technologies and Map/Reduce distributed frame of cloud computing.With the careful analysis of the characteristic of cloud resources, author summarizes the model,the goal, characteristic, traditional scheduling algorithms of virtual machine scheduling. It alsointroduces a simulator called CloudSim, which is applied to the resource scheduling of clouddata center.
2. This paper analyses the Map/Reduce frame carefully. Based on the Map/Reduce frame,author proposes virtual machine scheduling algorithm based on ant colony optimization (virtualmachine scheduling algorithm based on improved ant system and virtual machine schedulingalgorithm based on improved ant algorithm). In most current ant algorithms ants don’t communicate with each other directly, only rely on the pheromone exchange. The proposedvirtual machine scheduling algorithm based on improved ant algorithm brings in ant-meetmechanism, which can be used for finding the virtual machines in the cloud quickly through thedirect communication between the ants. Simulation results demonstrate the proposed virtualmachine scheduling algorithm based on improved ant algorithm can find the virtual machinesfaster than the proposed virtual machine scheduling algorithm based on improved ant system andassures Service Level Agreement.
3. This paper analyses the traditional trust-driven TD Max-Min algorithm, trust-driven TDMin-Min algorithm and energy-aware virtual machine scheduling algorithm. At present existingscheduling algorithms of trust-driven ignore energy requirements. An energy-aware andtrust-driven virtual machine scheduling algorithm is proposed. The proposed algorithm meets therequests of energy-aware in data centers while ensures the user task performance. The proposedalgorithm maps virtual machine and tasks by trust-driven mechanism between task and virtualmachine, deploys the virtual machine by initialization algorithm of virtual machine and migratesthe virtual machines by minimize migration algorithm. Simulation results demonstrate theproposed algorithm outperforms the trust-driven TD Max-Min and TD Min-Min algorithms interms of the total trust utility, average trust utility and the level of Service Level Agreementviolation while consumes less energy.
引文
[1]都志辉，陈渝，刘鹏.网格计算[M].北京:清华大学出版社，2002.
[2]赵东.网格计算环境中若干关键技术的研究[D].电子科技大学硕士学位论文，2003.
[3]陈康，郑纬民.云计算:系统实例与研究现状[J].软件学报，2009，20(5):1337-1348.
[4] Zhong M, Shen K, Seiferas J. Replication Degree customization for high availability[J]. Operating SystemReview, 42(4), 2008:55-68.
[5] Amazon elastic compute cloud[EB/OL].http://aws.amazon.com/ec2/.
[6] Google App Engine[EB/OL]. http://en.wikipedia.org/wiki/Google_App_Engine.
[7] Apache Hadoop[EB/OL]. http://en.wikipedia.org/wiki/Apache_Hadoop.
[8] Azure Services Platform[EB/OL]. http://en.wikipedia.org/wiki/Azure_Services_Platform.
[9] Scaling Hadoop to 4000 nodes at Yahod[EB/OL]. http://developer.yahoo.net/blogs/hadoop/2008/09/scaling_hadoop _to_4000 _n odes_a.html.
[10]成都云计算中心建成运行创国内商业超级计算之首[EB/OL].http://www.enet.com.cn/article/2009/1229/A200912292561.shtml.
[11]赵春燕.云环境下作业调度算法研究与实现[D].北京交通大学硕士学位论文，2009.
[12]李建峰，彭舰.云计算环境下基于遗传算法的任务调度策略[J].计算机应用，2011，31(1)，184-186.
[13]吕良干.云计算环境下资源负载均衡调度算法研究[D].新疆大学硕士学位论文，2010.
[14]宋坤芳.基于蚁群算法的云计算资源调度策略研究[D].武汉纺织大学硕士学位论文，2011.
[15] Kusic D, Kephart J O, Hanson J E, et al. Power and performance management of virtualized computingenvironments via lookahead control[J]. Cluster Computing, 2009, 12(1): 1-15.
[16] Truong Vinh Truong Duy, Yukinori SATO, Yasushi INOGUCHI. A Prediction-Based Green Scheduler forDatacenters in Clouds[J]. IEICE Transaction Information and Systems, E94-D(9):1731-1741.
[17] Beloglazov A, Buyya R. Energy Efficient Resource Management in Virtualized Cloud Data Centers[C].Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing,Melbourne, Australia, 2010: 826- 831.
[18] Beloglazov A, Buyya R. Energy Efficient Allocation of Virtual Machines in Cloud Data Centers[C].Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing,2010:577- 578.
[19] Beloglazov A, Abawajy J, Buyya R. Energy-Aware Resource Allocation Heuristics for EfficientManagement of Data Centers for Cloud Computing[J]. Future Generation Computer Systems, 2012,28(5):755-768.
[20] Beloglzov A, Buyya R. Adaptive Threshold-Based Approach for Energy-Efficient Consolidation ofVirtual Machines in Cloud Data Centers[C]. Proceedings of the 8th International Workshop on Middleware forGrids, Clouds and e-Science, New York, USA, 2010.
[21] Michael A, Fox A, Griffith R, et al. Above the clouds a berkley view of cloud computing[R]. UC Berkley,USA: Technical Report No.UCB/EEECS-2009-28,2009:1-25.
[22] IBM Cloud Computing[EB/OL]. http://www.ibm.com/cloud.
[23] Foster I, Yong Zhao, Raciu I,et al.Cloud Computing and Grid Computing 360-Degree Compared[C].GridComputing Environments Workshop,2008:1-10.
[24] Hewitt C. ORGs for Scalable, Robust, Privacy-Friendly client cloud computing[J]. Internet Computing,2008,12(5):96-99.
[25] Buyya R, Yeo C S, Venugopal S. Market-oriented cloud computing vision, hype, and reality for deliveringIT services as computing utilities[C]. Proc of the 10th IEEE International Conference on High PerformanceComputing and Communications, 2008:5-13.
[26] Qiu Yong gang, Frei H P. Concept based query expansion[C].Proceedings of the 16th ACM SIGIRConference on Research and Development in Information Retrieval,Pittsburgh, PA,USA,1993:160-169.
[27]余伟.基于云计算的分布式搜索技术研究[D].武汉科技大学硕士学位论文，2011.
[28]葛新.基于云计算集群扩展中的调度问题研究[D].中国科学技术大学硕士学位论文，2011.
[29] David S. Linthicum,马国耀译.云计算与SOA[M].北京:人民邮电出版社，2011.
[30] Ghemawat S, Gobioff H, Leung S H. The Google file system[C]. Proceedings of the 19th ACMsymposium on Operating systems principles, New York, 2003:29-43.
[31] Borthakur D. The Hadoop Distributed File System: Architecture and Design[EB/OL]. http://hadoop.apache.org/core/docs/current/hdfs_design.pdf.
[32] Daniel N, Rich W, Chris G,et al. Garcia-Luna-Aceves J. The eucalyptus open-source cloud-computingsystem[C]. Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and theGrid, 2009:124-131.
[33] Vaquero L M, Rodero-Merino L, Caceres J,et al. A Break in the Clouds: Towards a CloudDefinition[J].ACM SIGCOMM Computer Communication Review, 2009, 39(1):50-55.
[34] Dean J, Ghemawat S. MapReduce: Simplied Data Processing on Large Cluster[J]. Communications of theACM, 2008, 51(1):107-113.
[35] Lammel R. Google's MapReduce programming model-revisited[J]. Science of Computer Programming,2008, 70(1):1-30.
[36]李成华，张新访，金海，等.MapReduce:新型的分布式并行计算编程模型[J].计算机工程与科学，2011，33(3):129-135.
[37] Calheiros R N, Ranjan R,Buyya R, et al. CloudSim: a toolkit for modeling and simulation of cloudcomputing environments and evaluation of resource provisioning algorithms[J]. Software: Practice andExperience, 41(1):23-50.
[38]罗红，穆德俊，邓智群，等.网格计算中任务资源研究综述[J].计算机应用研究，2005，5(1):16-19.
[39] Sabata B,Chatterjee S,Davis M,et al. Taxonomy for QoS specifications[C]. Proceedings of the 3rdInternational Workshop on Object-Oriented Real-Time Dependable Systems, 1997:100~107.
[40]田文洪，赵勇.云计算资源调度管理[M].北京:国防工业出版社，2011.
[41]孙伟龙.基于IaaS云计算的Web应用技术研究[D].南京理工大学硕士学位论文，2011.
[42]刘鹏程.云计算中虚拟机动态迁移的研究[D].复旦大学硕士学位论文，2009.
[43]沈舒.云计算与网格计算的比较[J].软件导航，2009，8(12):10-11.
[44]陈海勇.网格作业调度关键技术研究[D].解放军信息工程大学博士学位论文，2009.
[45] Dorigo M, Gambardella L M. Ant colony system: A cooperative learning approach to the travelingsalesman problem[J]. IEEE Transactions on Evolutionary Computation,1997,1(1):53-66.
[46] Wickremasinghe B, Calheiros R N, Buyya R. CloudAnalyst: A CloudSim-Based Visual Modeller forAnalysing Cloud Computing Environments and Applications[C]. Proceedings of the 24th IEEE InternationalConference on Advanced Information Networking and Applications, 2010:446- 452.
[47]田文洪，赵勇.云计算——资源调度管理[M].北京:国防工业出版社，2011.
[48]杨沛，古德祥.蚁群的信息系统[J].昆虫知识，2000，38(1):23-25.
[49] Dorigo M, Gambardella L M. Ant Colonies for the Traveling Salesman Problem[J]. BioSystems, 1997,43(2):73-81.
[50] Dorigo M, Caro G D, Gambardella L M. Ant Algorithms for Discrete Optimization[J]. Artificial Life,1999, 5(2):137-172.
[51]潘达儒，袁艳波.一种基于AntNet改进的路由算法[J].小型微型计算机统，2006，27(7):1169-1174.
[52] Sebasian H, Diana M. Analysis of dynamic voltage/frequency scaling in chip-multiprocessors[C].Proceedings of the 2007 international symposium on low power electronics and design, 2007:27-29.
[53] Andrew L L, Lin M, Wierman A. Optimality, fairness, and robustness in speed scaling Designs[C].Proceedings of ACM International Conference on Measurement and Modeling of International ComputerSystems, 2010:37-38.
[54] Heller B, Seetharaman S, Mahadevan P. ElasticTree: Saving Energy in Data Center Networks[C].Proceedings of the 7th USENIX conference on Networked systems design and implementation, 2010:17-17.
[55] Mahmoodi T. Energy-Aware Routing in the Congitive Packet Network[J]. Performance Evaluation, 2011,68(4):338-346.
[56] Gelenbe E, Morfopoulou C. Routing and G-Networks to Optimise Energy and Quality of Service inPacket Networks[J]. Lecture Notes of the Institute for Computing Sciences, Social Informatics andTelecommunications Engineering, 2011, 54(4): 163-173.
[57] Brownetal R. Report to congress on server and data center energy efficienc Public law109-431,LBNL-363E [R]. Washington, D.C:U.S.Environmental Protection Agency,2007.
[58] Buyya R, Yeo C S, Venugopal S. Market-oriented cloud computing: Vision, hype, and reality fordelivering it services as computing utilities[C]. Proceedings of the 10th IEEE internal conference on HighPerformance Computing and Communication, 2008:5-13.
[59]张伟哲，刘欣，云晓春，等.信任驱动的网格作业调度算法[J].通信学报，2006，27(2):73-79.
[60]李冉，于炯，侯勇.信任驱动的网格调度算法[J].计算机工程与应用，2009，45(23):118-122.
[61] Tao F, Hu Y F, Zhou Z D. Application and modeling of resource service trust-QoS evaluation inmanufacturing grid system[J]. International journal of production research: 2009,47(6): 1521-1550.
[62] Tian J F, Yuan P, Lu Y Z. Security for Resource Allocation Based on Trust and Reputation inComputational Economy Model for Gird[C]. Proceedings of 2009 Fourth International Conference on Frontierof Computer Science and Technology, 2009:339-345.
[63] Kolodziej J, Xhafa F. Meeting Security and user behavior requirements in Gird scheduling[J]. SimulationModelling Practice and Theory: 2011,19(1):213-226.
[64]黄德才，张丽君，郑月峰，等.TD_Max-min:信任驱动的网格任务调度新算法[J].计算机工程，2007，33(24):80-82.
[65] AliS, Siegel H J, Maheswaran M. Task execution time modeling for heterogeneous computing Systems[C].Proceedings of the 9th Heterogeneous Computing Workshop, 2000:185-199.
[2]赵东.网格计算环境中若干关键技术的研究[D].电子科技大学硕士学位论文，2003.
[3]陈康，郑纬民.云计算:系统实例与研究现状[J].软件学报，2009，20(5):1337-1348.
[4] Zhong M, Shen K, Seiferas J. Replication Degree customization for high availability[J]. Operating SystemReview, 42(4), 2008:55-68.
[5] Amazon elastic compute cloud[EB/OL].http://aws.amazon.com/ec2/.
[6] Google App Engine[EB/OL]. http://en.wikipedia.org/wiki/Google_App_Engine.
[7] Apache Hadoop[EB/OL]. http://en.wikipedia.org/wiki/Apache_Hadoop.
[8] Azure Services Platform[EB/OL]. http://en.wikipedia.org/wiki/Azure_Services_Platform.
[9] Scaling Hadoop to 4000 nodes at Yahod[EB/OL]. http://developer.yahoo.net/blogs/hadoop/2008/09/scaling_hadoop _to_4000 _n odes_a.html.
[10]成都云计算中心建成运行创国内商业超级计算之首[EB/OL].http://www.enet.com.cn/article/2009/1229/A200912292561.shtml.
[11]赵春燕.云环境下作业调度算法研究与实现[D].北京交通大学硕士学位论文，2009.
[12]李建峰，彭舰.云计算环境下基于遗传算法的任务调度策略[J].计算机应用，2011，31(1)，184-186.
[13]吕良干.云计算环境下资源负载均衡调度算法研究[D].新疆大学硕士学位论文，2010.
[14]宋坤芳.基于蚁群算法的云计算资源调度策略研究[D].武汉纺织大学硕士学位论文，2011.
[15] Kusic D, Kephart J O, Hanson J E, et al. Power and performance management of virtualized computingenvironments via lookahead control[J]. Cluster Computing, 2009, 12(1): 1-15.
[16] Truong Vinh Truong Duy, Yukinori SATO, Yasushi INOGUCHI. A Prediction-Based Green Scheduler forDatacenters in Clouds[J]. IEICE Transaction Information and Systems, E94-D(9):1731-1741.
[17] Beloglazov A, Buyya R. Energy Efficient Resource Management in Virtualized Cloud Data Centers[C].Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing,Melbourne, Australia, 2010: 826- 831.
[18] Beloglazov A, Buyya R. Energy Efficient Allocation of Virtual Machines in Cloud Data Centers[C].Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing,2010:577- 578.
[19] Beloglazov A, Abawajy J, Buyya R. Energy-Aware Resource Allocation Heuristics for EfficientManagement of Data Centers for Cloud Computing[J]. Future Generation Computer Systems, 2012,28(5):755-768.
[20] Beloglzov A, Buyya R. Adaptive Threshold-Based Approach for Energy-Efficient Consolidation ofVirtual Machines in Cloud Data Centers[C]. Proceedings of the 8th International Workshop on Middleware forGrids, Clouds and e-Science, New York, USA, 2010.
[21] Michael A, Fox A, Griffith R, et al. Above the clouds a berkley view of cloud computing[R]. UC Berkley,USA: Technical Report No.UCB/EEECS-2009-28,2009:1-25.
[22] IBM Cloud Computing[EB/OL]. http://www.ibm.com/cloud.
[23] Foster I, Yong Zhao, Raciu I,et al.Cloud Computing and Grid Computing 360-Degree Compared[C].GridComputing Environments Workshop,2008:1-10.
[24] Hewitt C. ORGs for Scalable, Robust, Privacy-Friendly client cloud computing[J]. Internet Computing,2008,12(5):96-99.
[25] Buyya R, Yeo C S, Venugopal S. Market-oriented cloud computing vision, hype, and reality for deliveringIT services as computing utilities[C]. Proc of the 10th IEEE International Conference on High PerformanceComputing and Communications, 2008:5-13.
[26] Qiu Yong gang, Frei H P. Concept based query expansion[C].Proceedings of the 16th ACM SIGIRConference on Research and Development in Information Retrieval,Pittsburgh, PA,USA,1993:160-169.
[27]余伟.基于云计算的分布式搜索技术研究[D].武汉科技大学硕士学位论文，2011.
[28]葛新.基于云计算集群扩展中的调度问题研究[D].中国科学技术大学硕士学位论文，2011.
[29] David S. Linthicum,马国耀译.云计算与SOA[M].北京:人民邮电出版社，2011.
[30] Ghemawat S, Gobioff H, Leung S H. The Google file system[C]. Proceedings of the 19th ACMsymposium on Operating systems principles, New York, 2003:29-43.
[31] Borthakur D. The Hadoop Distributed File System: Architecture and Design[EB/OL]. http://hadoop.apache.org/core/docs/current/hdfs_design.pdf.
[32] Daniel N, Rich W, Chris G,et al. Garcia-Luna-Aceves J. The eucalyptus open-source cloud-computingsystem[C]. Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and theGrid, 2009:124-131.
[33] Vaquero L M, Rodero-Merino L, Caceres J,et al. A Break in the Clouds: Towards a CloudDefinition[J].ACM SIGCOMM Computer Communication Review, 2009, 39(1):50-55.
[34] Dean J, Ghemawat S. MapReduce: Simplied Data Processing on Large Cluster[J]. Communications of theACM, 2008, 51(1):107-113.
[35] Lammel R. Google's MapReduce programming model-revisited[J]. Science of Computer Programming,2008, 70(1):1-30.
[36]李成华，张新访，金海，等.MapReduce:新型的分布式并行计算编程模型[J].计算机工程与科学，2011，33(3):129-135.
[37] Calheiros R N, Ranjan R,Buyya R, et al. CloudSim: a toolkit for modeling and simulation of cloudcomputing environments and evaluation of resource provisioning algorithms[J]. Software: Practice andExperience, 41(1):23-50.
[38]罗红，穆德俊，邓智群，等.网格计算中任务资源研究综述[J].计算机应用研究，2005，5(1):16-19.
[39] Sabata B,Chatterjee S,Davis M,et al. Taxonomy for QoS specifications[C]. Proceedings of the 3rdInternational Workshop on Object-Oriented Real-Time Dependable Systems, 1997:100~107.
[40]田文洪，赵勇.云计算资源调度管理[M].北京:国防工业出版社，2011.
[41]孙伟龙.基于IaaS云计算的Web应用技术研究[D].南京理工大学硕士学位论文，2011.
[42]刘鹏程.云计算中虚拟机动态迁移的研究[D].复旦大学硕士学位论文，2009.
[43]沈舒.云计算与网格计算的比较[J].软件导航，2009，8(12):10-11.
[44]陈海勇.网格作业调度关键技术研究[D].解放军信息工程大学博士学位论文，2009.
[45] Dorigo M, Gambardella L M. Ant colony system: A cooperative learning approach to the travelingsalesman problem[J]. IEEE Transactions on Evolutionary Computation,1997,1(1):53-66.
[46] Wickremasinghe B, Calheiros R N, Buyya R. CloudAnalyst: A CloudSim-Based Visual Modeller forAnalysing Cloud Computing Environments and Applications[C]. Proceedings of the 24th IEEE InternationalConference on Advanced Information Networking and Applications, 2010:446- 452.
[47]田文洪，赵勇.云计算——资源调度管理[M].北京:国防工业出版社，2011.
[48]杨沛，古德祥.蚁群的信息系统[J].昆虫知识，2000，38(1):23-25.
[49] Dorigo M, Gambardella L M. Ant Colonies for the Traveling Salesman Problem[J]. BioSystems, 1997,43(2):73-81.
[50] Dorigo M, Caro G D, Gambardella L M. Ant Algorithms for Discrete Optimization[J]. Artificial Life,1999, 5(2):137-172.
[51]潘达儒，袁艳波.一种基于AntNet改进的路由算法[J].小型微型计算机统，2006，27(7):1169-1174.
[52] Sebasian H, Diana M. Analysis of dynamic voltage/frequency scaling in chip-multiprocessors[C].Proceedings of the 2007 international symposium on low power electronics and design, 2007:27-29.
[53] Andrew L L, Lin M, Wierman A. Optimality, fairness, and robustness in speed scaling Designs[C].Proceedings of ACM International Conference on Measurement and Modeling of International ComputerSystems, 2010:37-38.
[54] Heller B, Seetharaman S, Mahadevan P. ElasticTree: Saving Energy in Data Center Networks[C].Proceedings of the 7th USENIX conference on Networked systems design and implementation, 2010:17-17.
[55] Mahmoodi T. Energy-Aware Routing in the Congitive Packet Network[J]. Performance Evaluation, 2011,68(4):338-346.
[56] Gelenbe E, Morfopoulou C. Routing and G-Networks to Optimise Energy and Quality of Service inPacket Networks[J]. Lecture Notes of the Institute for Computing Sciences, Social Informatics andTelecommunications Engineering, 2011, 54(4): 163-173.
[57] Brownetal R. Report to congress on server and data center energy efficienc Public law109-431,LBNL-363E [R]. Washington, D.C:U.S.Environmental Protection Agency,2007.
[58] Buyya R, Yeo C S, Venugopal S. Market-oriented cloud computing: Vision, hype, and reality fordelivering it services as computing utilities[C]. Proceedings of the 10th IEEE internal conference on HighPerformance Computing and Communication, 2008:5-13.
[59]张伟哲，刘欣，云晓春，等.信任驱动的网格作业调度算法[J].通信学报，2006，27(2):73-79.
[60]李冉，于炯，侯勇.信任驱动的网格调度算法[J].计算机工程与应用，2009，45(23):118-122.
[61] Tao F, Hu Y F, Zhou Z D. Application and modeling of resource service trust-QoS evaluation inmanufacturing grid system[J]. International journal of production research: 2009,47(6): 1521-1550.
[62] Tian J F, Yuan P, Lu Y Z. Security for Resource Allocation Based on Trust and Reputation inComputational Economy Model for Gird[C]. Proceedings of 2009 Fourth International Conference on Frontierof Computer Science and Technology, 2009:339-345.
[63] Kolodziej J, Xhafa F. Meeting Security and user behavior requirements in Gird scheduling[J]. SimulationModelling Practice and Theory: 2011,19(1):213-226.
[64]黄德才，张丽君，郑月峰，等.TD_Max-min:信任驱动的网格任务调度新算法[J].计算机工程，2007，33(24):80-82.
[65] AliS, Siegel H J, Maheswaran M. Task execution time modeling for heterogeneous computing Systems[C].Proceedings of the 9th Heterogeneous Computing Workshop, 2000:185-199.