摘要
云计算系统具有服务器规模庞大和用户群体广泛的特点,系统需要频繁地对各种应用任务进行调度和管理。因此,如何对云计算资源进行合理分配和对大量应用任务进行高效调度管理,使得各类应用任务均能够获得一个较好的调度结果,并保证系统负载维持在一个相对均衡的状态,这已经成为云计算技术领域中的一个研究热点。由于云计算和传统分布式计算的任务调度问题具有一定的相似性,现有的云计算任务调度策略大都借鉴了传统分布式环境下的任务调度方法,或者在其基础上改进而来,本身具有一定的局限性;另外作为一种商业服务,云计算不但要考虑优化其任务调度策略来提高系统的服务能力,同时还应考虑云服务提供者的服务收益问题,目前虽然在该方面具有一些探讨,但尚无成熟的方法。因此,开展云计算的任务调度策略研究对于提高云计算系统的服务能力具有重要的理论价值和现实意义。
基于此,本文重点对云计算的任务调度策略进行了全面而系统的分析和研究,论文的内容主要包括云计算用户任务调度的QoS目标约束问题、云计算任务调度决策的执行效率问题以及云服务提供者的服务收益问题三个方面的研究。论文在对这些问题进行深入研究的基础上,给出了一种全新的云计算环境下的任务调度体系架构。
论文的主要研究工作和创新点体现在以下几个方面:
(一)针对云计算环境下对QoS目标约束要求各不相同的用户任务调度问题,提出了一种多QoS目标约束的云计算任务协同调度策略。云计算环境下,资源动态多变,用户偏好多样,用户任务的QoS目标约束条件通常会包含多个指标的要求,对于用户任务的QoS目标约束条件的满足程度在很大程度上决定了云计算任务调度策略的性能优劣。本文提出的多QoS目标约束的云计算任务协同调度策略针对大量用户任务各不相同的QoS目标约束要求,分别建立相应的QoS目标约束条件;然后应用构造的隶属度函数将多QoS的目标约束问题转换为单目标的约束求解问题。与传统方法相比,本文提出的方法在满足用户任务多QoS目标约束要求的前提下,能够有效降低用户任务调度的截止时间底线违背率,并能够减少其平均任务执行时间以及其平均任务执行成本。
(二)针对云计算环境中大量应用任务调度决策的执行效率问题,提出了一种基于遗传-蚁群算法的云计算任务调度策略。如何对云计算中的海量任务进行高效调度执行,使得各类任务的调度请求均能够在较短时间内获得一个较好的调度结果,是云计算领域的一个技术难点。本文提出的基于遗传-蚁群算法的云计算任务调度策略首先基于遗传算法进行初步的全局快速搜索,然后将得到的全局搜索信息转化为蚁群算法初始搜索时的信息素描述,最后基于蚁群算法实现精确求解。该方法综合了遗传算法全局搜索能力较好和蚁群算法求解精度较高的优势,并规避了遗传算法局部求解能力不足以及蚁群算法初始搜索阶段效率低下的缺陷;在大规模任务参与调度的情景下,该方法能够有效提高云计算任务调度决策的执行效率,并能够实现较好的系统负载均衡水平。
(三)为了提高云计算系统的服务收益,从云服务提供者的角度出发提出了一种服务成本驱动的云计算任务调度策略。现有的云计算任务调度策略往往从用户角度出发,以满足用户任务的资源请求和QoS目标约束要求为目标,对于云服务提供者的服务收益关注不足;作为一种商业服务,云计算系统应尽可能地提高其服务收益水平。在本文提出的服务成本驱动的云计算任务调度策略中,调度开支成为云用户在进行任务调度时所必须考虑的一个因素,在满足用户任务的资源请求与QoS目标约束要求的前提下,用户将优先选择价格低廉的计算资源,这样使得任务的调度决策更为理性和高效,同时计算资源的管理分配也更为公平和节约。该方法在一定程度上提高了云计算系统的资源利用率和云服务提供者的服务收益水平,最终可以促进云计算市场的健康可持续发展。
(四)在分析和研究了云计算任务调度问题所关注的各类性能指标的基础上,提出了一种云计算环境下的任务调度体系架构。现有关于云计算任务调度问题的研究往往基于某种假设,侧重于对约定的某一个或几个性能指标进行评估,测试结果具有主观性和片面性。本文提出的任务调度体系架构对各种功能模块进行了明确定义,同时对用户任务及处理单元的各种属性参数进行了详细描述。这样在对具体的任务调度策略进行测试和评估时,开发人员无需再进行条件假设,只需根据具体的测试要求和调度目标实例化相应的属性参数即可。与此同时,考虑到云计算系统故障发生较为频繁,故在提出的任务调度体系架构中引入了一种动态的数据副本管理策略,它不仅有效提高了云计算系统的可靠性和可用性,而且也提高了系统的数据文件访问效率和负载均衡水平。
Cloud computing system has a large number of servers and broad users, and it has toschedule and manage all kinds of application tasks frequently. Thus, so as to obtain a betterscheduling result in a relatively balanced state of system load, how to achieve reasonableallocation of computing resources and complete efficient scheduling execution of a largenumber of application tasks, has became a hot topic in the field of cloud computing. A certaindegree of similarity between the task scheduling strategies of cloud computing andconventional distributed computing system makes that the majority of the existing taskscheduling strategies of cloud computing are developed or improved on the basis of the taskscheduling methods of conventional distributed environment, which leads to some limitations.In addition, as a commercial service, so as to improve its service performance, cloudcomputing system should not only take the optimization of its task scheduling strategy intoconsideration, but also pay attention to the service revenue of cloud service providers. Atpresent, some discussions are appeared on this point, but there is no mature method. Therefore,it is valuable in theory and significant in practice to study on the task scheduling strategy ofcloud computing for improving the service performance of cloud computing system.
On this basis, this dissertation analyzes and studies the task scheduling strategies of cloudcomputing systemically and roundly. The main research content of this dissertation includesthrees aspects, which are the problem of the QoS objective constraint of application taskssubmitted by system users, the executing efficiency of task scheduling strategy and theservice revenue of cloud service providers in the cloud computing environment. After theprofound study on the above problems, a task scheduling architecture is given at the end ofthis dissertation in cloud computing environment.
The main research works and innovations of this dissertation are as follows:
(1) Focusing on the scheduling issue of a large number of application tasks with differentQoS objective constraint requirements in the cloud computing environment, a multi-QoSobjective constrained task scheduling strategy of cloud computing is proposed. Thecomputing resources are dynamical and variable in cloud computing environment, and thepreferences of users are also diversiform, and moreover, the QoS objective constraintrequirements of application tasks always involve more than one parameter. At the same time,the meeting degree to the QoS objective constraint requirements of application tasks affectsthe performance of cloud task scheduling strategy seriously. Focusing on the different QoSobjective constraint requirements of a large number of application tasks, the proposed method respectively constructs the corresponding QoS objective constraint condition, and then use aconstructed subjection degree function to transform the multi-QoS objective constrained issueinto a single objective constrained optimization issue. Compared with traditional methods, theproposed method in this dissertation achieves a better scheduling result in the measurementsof the violating ratio of deadline, the average scheduling makespan, and the average taskexecution cost, under the condition of satisfying the multi-QoS objective constraintrequirements of application tasks.
(2) Focusing on the scheduling efficiency of a large number of application tasks in cloudcomputing environment, a genetic-ant colony optimization algorithm based task schedulingstrategy of cloud computing is put forwarded. How to achieve efficient scheduling andexecution of numerous application tasks in cloud computing environment and obtain a betterscheduling result in a shorter makespan for every task, is a technical difficulty in the field ofcloud computing. A fast global search is executed at first based on genetic algorithm in theproposed task scheduling strategy, and then the global searching information obtained at theend of the initial search stage is transformed into the initialization of pheromone of ant colonyalgorithm. Finally, we get a satisfactory scheduling solution of application tasks based on theant colony optimization algorithm. The proposed method integrates the fast global searchcapability of genetic algorithm and the high solving precision of ant colony optimizationalgorithm, whereas, it avoids the deficient local search capability of genetic algorithm, andalso overcomes the inefficiency of ant colony optimization algorithm at its initial search stage.In a large-scale application task scheduling scene, the proposed method improves thescheduling efficiency of a large number of application tasks effectively as well as remains arelatively balanced system load in cloud computing environment.
(3) In order to improve the service revenue of cloud computing system, a servicecost-driven task scheduling strategy of cloud computing is proposed from the standpoint ofcloud service providers. The majority of the existing task scheduling strategies of cloudcomputing aim at meeting the resource requests and QoS objective constraint requirements ofapplication tasks from the view of system users, rather than paying more attention to theservice revenue of cloud service providers. As a kind of commercial service, cloud computingsystem should try its best to improve its service revenue. In the proposed service cost-drivencloud computing task scheduling strategy, the scheduling expenditure becomes an importantconsideration when cloud users send their scheduling requests of application tasks, and oncondition that the resource requests and QoS objective constraint requirements of applicationtasks are satisfied, the users will prefer to choose the cheaper computing resources rather than choose those high-powered ones, which makes the scheduling decision of application tasksmore reasonable and efficient, and the management and allocation of computing resourcesmore fair and economical. The proposed method improves the resource utility of cloudcomputing system and the service revenue of cloud service providers in a certain extent, andultimately promotes the healthy and sustainable development of cloud computing market.
(4) A task scheduling architecture is proposed in the cloud computing environment bytaking all kind of performance evaluation criterions of cloud task scheduling issue intoconsideration. The existing studies on the task scheduling issue of cloud computing are oftenbased on a certain assumption, and emphasize particularly on one or more given performanceevaluation criterions, which leads that the corresponding performance testing results are alitter subjective and one-sided. All kind of functional modules are definitely defined and allthe attribute parameters of both application tasks and processing elements are also describedin detail in the proposed task scheduling architecture of this dissertation, in which, thetechnical developer just instantiate the corresponding attribute parameters based on thespecific testing requirements and scheduling objectives, rather than making any conditionassumption when demonstrating and evaluating a given task scheduling strategy of cloudcomputing. In addition, a dynamic data replica management strategy is introduced in theproposed task scheduling architecture due to the frequent mistakes occurred in cloudcomputing system, which not only improves the reliability and availability of cloudcomputing system, but also improves its data file accessing efficiency and the balance level ofsystem load.
引文
[1] Cusumano M.. Cloud computing and SaaS as new computing platforms[J].Communications of the ACM,2010,53(4):27-29
[2] Manias E., Baude F.. A component-based middleware for hybrid grid/cloud computingplatforms[J]. Concurrency and computation: practice&experience,2012,24(13):1461-1477
[3] Armbrust M., Fox A., Griffith R., et al. A view of cloud computing[J]. Communicationsof the ACM,2010,53(4):50-58
[4] Luis M. V., Luis R. M., Juan C., et al. A break in the clouds: towards a clouddefinition[J]. ACM SIGCOMM computer communication review,2009,39(1):50-55
[5] Foster I., Zhao Y., Raicu L., et al. Cloud computing and grid computing360-degreecompared[A]. Proceedings of the2008grid computing environments workshop[C],2008,1-10
[6]刘鹏.云计算[M].第二版.北京:电子工业出版社.2011年
[7] Rosenthal A., Mork P., Li M. H., et al. Cloud computing: a new business paradigm forbiomedical information sharing[J]. Journal of biomedical informatics,2010,43(2):342-353
[8] Luo Yan. Network I/O virtualization for cloud computing[J]. IT professional,2010,12(5):36-41
[9] Ghosh A., Arce I.. In cloud computing we trust–but should we?[J] IEEE security&privacy,2010,8(6):14-16
[10] Brumec S., Vrcek N.. Cost effectiveness of commercial computing clouds[J].Information systems,2013,38(4):495-508
[11] Liu Ke, Jin Hai, Chen Jinjun, et al. A compromised-time-cost scheduling algorithm inswinDeW-C for instance-intensive cost-constrained workflows on a cloud computingplatform[J]. International journal of high performance computing applications,2010,24(4):445-456
[12] Fard H. M., Prodan R., Fahringer T.. A truthful dynamic workflow schedulingmechanism for commercial multicloud environments[J]. IEEE transactions on paralleland distributed systems,2013,24(6):1203-1212
[13] Su Sen, Li Jian, Huang Qingjia, et al. Cost-efficient task scheduling for executing largeprograms in the cloud[J]. Parallel computing,2013,39(4-5):177-188
[14] Wu Heng, Zhang Wenbo, Zhang Jiahua, et al. A benefit-aware on-demand provisioningapproach for multi-tier applications in cloud computing[J]. Frontiers of computerscience,2013,7(4):459-474
[15] Moreno-Vozmediano R., Montero R.S., Llorente L.M. Key challenges in cloudcomputing: enabling the future internet of services[J]. IEEE internet computing,2013,17(4):18-25
[16] Delimitrou C., Kozyrakis C.. QoS-aware scheduling in heterogeneous datacenters withparagon[J]. ACM transactions on computer systems,2013,31(4):12
[17] Wang Weijen, Chang Yueshan, Lo W., et al. Adaptive scheduling for parallel tasks withQoS satisfaction for hybrid cloud environments[J]. The journal of supercomputing,2013,66(2):783-811
[18] Ebejer J.P., Fulle S., Morris G. M., et al. The emerging role of cloud computing inmolecular modeling[J]. Journal of molecular graphics and modeling,2013,44:177-187
[19] Krishnappa D. K., Irwin D., Lyons E., et al. CloudCast: cloud computing for short-termweather forecasts[J]. Computing in science&engineering,2013,15(4):30-37
[20] Zhang Liangjie. Editorial: big services era: global trends of cloud computing and bigdata[J]. IEEE transactions on services computing,2012,5(4):467-468
[21] Khasnabish B., Huang Dijiang, Bai Xiaoying, et al. Cloud computing, networking, andservices[J]. Journal of network and systems management,2012,20(4):463-467
[22] Ghemawat S., Gobioff H., Leung S. T.. The google file system[A]. Proceedings of the19th ACM symposium on operating systems principles[C], New York: ACM press,2003:29-43
[23] Dean J., Ghemawat S.. MapReduce: simplified data processing on large clusters[J].Communications of the ACM,2008,51(1):107-113
[24] Burrows M.. The chubby lock service for loosely-coupled distributed systems[A].Proceedings of the7th USENIX symposium on operating systems design andimplementation[C]. Berkeley: USENIX Association,2006:335-350.
[25] Fay C., Jeffrey D., Sanjay G., et al. Bigtable: a distributed storage system for structureddata[A]. Proceedings of the7th USENIX symposium on operating systems design andimplementation[C]. Berkeley: USENIX Association,2006:205-218
[26] Karjoth G.. Access control with IBM Tivoli access manager[J]. ACM transactions oninformation and system security,2003,6(2):232-257
[27] Qi Xing, Estela B. B.. A cloud computing system in windows azure platform for dataanalysis of crystalline materials[J]. Concurrency and computation practice andexperience,2013,25(15):2157-2169
[28] Huang Qunying, Yang Chaowei, Liu Kai, et al. Evaluating open-source cloud computingsolutions for geosciences[J]. Computers&geosciences,2013,59:41-52
[29] Muth T., Peters J., Blackbum J., et al. ProteoCloud: a full-featured open sourceproteomics cloud computing pipeline[J]. Journal of proteomics,2013,88:104-108
[30] Avetisyan A. I., Campbell R., Gupta I., et al. Open cirrus: a global cloud computingtestbed[J]. Computer,2010,43(4):35-43
[31] Xiong N., Han W., Vandenberg A.. Green cloud computing schemes based on networks:a survey[J]. IET communications,2012,6(18):3294-3300
[32] Wang Hui. Privacy-preserving data sharing in cloud computing[J]. Journal of computingscience and technology,2010,25(3):401-414
[33] Geiger K.. Cloud computing in pharmaceutical R&D: business risks and mitigations[J].Current opinion in drug discovery&development,2010,13(3):279-285
[34] Pallis G.. Cloud computing: the new frontier of internet computing[J]. IEEE internetcomputing,2010,14(5):70-73
[35] Johan T., Montero R. S., Rafael M. V., ei al. Cloud brokering mechanisms for optimizedplacement of virtual machines across multiple providers[J]. Future generation computersystems,2012,28(2):358-367
[36] Li Bing, Song A. M., Song J.. A distributed QoS-constraint task scheduling scheme incloud computing environment: model and algorithm[J]. Advances in informationsciences and service sciences,2012,4(5):283-291
[37] Li Jiayin, Qiu Meikang, Niu Jianwei, et al. Feedback dynamic algorithms forpreemptable job scheduling in cloud systems[A]. Proceedings of IEEE internationalconference on web intelligence and intelligent agent technology[C],2010:561-564
[38] Dakshayini M., Guruprasad H. S.. An optimal model for priority based servicescheduling policy for cloud computing environment[J]. International journal ofcomputer applications,2011,32(9):23-29
[39] Hu Zheng, Wu K., Huang J.. An utility-based job scheduling algorithm for currentcomputing cloud considering reliability factor[A]. Proceedings of the2012IEEE3rdinternational conference on software engineering and service science[C],2012:296-299
[40] Capacity scheduler guide[EB/OL].http://hadoop.apache.org/docs/r1.2.1/capacity_scheduler.html,[2013-08-04]
[41] Fair scheduler guide[EB/OL]. https://hadoop.apache.org/docs/r1.2.1/fair_scheduler.html,
[2013-08-04]
[42] Zheng Zhongni, Wang Rui, Zhong Hai, et al. An approach for cloud resource schedulingbased on parallel genetic algorithm[A]. Proceedings of3rd international conference oncomputer research and development[C],2011:444-447
[43] Wang Wei, Zeng Guosun, Tang Daizhong, et al. Cloud-DLS: dynamic trusted schedulingfor cloud computing[J]. Expert systems with applications,2012,39(3):2321-2329
[44] Randies M., Lamb D., Taleb-Bendiab A.. A comparative study into distributed loadbalancing algorithms for cloud computing[A]. Proceedings of the IEEE24thinternational conference on advanced information networking and applicationsWorkshops[C],2010:551-556
[45] Noriyuki F., Kenichi H.. A comparison among grid scheduling algorithms forindependent coarse-grained tasks[A]. Proceedings of the symposium on applications andthe internet-workshops[C], Washington, IEEE computer society Press,2004:674-680
[46] Richard F. F., Michael G., Stephen A., et al. Scheduling resources in multi-user,heterogeneous, computing environments with SmartNet[A]. Proceedings of the7thIEEE heterogeneous computing workshop[C],1998:184-199
[47]徐志伟,冯百明,李伟.网格计算技术[M].北京:电子工业出版社,2004
[48] Wu Minyou, Shu Wei, Zhang Hong. Segmented min-min: a static mapping algorithm formeta-tasks on heterogeneous computing systems[A]. Proceedings of the9th IEEEheterogeneous computing workshop[C], USA, Washington, IEEE computer societypress,2000:375-385
[49] Muthucumaru M., Shoukat A., Howard J., et al. Dynamic matching and scheduling of aclass of independent tasks onto heterogeneous computing systems[A]. Proceedings ofthe8thheterogeneous computing workshop[C], USA, Washington, IEEE computersociety press,1999:30-44
[50] Lai G. J.. A novel task scheduling algorithm for distributed heterogeneous computingsystems[R]. Applied parallel computing, state of the art in scientific computing,7thinternational workshop, Lyngby, Denmark,2004:20-23
[51] Harmel S., Abdou Y.. Mapping and scheduling heterogeneous task graphs using geneticalgorithms[A]. Proceedings of the5th IEEE heterogeneous computing workshop[C],1996:86-97
[52] Vincenzo D., Marco M.. Scheduling in a grid computing environment using geneticalgorithms[A]. Proceedings of the16th international parallel and distributed processingsymposium[C],2002:235-239
[53] Mocanu E. M., Florea M., Andreica M. I., et al. Cloud computing–task schedulingbased on genetic algorithm[A]. Proceedings of the2012IEEE international systemsconference[C],2012:167-172
[54] Xu Zhihong, Hou Xiangdan, Sun Jizhou. Ant algorithm-based task scheduling in gridcomputing[A]. Proceedings of the IEEE Canadian conference on electrical andcomputer engineering[C],2003:1107-1110
[55] Ajith A., Rajkmar B., Nath B.. Nature's heuristics for scheduling jobs on computationalgrids[A]. Proceedings of the8th international conference on advanced computing andcommunications[C], Cochin, India,2000,45-52
[56] Zhang L., Chen Y., Sun R., et al. A task scheduling algorithm based on PSO for gridcomputing[J]. International journal of computational intelligence research,2008,4(1):37-43
[57] Wen Xiaotang, Huang Minghe, Shi Jianhua. Study on resources scheduling based onACO algorithm and PSO algorithm in cloud computing[A]. Proceedings of the11thinternational symposium on distributed computing and applications to business,engineering&science[C],2012,219-222
[58] Zhan Shaobin, Huo Hongying. Improved PSO-based task scheduling algorithm in cloudcomputing[J]. Journal of information&computational science,2012,9(13):3821-3829
[59] Falco I. D., Balio R. D., Tarantino E., et al. Improving search by incorporating evolutionprinciples in parallel Tabu search[A]. Proceedings of the IEEE conference onevolutionary computation[C],1994:823-828
[60] Chow K., Liu B.. On mapping signal processing algorithms to a heterogeneousmultiprocessor system[A]. Proceedings of the international conference on acoustics,speech, and signal processing[C],1991:1585-158
[61] Selvarani S., Sadhasivam G. S.. Improved cost-based algorithm for task scheduling incloud computing[A]. Proceedings of the2010IEEE international conference oncomputational intelligence and computer research[C],2010:1-5
[62] Kondo D., Javadi B., Malecot P., et al. Cost-bebefit analysis of cloud computing versusdesktop grids[A]. Proceedings of the2009IEEE international symposium on paralleland distributed processing[C],2009:1-12
[63] David E. I., Laura E. G., Jeffrey S. C.. Balancing risk and reward in a market-based taskservice[A]. Proceedings of the13thIEEE international symposium on high performancedistributed computing[C].2004:160-169
[64] Buyya R., Chee S. Y., Venugopal S.. Market-oriented cloud computing: vision, hype,and reality for delivering it service as computing utilities[A]. Proceedings of the10thIEEE international conference on high performance computing and communication[C],2008:5-13
[65] Greenberg A., James H., David A. M., et al. The cost of a cloud: research problems indata center networks[J]. Computer communication review,2009,39(1):68-73
[66] Bonvin N., Papaioannou T. G., Aberer K.. Dynamic cost-efficient replication in dataclouds[A]. The first ACM workshop on automated control for datacenters and clouds[C],2009:49-56
[67] Lee Y. C., Wang C., Zomaya A. Y., et al. Profit-driven scheduling for cloud services withdata access awareness[J]. Journal of parallel and distributed computing.2012,72(4):591-602
[68] Li Luqun. An optimistic differentiated service job scheduling system for cloudcomputing service users and providers[A]. Proceedings of the3rdinternationalconference on multimedia and ubiquitous engineering[C],2009:295-299
[69] Okuhara M., Shiozaki T., Suzuki T.. Security architectures for cloud computing[J].FUJITSU science&technical journal,2010,46(4):397-402
[70] Marcos D. de A., Alexandre di C., Rajkumar B.. A cost-benefit analysis of using cloudcomputing to extend the capacity of clusters[J]. Cluster computing,2010,13(3):335-347
[71] Laili Yuanjun, Tao Fei, Zhang Lin, et al. A study of optimal allocation of computingresources in cloud manufacturing systems[J]. The international journal of advancedmanufacturing technology,2012,63(5-8):671-690
[72] Jang E. Y., Kim H. J.. Rule-based cloud RBAC model for flexible resource allocation incloud computing service[J]. Information-an international interdisciplinary journal,2010,13(5):1653-1666
[73] Venters W., Whitley E. A.. A critical review of cloud computing: researching desires andrealities. Journal of information technology[J],2012,27(3):179-197
[74] Han Yanbo, Sun Junyi, Wang Guiling, et al. A cloud-based BPM architecture withuser-end distribution of non-compute-intensive activities and sensitive data[J]. Journalof computer science and technology,2010,25(6):1157-1167
[75] Lin Weiwei, Liang Chen, Wang J. Z., et al. Bandwidth-aware divisible task schedulingfor cloud computing[J]. Software: practice and experience,2014,44(2):163-174
[76] Hirai T., Masuyama H., Kasahara S., et al. Performance analysis of large-scaleparallel-distributed processing with backup tasks for cloud computing[J]. Journal ofindustrial and management optimization,2014,10(1):113-129
[77] Pedersen J. M., Riaz M. T., Dubalski B., et al. Using latency as a QoS indicator for aglobal cloud computing services[J]. Concurrency and computation practice andexperience,2013,25(18):2488-2500
[78] Pop F., Cristea V., Bessis N., et al. Reputation guided genetic scheduling algorithm forindependent tasks in inter-clouds environments[A]. Proceedings of the27thinternationalconference on advanced information networking and applications workshops[C],2013,772-776
[79] He X. S., Sun X. H., Laszewski G., QoS guided Min-Min heuristic for grid taskscheduling[J], Journal of Computer Science and Technology,2003,18(4):442-451
[80] Etminani K.. A Min-Min Max-Min selective algorithm for grid task scheduling[A],Proceedings of the3rdIEEE/IFIP international conference in central Asia on internet[C],2007,1-7
[81] Gao Y., Rong H., Huang J. Z., Adaptive grid job scheduling with genetic algorithms[J],Future generation computer systems,2005,21:151-161
[82] Garg S., Konugurthi P., Buyya R., A linear programming driven genetic algorithm formeta-scheduling on utility grids[J], International journal of parallel, emergent anddistributed systems,2011,26(6):493-517
[83] Buyya R., Giddy J., Abramson D.. An evaluation of economy based resource trading andscheduling on computational power Grids for parameter sweep applications[A].Proceedings of the2ndinternational workshop on active middleware services[C], KluwerAcademic Press, Pittsburgh, USA,2000
[84] Benoit A., Marchal L., Pineau J. F., et al, Scheduling concurrent bag-of-tasksapplications on heterogeneous platforms[J], IEEE transactions on computers,2010,59(2):202-217
[85] Oprescu A., Kielmann T.. Bag-of-tasks scheduling under budget constraints[A].Proceedings of the2ndIEEE international conference on cloud computing technologyand science[C],2010,351-359
[86] Netto M. A. S., Buyya R.. Offer-based scheduling of deadline-constrained bag-of-tasksapplications for utility computing systems[A]. Proceedings of the IEEE internationalsymposium parallel&distributed processing[C],2009,1-11
[87] Tao M., Dong S., He K.. A new replication scheduling strategy for grid workflowapplications[A]. Proceedings of the6thChinaGrid[C],2011,74-80
[88] Santos-Neto E., Cirne W., Brasileiro F., et al. Exploiting replication and data reuse toefficiently schedule data-intensive applications on grids[R]. Job scheduling strategies forparallel processing, lecture notes in computer science.2005(3277):210-232
[89] Liu W., Kettimuthu R., Li B., et al, An adaptive strategy for scheduling data-intensiveapplications in grid environments[A]. Proceedings of the17thIEEE internationalconference on telecommunications[C],2010,642-649
[90] Kousiouris G., Cucinotta T., Varvarigou T.. The affect of scheduling, workload type andconsolidation scenarios on virtual machine performance and their prediction throughoptimized artificial neural networks[J]. Journal of sysytems and software,2011,84(8):1270-1291
[91] Kiran M., Hashim A. H. A., Kuan L. M., et al. Execution time prediction of imperativeparadigm tasks for grid scheduling optimization[J]. International journal of computerscience and network security,2009,9(2):155-163
[92] Phinjaroenphan P., Bevinakoppa S., Zeephongsekul P.. A method for estimating theruntime of a parallel task on a grid node[A], Proceedings of the European gridconference[C], LNCS,2005,(3470):226–236
[93] Nadeem F., Fahringer T.. Using templates to predict execution time of scientificworkflow applications in the grid[A]. Proceedings of the9thIEEE/ACM internationalsymposium on cluster computing and the grid[C],2009,316-323
[94] Tao M., Dong S., Zhang L.. A multi-strategy collaborative prediction model for theruntime of online tasks in computing cluster/grid[A]. Cluster computing,2011,14(2):199-210
[95] Wolski R., Spring N. T., Hayes J.. The network weather service: a distributed resourceperformance forecasting service for meta-computing[J]. Future generation computersystems,1999,15(5–6):757-768
[96] Rodrigo N. C., Rajiv R., Anton B., et al. CloudSim: a toolkit for modeling andsimulation of cloud computing environments and evaluation of resource provisioningalgorithms[J]. Software: practice and experience,2011,41(1):23-50
[97] Ghalem B., Fatima Z. T., Wieme Z.. Approaches to improve the resources managementin the simulator CloudSim[A]. Proceedings of the first international conference oninformation computing and applications[C],2010,189-196
[98] Buyya R., Murshed M., Abramson D.. A deadline and budge constrained cost-timeoptimization algorithm for scheduling task farming applications on global grids[A].Proceedings of the international conference on parallel and distributed processingtechniques and applications[C],2002,1-12
[99] Ian F., Carl K.. The grid2: blueprint for a new computing infrastructure[M].2nd ed.USA, San Fransisco, Morgan Kaufmann Publisher,2004
[100]Ian F., Carl K., Steven T.. The anatomy of the grid: enabling scalable virtualorganizations[J]. International journal of high performance computing applications,2001,15(3):200-222
[101]罗红,慕德俊,邓智群等.网格计算中任务调度研究综述[J].计算机应用研究,2005,22(5):16-19
[102]Lin Chen. A novel green cloud computing framework for improving systemefficiency[J]. Physics procedia,2012,24(C):2326-2333
[103]Lin Chen. A novel college network resource management method using cloudcomputing[A]. International conference on applied physics and industrialengineering[C], Yang D, Physics procedia,2012,24(C):2293-2297
[104]Yang Kan, Jia Xiaohua. An efficient and secure dynamic auditing protocol for datastorage in cloud computing[J]. IEEE transactions on parallel and distributed systems,2013,24(9):1717-1726
[105]Lin Fengcheng, Chung Lankun, Wang Chunju, et al. Storage and processing of massiveremote sensing images using a novel cloud computing platform[J]. GIScience&remotesensing,2013,50(3):322-336
[106]Liao Bin, Yu Jiong, Sun Hua, et al. A QoS-aware dynamic data replica deletion strategyfor distributed storage systems under cloud computing environments[A]. Proceedings ofthe2ndinternational conference on cloud and green computing[C], IEEE computersociety,2012,219-225
[107]Samson Y., McDowall R. D.. Cloud computing: how to choose the right cloudsupplier[J]. Spectroscopy,2013,28(12):15-24
[108]Zhu Mengxia, Cao Fei, Mi Jia. A hybrid mapping and scheduling algorithm fordistributed workflow applciaiton in a heterogeneous computing environment[A].Proceedings of the5thinternational symposium on intelligent distributed computing[C],2011,117-127
[109]Plankensteiner K., Prodan R., Fahringer T.. Scheduling scientific workflows to meet softdeadlines in the absence of failure model[A]. Proceedings of the16thEuropeanconference on parallel processing[C],2010,6271:367-378
[110]Choe G., Lee B., Bae J., et al. An efficient scheduling algorithm for multiple workflowapplication in cloud computing[A]. ASME: proceedings of the international conferenceon internet technology and security[C],2010,151-156
[111]Garg S. K., Yeo C. S., Anandasivam A., et al. Environment-conscious scheduling ofHPC applications on distributed cloud-oriented data centers[J]. Journal of parallel anddistributed computing,2011,71(6):732-749
[112]Braun T. D., Siegel H. J., Beck N., et al. A comparison of eleven static heuristics formapping a class of independent tasks onto heterogeneous distributed computingsystems[J]. Journal of parallel and distributed computing,2001,61(6):810-837
[113]Shroff P., Watson D., Flann N., et al. Genetic simulated annealing for schedulingdata-dependent tasks in heterogeneous environments[A]. Proceedings of the5th IEEEheterogeneous computing workshop[C],1996:98-104
[114]Liu Jing, Chen Li, Dun Yuqing, et al. The research of ant colony and genetic algorithmin grid task scheduling[A]. Proceedings of the2008international conference onmultimedia and information technology[C]. USA, Washington: IEEE computer society,2008,47-49
[115]Fang Yiqiu, Wang Fei, Ge Junwei. A task scheduling algorithm based on load balancingin cloud computing[R]. Proceedings of the international conference on web informationsystems and mining[C],2010,271-277
[116]Martens B., Teuteberg F.. Decision-making in cloud computing environments: a cost andrisk based approach[J]. Information systems frontiers,2012,14(4):871-893
[117]Durkee D.. Why cloud computing will never be free[J]. Communications of the ACM.2010,53(5):62-69
[118]Peter S., Douglas T.. A comparison of critique of Encalyptus, OpenNebula andNimbus[J/OL], http://www.cse.nd.edu/~ccl/research/papers/psempoli-cloudcom.pdf,
[2013-11-11]
[119]Sandra K.. Cumulus and stratus clouds microstructure[J/OL],http://pernerscontacts.upce.cz/24_2011/Krollova.pdf,[2013-11-11]
[120]Rehr J. J., Vila F. D., Gardner J. P., et al. Scientific computing in the cloud[J].Computing in science&engineering,2010,12(3):34-43
[121]Andriole K. P., Khorasani R.. Cloud computing: what is it and could it be useful?[J]Journal of the American college of radiology,2010,7(4):252-254
[122]Bittencourt L. F., Madeira Edmundo R. M., Da Fonseca N. L. S.. Scheduling in hybridclouds[J]. IEEE communications magazine,2012,50(9):42-47
[123]Gottschalk I., Kirn S.. Cloud computing as a tool for enhancing ecological goal?-Analyzing necessary preconditions on the consumer side[J]. Business&informationsystems engineering,2013,5(5):299-313
[124]Choudhary V., Vithayathil J.. The impact of cloud computing: should the IT departmentbe organized as a cost center or a profit center?[J] Journal of management informationsystems,2013,30(2):67-100
[125]Kshetri N.. Cloud computing in developing economies[J]. Computer,2010,43(10):47-55
[126]Lorpunmanee S., Mohd N. S., Abdullah A. H., et al. An ant colony optimization fordynamic job scheduling in grid environment[J]. International journal of computer andinformation science and engineering,2007,1(4):207-214
[127]Wang Q., Gao Y., Liu P.. Hill climbing-based decentralized job scheduling oncomputational grids[A]. The first international multi-symposiums on computer andcomputational sciences[C], Hangzhou, China,2006:705-708
[128]Hoang P.. Springer handbook of engineering statistics[M]. London, USA, Spring-verlag.2006:229-247
[129]Xu M., Cui L., Wang H., et al. A multiple QoS constrained scheduling strategy ofmultiple workflows for cloud computing[A]. IEEE international symposium on paralleland distributed processing with applications[C], Chengdu, China,2009:629-634
[130]Mukherjee K., Sahoo G.. Mathematical model of cloud computing framework usingfuzzy bee colony optimization technique[A]. International conference on advances incomputing, control,&telecommunication technologies[C]. Trivandrum, Kerala,2009:664-668
[131]Maciej M., Kamil F., Jarek N.. Cost minimization for computational application onhybrid cloud infrastructures[J]. Future generation computer systems,2013,29(7):1786-1794
[132]Maurer M., Emeakaroha V. C., Brandic I., et al. Cost-benefit analysis of an SLAmapping approach for defining standardized cloud computing goods[J]. Futuregeneration computer systems,2012,28(1):39-47
[133]Robert A., Debra H., Taylor K.. The relative performance of various mapping algorithmsis independent of sizable variances in run-time predictions[A]. Proceedings of the7thIEEE heterogeneous computing workshop(HCW98)[C],1998:79-87
[134]Ibarra H., Kim C. E.. Heuristic algorithms for scheduling independent tasks onnonidentical processors[J]. Journal of the ACM.1977,24(2):280-289
[135]Elzeki M., Reshad M. Z., Elsoud M. A.. Improved max-min algorithm in cloudcomputing[J]. International journal of computer applications,2012,50(12):22-27
[136]Van den B. R., Vanmechelen K., Broeckhove J.. Online cost-efficient scheduling ofdeadline-constrained workloads on hybrid clouds[J]. Future generation computersystemd,2013,29(4):973-985
[137]Rajkumar B., David A., Jonathan G.. An economy driven resource managementarchitecture for global computational power Grids[A]. Proceedings of the7thinternational conference on parallel and distributed processing techniques andapplications[C],2000
[138]Vanmechelen K., Depoorter W., Broeckhove J.. Economic grid resource management forCPU bound applications with hard deadlines[A]. Proceedings of8th IEEE internationalconference on cluster computing and the grid[C], Lyon, France.2008,258-266
[139]Vasumathi S., Abhishek C., Jon W.. Exploring the throughput-fairness tradeoff ofdeadline scheduling in heterogeneous computing environments[J]. ACM SIGMETRICSperformance evaluation review,2008,36(1):463-464
[140]Kavitha G. S., Ozguner F., Dogan A.. A comparison of static QoS-based schedulingheuristics for a meta-task with multiple QoS dimensions in heterogeneous computing[A].Proceedings of the18thinternational conference on parallel and distributed processingsymposium[C].2004:26-30
[141]Cheng C., Sourav B.. Dynamic scheduling of real-time messages over an opticalnetwork[A]. Proceedings of the6th IEEE international conference on computercommunications and networks[C],1997:336-339
[142]Chun B. N.. Market-based cluster resource management[D]. PhD thesis, University ofCalifornia at Berkeley,2001
[143]Chun B. N., Culler D. E.. User-centric performance analysis of market-based clusterbatch schedulers[A]. Proceedings of the2nd IEEE/ACM international symposium oncluster computing and the grid[C].2002:21-24
[144]Subramoniam K., Maheswaran M., Toulouse M.. Towards a micro-economic model forresource allocation in grid computing systems[A]. Proceedings of the IEEE Canadianconference on electrical and computer engineering[C].2002:782-785
[145]Ravi B., Sanjukta D., Robert G., et al. A market design for grid computing[J].INFORMS journal on computing,2007,20(1):100-111
[146]Gunther S., Kurt V., Jan B.. A commodity market algorithm for pricing substitutable gridresources[J]. Future Generation Computer Systems,2007,23(5):688-701
[147]Buyya R., Murshed M., Abramson D.. A deadline and budget constrained cost-timeoptimization algorithm for scheduling task farming applications on global grids[A].Proceedings of the international conference on parallel and distributed processingtechniques and applications[C],2001
[148]Liu J., Li G.. An improved min-min grid tasks scheduling algorithm based on Qosconstraints[A]. Proceedings of the international conference on optics photonics andenergy engineering[C].2010:281-283
[149]Calheiros R. N., Ranjan R., Cesar AF De R., et al. CloudSim: a novel framework formodeling and simulation of cloud computing infrastructures and services[R]. Echnicalreport, Grids-TR-2009-1, Grid computing and distributed systems laboratory, TheUniversity of Melbourne, Australia,2009
[150]Muhammad I., Zhu Hong, Tauseef Q., et al. Requirement analysis and design of servicelevel integration layer for cloud computing services, to meet service level agreementsand quality of service[J]. Journal of computational and theoretical nanoscience,2014,11(3):629-636
[151]Zhu Yishui, Shtykh R. Y., Jin Qun. A human-centric framework for context-awareflowable services in cloud computing environments[J]. Information sciences,2014,257:231-247
[152]Kaewpuang R., Niyato D., Wang Ping, et al. A framework for cooperative resourcemanagement in mobile cloud computing[J]. IEEE journal on selected areas incommunications,2013,31(12):2685-2700
[153]Exposito R. R., Taboada G. L., Ramos S., et al. Evaluation of messaging middleware forhigh-performance cloud computing[J]. Personal and ubiquitous computing,2013,17(8):1709-1719
[154]Xu Baomin, Zhao Chunyan, Hu E., et al. Job scheduling algorithm based on Bergermodel in cloud environment[J]. Advances in engineering software,2011,42(7):419-425
[155]Bessis N., Sotiriadis S., Xhafa F., et al. Cloud scheduling optimization: a reactive modelto enable dynamic deployment of virtual machines instantiations[J]. Informatica,2013,24(3):357-380
[156]Grace R. K., Manimegalai R.. Dynamic replica placement and selection strategies indata grids–a comprehensive survey[J]. Journal of parallel and distributed computing,2014,74(2):2099-2108
[157]Li Jing. A replica selection decision in cloud computing environment[A]. Proceedings ofthe international conference on advances in computing science and engineering[C],2010,801-806
[158]Ryu B. G., Choi J. H., Lee S. K.. Impact of node distance on selfish replica allocation ina mobile ad-hoc network[J]. Ad hoc networks,2013,11(8):2187-2202
[159]He Jing, Zhang Yanchun, Huang Guangyan, et al. Distributed data possession checkingfor securing multiple replicas in geographically-dispersed clouds[J]. Journal of computerand system sciences,2012,78(5):1345-1358
[160]Ai Lifeng, Tang Maolin, Fidge Colin. Resource allocation and scheduling of multiplecomposite web services in cloud computing using cooperative coevolution geneticalgorithm[A]. Proceedings of the18thinternational conference on neural informationprocessing[C],2011,258-267
[161]Kavitha, R., Iamnitchi A., Foster I.. Improving data availability through dynamic modeldriven replication in large peer-to-peer communities[A]. Proceedings of global andpeer-to-peer computing on large scale distributed systems workshop[C], Berlin,Germany,2002
[162]Ranganathan K., Foster I.. Design and evaluation of dynamic replication strategies for ahigh-performance data grid[A]. Proceedings of international conference on computing inhigh energy and nuclear physics[C],2001
[163]Bell W. H., Cameron D. G., Capozza L., et al. OptorSim-a grid simulator for studyingdynamic data replication strategies[J]. International journal of high performancecomputing applications,2003,17(4)
[164]Park S. M., Kim J. H., Ko Y. B., et al. Dynamic data grid replication strategy based oninternet hierarchy[R]. International workshop on grid and cooperative computing,Lecture note in computer science,2003,836-846
[165]Horri A., Sepahvand R., Dastghaibyfard G. H.. A hierarchical scheduling and replicationstrategy[J]. International journal of computer science and network security,2008,8(8):30-35
[166]Mansouri N., Dastghaibyfard G. H.. A dynamic replica management strategy in datagrid[J]. Journal of network and computer applications,2012,35(4):1297-1303
[167]Sepahvand R., Horri A., Dastghaibyfard G. H.. Replication and scheduling methodsbased on prediction in data grid[J]. Journal of applied sciences research,2011,7(11):1485-1496
[168]Lamehamedi H., Shentu Z., Szymanski B., et al. Simulation of dynamic data replicationstrategies in data grids[A]. Proceedings of the17th international parallel and distributedprocessing symposium[C], IEEE computer society2003
[169]Tang Ming, Lee Busung, Tang Xueyan, et al. The impact of data replication on jobscheduling performance in the data grid[J]. Future generation computing systems,2006,22(3):254-268