Web服务中若干问题的研究
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摘要
本文主要研究内容为当前Web服务领域中的几个热点问题，包括Web服务选取方法、多等级Web服务部署问题以及Web服务组合运行时容错处理和异常恢复方法及策略的研究。
本文中首先提出了一种改进的人工蜂群算法用来求解服务选取问题。结合遗传操作设计雇用蜂觅食及侦查蜂策略，并针对服务选取问题的特点提出了基于效用值及违反约束度的食物源评价方法及基于邻域的随机贪心观察蜂觅食策略，进而建立求解该问题的人工蜂群算法优化模型，在此基础上实现了求解服务选取问题的混合人工蜂群算法-GABC。
其次提出了一种基于协同过滤推荐的混合式Web服务选取方法，将Web服务请求者的个人兴趣偏好和相似用户群体的经验相结合，利用推荐技术对具有相同功能和不同Qos属性值的Web服务进行选取。
再次提出了一种改进的遗传算法用来求解多等级服务部署问题。在选择过程中引入了个体被支配强度，通过将个体的支配强度和被支配强度结合到一起建立对个体的评价策略，进而对效用函数进行重新定义，并设计了基于概率的交叉策略及结合局部搜索的个体变异策略和根据评价结果进行环境选择及生成个体的交叉概率方法。此外还设计新的局部搜索策略并将其融入到变异策略中，提高了变异操作的有效性。
最后给出了一种基于服务冗余和约束冗余的Web服务组合运行时容错处理和异常恢复策略。通过在组合规划阶段为服务流程中每个抽象成员服务建立后备服务组和将约束分解到每个抽象成员服务上的方法，为运行时的容错处理和异常恢复提供了一种以尽可能小的代价恢复服务组合运行并尽量保留已执行部分结果的处理策略。在此基础上，进一步给出了服务组件库的建立和维护策略及其算法。
本文中针对Web服务领域中几个关键问题进行了研究和探讨，所做的主要研究工作和研究成果在提高web服务和服务组合的可靠性和效率等方面具有一定的意义。
Due to the rapid development of Internet technology and the rise of e-commerce, greatdemands for large numbers of new resources and applications emerge. Traditional softwaredevelopment architectures and methods could not satisfy the needs in complex enterpriseapplications. In order to handle the differences between platforms and protocols in the Webenvironment encountered in enterprise application integration and the collaboration in looselycoupled approaches, and to improve the efficiency of software development and integration,in addition to the sharing of information resources, a new software development architectureis proposed as a service-oriented architecture, namely SOA.
SOA is an open, standard protocol that serves as a basic functional unit, a looselycoupled model framework, and also a design method. It can be applied to the existed isolatedsoftware applications into self-contained services. Applications may access these servicesthrough simple interfaces and communication protocols, which reduces the costs ofinteroperability, and supports the rapid development of enterprise applications. It can be usedswift development of enterprise applications, and may respond in time to the actual needs ofthe rapidly changing Internet environment. The research of SOA has received widespreadattention in recent years, from industry and academia.
With the gradual popularization and application of SOA, Web service has been playingan increasingly significant role as a way to achieve SOA, also as a new type of distributedcomputing resources.
Web services collect application logic, network technology and a variety of servicefunctional modules so that any company or individual could access them in any place andtime. With the extensive application of Web services, a series of major issues emerge on thelist. For example, in the instantiation of an abstract service process, how to select from a largenumber of candidate emerged Web services with the same quality of service for differentfunctions; in the dynamic Internet environment, how to ensure the reliability of the serviceportfolio under the possibility of a service failure or new services available, and so on.
On the basis of Systematic analysis of related methods and the current research status athome and abroad, this dissertation is focused mainly on web services selection, servicesdeployment and fault-tolerance and recovery in runtime.
The main achievements and content of this dissertation are summarized as following:
(1) This thesis reviews the process of software development architectures and methods inthe past years, especially introduces the emerging SOA architecture. The analysis andsummary of research status of hot issues in the field of Web services is also given. All theseintroduce and summaries provide a solid foundation for the research of this thesis.
(2)In order to solve the service selection problem, a discrete artificial bee colonyalgorithm called GABC was proposed and the corresponding optimization model wasestablished. According the character of the service selection problem, the genetic operator iscombined to form the foraging strategy of employed bees, and a greedy exploited strategy isdesigned for the onlookers. The attractive probability of a food source is redefined combiningits constraint violations. Through experiments, the algorithm was tested and compared withrelated algorithms. The results showed that GABC algorithm can effectively solve thisproblem.
(3)A web services selection method based on collaborative filtering recommendation isproposed. In this dissertation, collaborative filtering recommendation method is indroduced toweb services selection field.According to the selection of similar web service users andcurrent user’s individual preferences, the unknown Qos values can be predicted, and then theweb services with top Qos values are Personalized recommendated to the current user.
(4)For SLA-aware service composition problem, a multi-objective model for thisproblem is built and a multi-objective genetic optimization algorithm is also proposed in thispaper. According to the characteristics of this problem, genetic operations are redefined,including the introduction of individual domination strength into environmental selection, andthe redefinition of crossover strategy and individual mutation strategy incorporated with localsearch. At last, we analyzed our algorithm SMOGA, and compared it with recently proposedalgorithm E3-MOGA and NSGA-II for this problem on different scale test cases; theexperiments results show that algorithm SMOGA can solve this problem more effectively.
(5)On the basis of already existing runtime fault-tolerance and recovery methods, a QoSconstrained based on similar web service substitue and Dynamic Partial Reconfigurationstrategy is proposed, and then the corresponding algorithm is given. The core idea of thisstragety is taking the redundant constraints into account and decomposing the globalconstraints into every member of service composition, and then the result of decomposing canbe used as constraint conditions of similar web service substitue and Dynamic PartialReconfiguration. Further, a web service components library and corresponding maintenancealgorithm are proposed.
The research work and achievement in this dissertation has some theoretic andapplication value to improve the reliability and efficiency of Web services and servicecomposition.
本文中首先提出了一种改进的人工蜂群算法用来求解服务选取问题。结合遗传操作设计雇用蜂觅食及侦查蜂策略，并针对服务选取问题的特点提出了基于效用值及违反约束度的食物源评价方法及基于邻域的随机贪心观察蜂觅食策略，进而建立求解该问题的人工蜂群算法优化模型，在此基础上实现了求解服务选取问题的混合人工蜂群算法-GABC。
其次提出了一种基于协同过滤推荐的混合式Web服务选取方法，将Web服务请求者的个人兴趣偏好和相似用户群体的经验相结合，利用推荐技术对具有相同功能和不同Qos属性值的Web服务进行选取。
再次提出了一种改进的遗传算法用来求解多等级服务部署问题。在选择过程中引入了个体被支配强度，通过将个体的支配强度和被支配强度结合到一起建立对个体的评价策略，进而对效用函数进行重新定义，并设计了基于概率的交叉策略及结合局部搜索的个体变异策略和根据评价结果进行环境选择及生成个体的交叉概率方法。此外还设计新的局部搜索策略并将其融入到变异策略中，提高了变异操作的有效性。
最后给出了一种基于服务冗余和约束冗余的Web服务组合运行时容错处理和异常恢复策略。通过在组合规划阶段为服务流程中每个抽象成员服务建立后备服务组和将约束分解到每个抽象成员服务上的方法，为运行时的容错处理和异常恢复提供了一种以尽可能小的代价恢复服务组合运行并尽量保留已执行部分结果的处理策略。在此基础上，进一步给出了服务组件库的建立和维护策略及其算法。
本文中针对Web服务领域中几个关键问题进行了研究和探讨，所做的主要研究工作和研究成果在提高web服务和服务组合的可靠性和效率等方面具有一定的意义。
Due to the rapid development of Internet technology and the rise of e-commerce, greatdemands for large numbers of new resources and applications emerge. Traditional softwaredevelopment architectures and methods could not satisfy the needs in complex enterpriseapplications. In order to handle the differences between platforms and protocols in the Webenvironment encountered in enterprise application integration and the collaboration in looselycoupled approaches, and to improve the efficiency of software development and integration,in addition to the sharing of information resources, a new software development architectureis proposed as a service-oriented architecture, namely SOA.
SOA is an open, standard protocol that serves as a basic functional unit, a looselycoupled model framework, and also a design method. It can be applied to the existed isolatedsoftware applications into self-contained services. Applications may access these servicesthrough simple interfaces and communication protocols, which reduces the costs ofinteroperability, and supports the rapid development of enterprise applications. It can be usedswift development of enterprise applications, and may respond in time to the actual needs ofthe rapidly changing Internet environment. The research of SOA has received widespreadattention in recent years, from industry and academia.
With the gradual popularization and application of SOA, Web service has been playingan increasingly significant role as a way to achieve SOA, also as a new type of distributedcomputing resources.
Web services collect application logic, network technology and a variety of servicefunctional modules so that any company or individual could access them in any place andtime. With the extensive application of Web services, a series of major issues emerge on thelist. For example, in the instantiation of an abstract service process, how to select from a largenumber of candidate emerged Web services with the same quality of service for differentfunctions; in the dynamic Internet environment, how to ensure the reliability of the serviceportfolio under the possibility of a service failure or new services available, and so on.
On the basis of Systematic analysis of related methods and the current research status athome and abroad, this dissertation is focused mainly on web services selection, servicesdeployment and fault-tolerance and recovery in runtime.
The main achievements and content of this dissertation are summarized as following:
(1) This thesis reviews the process of software development architectures and methods inthe past years, especially introduces the emerging SOA architecture. The analysis andsummary of research status of hot issues in the field of Web services is also given. All theseintroduce and summaries provide a solid foundation for the research of this thesis.
(2)In order to solve the service selection problem, a discrete artificial bee colonyalgorithm called GABC was proposed and the corresponding optimization model wasestablished. According the character of the service selection problem, the genetic operator iscombined to form the foraging strategy of employed bees, and a greedy exploited strategy isdesigned for the onlookers. The attractive probability of a food source is redefined combiningits constraint violations. Through experiments, the algorithm was tested and compared withrelated algorithms. The results showed that GABC algorithm can effectively solve thisproblem.
(3)A web services selection method based on collaborative filtering recommendation isproposed. In this dissertation, collaborative filtering recommendation method is indroduced toweb services selection field.According to the selection of similar web service users andcurrent user’s individual preferences, the unknown Qos values can be predicted, and then theweb services with top Qos values are Personalized recommendated to the current user.
(4)For SLA-aware service composition problem, a multi-objective model for thisproblem is built and a multi-objective genetic optimization algorithm is also proposed in thispaper. According to the characteristics of this problem, genetic operations are redefined,including the introduction of individual domination strength into environmental selection, andthe redefinition of crossover strategy and individual mutation strategy incorporated with localsearch. At last, we analyzed our algorithm SMOGA, and compared it with recently proposedalgorithm E3-MOGA and NSGA-II for this problem on different scale test cases; theexperiments results show that algorithm SMOGA can solve this problem more effectively.
(5)On the basis of already existing runtime fault-tolerance and recovery methods, a QoSconstrained based on similar web service substitue and Dynamic Partial Reconfigurationstrategy is proposed, and then the corresponding algorithm is given. The core idea of thisstragety is taking the redundant constraints into account and decomposing the globalconstraints into every member of service composition, and then the result of decomposing canbe used as constraint conditions of similar web service substitue and Dynamic PartialReconfiguration. Further, a web service components library and corresponding maintenancealgorithm are proposed.
The research work and achievement in this dissertation has some theoretic andapplication value to improve the reliability and efficiency of Web services and servicecomposition.
引文
[1] A.Brown.Large-Scale Component-Based Development[M].NewJersery: Prentice-Hallinc.,2000.
[2] Roy W. Schulte,Yefim V. Natisy. Service Oriented Architectures,Part1and2[M].Garter Researeh Note SPA-401-068and SPA-401-069,1996.
[3] E.Neweomer,G. Lomov.Understanding SOA with WebServiees[M]. Addison-Wesley,2005.
[4] Niekull D. Service Oriented Architecture Whitepaper[M]. Whitepaper Adobe SystemsIncorporated,2005.
[5] BoraYurday. A service oriented reflective wireless middleware,Intl[C].conf.onService oriented ComPuting, ICSOC06,Chicago US,Springer LNCS4294,BerlinHeidelberg,2006.
[6] Ecabrera et al. Web Services Coordination(WS-Coordination)[M/OL]. August2002,http://www.ibm.com/developer works/library/ws-coor/
[7] Mike P PaPazoglou,Willem-Jan van den Heuvel. Service oriented architectures:approaches,technologies and research issues[J].The VLDB Joumal,2007,16(3):389-415.
[8] Alonso G, Casati F, et al. Web Services: Concepts, Architecture and Applications [M].London: Springer-Verlag,2004.
[9] Paolucci M, Kawamura T, et al. Importing the Semantic Web in UDDI [C],International Workshop on Web Services, E-Business, and the Semantic Web,2002,225-236.
[10]Klusch M. Semantic Web Service Coordination [M]//Schumacher M, Helin H,Schuldt H. CASCOM: Intelligent Service Coordination in the Semantic Web.Birkh user Basel.2008:59-104.
[11]Sioutas S, Sakkopoulos E, et al. Dynamic Web Service discovery architecture basedon a novel peer based overlay network [J]. The Journal of Systems and Software,2009,82(5):809-824.
[12]Li Y, Su S, Yang F. A Peer-to-Peer Approach to Semantic Web Services Discovery [C].The6-th International Conference on Computational Science, Reading, UK,2006,73-80.
[13]Liu S, Kungas P, Matskin M. Agent-Based Web Service Composition with JADE andJXTA [C].The2006International Conference on Semantic Web&Web Services, LasVegas, USA,2006,110-116.
[14]Haase P, Siebes R, van~Harmelen F. Expertise-based peer selection in Peer-to-Peernetworks [J]. Knowledge and Information Systems,2008,15(1):75-107.
[15]Loo B T, Huebsch R, et al. The Case for a Hybrid P2P Search Infrastructure [C]. The3rd International Workshop on P2P Systems, CA, USA,2004,141-150.
[16]Verma K, Sivashanmugam K, et al. METEOR-S WSDI: A Scalable P2P Infrastructureof Registries for Semantic Publication and Discovery of Web Services [J].Information Technology and Management,2005,6(1):17-39.
[17]Fernandez A, Hayes C, et al. Closing the Service Discovery Gap by CollaborativeTagging and Clustering Techniques [C]. Proceedings of the2nd InternationalWorkshop on Service Matchmaking and Resource Retrieval in the Semantic Web,Karlsruhe, Germany,2008, No.8.
[18]Wu C, Chang E. Aligning with the Web: an atom-based architecture for Web servicesdiscovery [J]. Service Oriented Computing and Applications,2007,1(2):97-116.
[19]Bose A, Nayak R, Bruza P. Improving Web Service Discovery by Using SemanticModels [C]. Proceedings of the9th international conference on Web InformationSystems Engineering, Auckland, New Zealand,2008,366-380.
[20]D'Mello D A, Ananthanarayana V S. A tree structure for web service compositions
[C]. Proceedings of the2nd Bangalore Annual Compute Conference, Bangalore, India,2009, No.18.
[21]Slavova S. Dynamic Selection Of Redundant Web Services [D]. Ms.D. Thesis.Saskatoon, Canada; University of Saskatchewan,2007.
[22]Stein S, Payne T R, Jennings N R. Flexible Provisioning of Web Service Workflows[J]. ACM Transactions on Internet Technology,2009,9(1):2.
[23]Li Y, Dong B. An Algorithm for Semantic Web Services Composition Based onOutput and Input Matching [M]//Wang W. Integration and Innovation Orient toE-Society Volume2. Springer.2008:297-307.
[24]Blankenburg B, Botelho L, et al. Service Composition [M]//Schumacher M, Helin H,Schuldt H. CASCOM: Intelligent Service Coordination in the Semantic Web.Birkh user Basel.2008:235-262.
[25]Karakoc E, Senkul P. Composing Semantic Web Services Under Constraints [J].Expert Systems with Applications,2009.doi:10.1016/j.eswa.2009.02.098.
[26]Hamadi R, Benatallah B. A Petri Net-based Model for Web Service Composition [C],Proceedings of the14th Australasian Database Conference, Adelaide, South Australia,2003,191-200.
[27]W3C.Web Services Architecture [M/OL].2003,http://www.w3.org/TR/ws-arch/
[28]Alonso G, Casati F, et al. Web Services: Concepts, Architecture and Applications [M].London: Springer-Verlag,2004.
[29]Wang H, Huang J, et al. Web services: problems and future directions [J]. Journal ofWeb Semantics,2004,1:309–320.
[30]Bray T,Paoli J,Sperberg-McQueen C M, Maler E.Extensible Markup Language (XML)1.0(Second Edition)[M/OL].2000,http://www.w3.org/TR/2000/REC-xml-20001006.
[31]W3C. Web services description language(WSDL) version2.0Part1,:Corelanguage[M/OL].2007,http://www.w3c.org/tr/REC-dl20-20070626
[32]UDDI. The UDDI technical white paper [M/OL].2002.http://uddi.org/pubs/uddi-v3.00-published-20020719.htm.
[33]T. Berners-Lee,J.Hendler,et al.The semantic Web[J]. Scientific American,2001,279(5):34-43.
[34]Martin D, Burstein M, et al. OWL-S: Semantic markup for web services [M/OL].W3C member submission,2004. http://www.w3.org/Submission/OWL-S/.
[35]de Bruijn J, Bussler C, et al. Web service modeling ontology (WSMO)[M/OL]. W3CMember Submission,2005. http://www.wsmo.org/.
[36]Battle S, Bernstein A, et al. Semantic Web service ontology (SWSO)[M/OL]. W3Cmember submission,2005. http://www.w3.org/Submission/SWSF-SWSO/.
[37]Klein M, Konig-Ries B, Mussig M. What is needed for semantic service descriptions-A proposal for suitable language constructs [J]. International Journal of Web GridService,2005,1(3-4):328-364.
[38]Akkiraju R, Farrell J, et al. Web service semantics-WSDL-S [M/OL]. W3C MemberSubmission,2005. http://www.w3.org/Submission/WSDL-S/.
[39]S. Ran. A model for Web services discovery with QoS [J]. ACM SIGecom Exchangs,2003,4(1):1-10.
[40]L.Z.Zeng,B. Benatallah, Anne H. H. Ngu, et al. QoS-aware middleware for Webservices composition [J]. IEEE Transactions on Software Engineering,2004,30(5):311-327.
[41]Wang Y, Vassileva J. Toward Trust and Reputation Based Web Service Selection: ASurvey [J]. International Transactions on Systems Science and Applications,2007,3(2):118-132.
[42]Bonabeau E,Dorigo M,Theraulaz G. Swarm Intelligence:From Natural to ArtificialSystem[M].New York: Oxford University Press,1999.
[43]Dorigo M, Bonabeau E, Theraulaz G. Ant algorithms and stigmergy[J].FutureGeneration Computer Systems,2000,16(9):851-871.
[44]Passino, K M，Biomimicry of bacterial foraging for distributed optimization andcontrol[J].IEEE Control Systems Magazine,2002,22(3):52-67.
[45]Eusuff M M, Lansey K E.Optimization of Water Distribution Network Design UsingShuffled Frog Leaping Algorithm[J]. Journal of Water Resources Planning andManagement，2003，129(3):210-225.
[46]Dervis Karaboga,Bahriye Akay.A comparative study of Artificial Bee ColonyAlgorithm[J].Applied Mathematics and Computation,2009,214:108-132.
[47]李晓磊，邵之江，钱积新.一种基于动物自治体的寻优模式:鱼群算法[J].系统工程理论与实践，2002,22(11):32-38.
[48]Kennedy J, Eberhart R C. Particle swarm optimization[C]. Proceeding Soft the IEEEInternational Conference on Neural Networks, Perth, Australia. Piscataway, NJ:IEEEPress,1995，1942-1948.
[49]Colorni A,Dorigo M,Maniezzo V,et al.Distributed Optimization by Ant colonies
[C].Proceedings of the1st European Conference on Artificial Life,1991:134-142.
[50]Solnon C. Ants Can Solve Constraint Satisfaction Problems[J]. IEEE Transactions onEvolutionary Computation,2002,6(4):347-357.
[51]Tavares Neto R F,Godinho Fiho M.An Ant Colony Optimization Approach to aPermutational Flowshop Scheduling Problem with Outsourcing allowed [J].Computers&Operations Research,2011,38(9):1286-1293.
[52]Mousa A A,Abd EI-Wahed W F,Rizk-Allah R M. A Hybrid Ant Colony OptimizationApproach Based Local Search Scheme for Multi-objective Design Optimizations[J].Electric Power Systems Research,2011,81(4):1014-1023.
[53]Samarghandi H,Taabayan P,Jahantigh F F.A Particle Swarm Optimization for theSingle Row Facility Layout Problem[J]. Computers&Industrial Engineering,2010,58(4):529-534.
[54]Wang H,Moon L,Yang S,et al. A Memetic Particle Swarm Optimization Algorithm forMultimode Optimization Problems[J]. Information Science,2012,197(15):38-52.
[55]Karaboga D. An Idea Based on Honey Bee Swarm for Numerical Optimization
[R].Technical Report-TR06, Erciyes. University, Engineering Faculty, ComputerEngineering Department,2005.
[56]J. Cardoso, J. Miller, A. Sheth, and J. Arnold. Quality of service for workflows andweb service processes[J]. Journal of Web Semantics,2004,1:281–308.
[57]D.A. Menascé, et al. On optimal service selection in Service Oriented Architectures[J]. Performance Evaluation,2010,67(8):659-675.
[58]E. Al-Masri, and Q.H.Mahmooud, Investigating Web Services on the World WideWeb[A],17th International Conference on World Wide Web (WWW,2008)[C].Beijing, April2008, pp.795-804.
[59]C.W. Zhang, S. Su, J. L. Chen. DiGA: Population diversity handling geneticalgorithm for QoS-aware web services selection[J]. Computer Communications,30(5):1082-1090,2007
[60]X.Q. Fan, X.W. Fang, C.J. Jiang. Research on Web service selection based oncooperative evolution[J]. Expert Systems with Applications,2011,38(8):9736-9743.
[61]夏亚梅，程渤等.基于改进蚁群算法的服务组合优化[J].计算机学报,2012，35(2):270-281.
[62]Rodriguez F J, Lozano M, García-Martínez C, et al. An artificial bee colonyalgorithm for the maximally diverse grouping problem[J]. Information Sciences,2013,230:183-196.
[63]Karaboga D, Akay B. A modified artificial bee colony (ABC) algorithm forconstrained optimization problems. Applied Soft Computing,2011,11(3):3021-3031.
[64]Pan Q K, Wang L, Mao K, et al. An effective artificial bee colony algorithm for areal-world hybrid flowshop problem in steelmaking process[J]. IEEE Transactions onAutomation Science and Engineering,2013,10(2):307-322.
[65]Pan Q K, Fatih Tasgetiren M, Suganthan P N, et al. A discrete artificial bee colonyalgorithm for the lot-streaming flow shop scheduling problem[J]. InformationSciences,2011,181(12):2455-2468.
[66]Mohammad A, Dimitrios S, Thomas R. Selecting skyline services for QoS based webservice composition[C]//WWW2010. Raleigh,2010:11-20.
[67]Adomavicius G, Tuzhilin A. Toward the next generation of recommender systems: Asurvey of the state-of-the-art and possible extensions[J]. Knowledge and DataEngineering, IEEE Transactions on.2005,17(6):734-749.
[68]Goldberg D, Nichols D, Oki B.M, Terry D. Using collaborative filtering to weave aninformation Tapestry[J]. Communications of the ACM,1992,35(12):61-70.
[69]Resnick P, lacovou N, Suchak M,et al. Grouplens: An open architecture forcollaborative filtering of netnews[C]. Proceedings of the1994ACM conference onComputer supported cooperative work,1994:175-186.
[70]Shardanand U, Maes P. Social information filtering: algorithms for automating “wordof mouth”[C]. Proceedings of the SIGCHI conference on Human factors incomputing systems,1995. ACM Press/Addison-Wesley Publishing Co.,1995:210-217.
[71]Adomavicius G,Tuzhilin A.Towards the Next Generation of RecommenderSystems:A Survey of the State-of-the-Art and Possible Extensions [J]. IEEETransactions on Knowledge and Data Engineering,2005,17(6):734-749.
[72]Sarwar B,Karypis G, Konstan J, et al. Item-Based Collaborative FilteringRecommendation Algorithms[C]. In Proceedings of the10th Int’1World Wide WebConf. New York: ACM Press,2001,285-295.
[73]Sarwar B,Karypis G,Konstan J,et al.Analysis of Recommendation Algorithms forE-commerce[C]. In Proceedings of the2ndACM Conf. On Electronic Commerce.NewYork: ACM Press,2001,158-167.
[74]Koren Y. Factor in the neighbors:Scalable and accurate collaborative filtering[J].ACM Transactions on Knowledge Discovery from Data,2010,4(1):1-24.
[75]Huang Z, Chen H，Zeng D. Applying associative retrieval techniques to alleviate thesparsity problem in collaborative filtering [J].ACM Transactions on InformationSystems,2004，22(1):116-142.
[76]ROBIN B. Hybrid recommender systems: survey and experiments [J]. User Modelingand User-Adapted Interaction,2002,12(4):331-370.
[77]Breese J S, Heckertnan D, Kadie C. Empirical analysis of predictive algorithms forcollaborative filtering[C]. Proceedings of the14th conference on Uncertainty inArtificial Intelligence,1998:43-52.
[78]Bell R,Koren Y,Volinsky C. The BellKor2008Solution to the Netflix Prize.2008.http://www.netflixprize.com/community/viewtopic.php?id=1193.
[79]Yu C,Xu,J R, Du X Y.Recommendation algorithm combining the user-basedclassified regression and the item-based filtering[C]. Proceedings of the8thlnternational Conference on Electronic Commerce: The New E-commercelnnovations for Conquering Current Barriers, Obstacles and Limitations toConducting Successful Business on the lnternet. New York:ACM Press,2006:574-578.
[80]Sarwar B,Karypis G,Konstan,J，et al.Analysis of recommendation algoritlims fore-commerce[C].Proceedings of the2ndACM Conference on electronic Commerce.New York:ACM Press,2000:158-167.
[81]Sarwar B, Karypis G, Konstan J, et al. Item-based collaborative filteringrecommendation algorithms[C]. Proceedings of the10th international conference onWorld Wide Web,2001:285-295.
[82]Wang J, De Vries A P, Reinders M J T. Unifying user-based and item-basedcollaborative filtering approaches by similarity fusion[C]. Proceedings of the29thannual international ACM SIGIR conference on Research and development ininformation retrieval,2006:501-508.
[83]J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl. Evaluating collaborativefiltering recommender systems[J].ACM Transactions on Information Systems,2004,22(1):5-53.
[84]Shannon C.E. The mathematical theory of communication[J]. The Bell SystemTechnical Journal,1948, Vol.27,379-423.
[85]Ouzzani, Bouguettaya. Efficient access to Web services[j]. IEEE Internet Computing.2004,8(2):34-44.
[86]Yin Y, Zhang B, Zhang X Z. An active and opportunistic service replacementalgorithm orienting transactional composite service dynamic adaptation[J]. JisuanjiXuebao(Chinese Journal of Computers),2010,33(11):2147-2162.
[87]Yan Gao, Shang-xin Zhang, Bin Zhang, et al. SOA Based Web Services CompositionSystem[J]. Journal of Chinese Computer System,2007,28(4):729-733.
[88]J.H.Holland.Adaptation in Natural and Artificial Systems[M].University of MichiganPress,1975.
[89]Goldberg D E. Genetic Algorithms in Search, Optimization and Machine Learning
[M]. Addison-Wesley,1989.
[90]Cardellini V, Casalicchio E, Grassi V, et al. Moses: A framework for qos drivenruntime adaptation of service-oriented systems[J]. IEEE Transactions on SoftwareEngineering, vol.38, no.5, Sept-Oct2012, pp.1138-1159.
[91]Yu T, Zhang Y, Lin K J. Efficient algorithms for Web services selection withend-to-end QoS constraints[J]. ACM Transactions on the Web (TWEB),2007,1(1):6.
[92]Calinescu R, Grunske L, Kwiatkowska M, et al. Dynamic QoS management andoptimization in service-based systems[J]. Software Engineering, IEEE Transactionson,2011,37(3):387-409.
[93]Ming-wei Z, Bin Z, Xin-zhe Z, et al. A Division Based Composite Service SelectionApproach[J]. Journal of Computer Research and Development,2012,49(5):1005-1017.
[94]Wada H, Suzuki J, Yamano Y, et al. E3: A Multiobjective Optimization Framework forSLA-Aware Service Composition[J]. Services Computing, IEEE Transactions on,2012,5(3):358-372.
[95]Deb K, Pratap A, Agarwal S, et al. A fast and elitist multiobjective genetic algorithm:NSGA-II[J]. Evolutionary Computation, IEEE Transactions on,2002,6(2):182-197.
[96]While L, Bradstreet L, Barone L. A fast way of calculating exact hypervolumes[J].Evolutionary Computation, IEEE Transactions on,2012,16(1):86-95.
[97]Johnson B W. Design and Analysis of Fault-tolerant Digital Systems[M].A.W. Publishing Company,1989:17-41.
[98]E.Alwagait, S.Ghandeharizadeh. DeW: A dependable Web services framework[J].Proceedings of14th International Workshop on Research Issues on Data Engineering.USA,2004.111-118.
[99]Deepal Jayasinghe. FAWS-A client-transparent fault tolerant system for SOAP-basedWeb services.[EB/OL].[2005.10].http://www-128.ibm.com/developerworks/Webservices/library/ws-faws/
[100] L. Deron, C.Fang, C. Chen, F. Lin. Fault-tolerant Web service[C]. Proceedings of10th Asia-Pacific Software Engineering Conference (APSWC'03). Chiang Mai,Thailand,2003.310-319.
[101] Yu T, Lin K J. Adaptive algorithms for finding replacement services inautonomic distributed business processes[C].Proc. of the7th InternationalSymposium on Autonomous Decentralized Systems (ISADS).2005.
[102] Liu Y, Ngu A H, Zeng L. QoS computation and policing in dynamic web serviceselection[C]. In Proceedings of the13th International Conference on World WideWeb (WWW), ACM Press, New York, USA,2004,66-73.
[103] Canfora G Penta M Di, Esposito R, et al. A lightweight approach for QoS-awareservice composition[C]. In Proc.2nd International Conference on Service OrientedComputing (ICSOC'04), New York, USA,2004,36-47.
[104] Jorge Cardoso, Amit P. Sheth, John A. Miller, Jonathan Arnold, KrysKochut.Quality of service for workflows and web service processes[J]. Journal ofWeb Semantics,2004,1(3):281-308.
[2] Roy W. Schulte,Yefim V. Natisy. Service Oriented Architectures,Part1and2[M].Garter Researeh Note SPA-401-068and SPA-401-069,1996.
[3] E.Neweomer,G. Lomov.Understanding SOA with WebServiees[M]. Addison-Wesley,2005.
[4] Niekull D. Service Oriented Architecture Whitepaper[M]. Whitepaper Adobe SystemsIncorporated,2005.
[5] BoraYurday. A service oriented reflective wireless middleware,Intl[C].conf.onService oriented ComPuting, ICSOC06,Chicago US,Springer LNCS4294,BerlinHeidelberg,2006.
[6] Ecabrera et al. Web Services Coordination(WS-Coordination)[M/OL]. August2002,http://www.ibm.com/developer works/library/ws-coor/
[7] Mike P PaPazoglou,Willem-Jan van den Heuvel. Service oriented architectures:approaches,technologies and research issues[J].The VLDB Joumal,2007,16(3):389-415.
[8] Alonso G, Casati F, et al. Web Services: Concepts, Architecture and Applications [M].London: Springer-Verlag,2004.
[9] Paolucci M, Kawamura T, et al. Importing the Semantic Web in UDDI [C],International Workshop on Web Services, E-Business, and the Semantic Web,2002,225-236.
[10]Klusch M. Semantic Web Service Coordination [M]//Schumacher M, Helin H,Schuldt H. CASCOM: Intelligent Service Coordination in the Semantic Web.Birkh user Basel.2008:59-104.
[11]Sioutas S, Sakkopoulos E, et al. Dynamic Web Service discovery architecture basedon a novel peer based overlay network [J]. The Journal of Systems and Software,2009,82(5):809-824.
[12]Li Y, Su S, Yang F. A Peer-to-Peer Approach to Semantic Web Services Discovery [C].The6-th International Conference on Computational Science, Reading, UK,2006,73-80.
[13]Liu S, Kungas P, Matskin M. Agent-Based Web Service Composition with JADE andJXTA [C].The2006International Conference on Semantic Web&Web Services, LasVegas, USA,2006,110-116.
[14]Haase P, Siebes R, van~Harmelen F. Expertise-based peer selection in Peer-to-Peernetworks [J]. Knowledge and Information Systems,2008,15(1):75-107.
[15]Loo B T, Huebsch R, et al. The Case for a Hybrid P2P Search Infrastructure [C]. The3rd International Workshop on P2P Systems, CA, USA,2004,141-150.
[16]Verma K, Sivashanmugam K, et al. METEOR-S WSDI: A Scalable P2P Infrastructureof Registries for Semantic Publication and Discovery of Web Services [J].Information Technology and Management,2005,6(1):17-39.
[17]Fernandez A, Hayes C, et al. Closing the Service Discovery Gap by CollaborativeTagging and Clustering Techniques [C]. Proceedings of the2nd InternationalWorkshop on Service Matchmaking and Resource Retrieval in the Semantic Web,Karlsruhe, Germany,2008, No.8.
[18]Wu C, Chang E. Aligning with the Web: an atom-based architecture for Web servicesdiscovery [J]. Service Oriented Computing and Applications,2007,1(2):97-116.
[19]Bose A, Nayak R, Bruza P. Improving Web Service Discovery by Using SemanticModels [C]. Proceedings of the9th international conference on Web InformationSystems Engineering, Auckland, New Zealand,2008,366-380.
[20]D'Mello D A, Ananthanarayana V S. A tree structure for web service compositions
[C]. Proceedings of the2nd Bangalore Annual Compute Conference, Bangalore, India,2009, No.18.
[21]Slavova S. Dynamic Selection Of Redundant Web Services [D]. Ms.D. Thesis.Saskatoon, Canada; University of Saskatchewan,2007.
[22]Stein S, Payne T R, Jennings N R. Flexible Provisioning of Web Service Workflows[J]. ACM Transactions on Internet Technology,2009,9(1):2.
[23]Li Y, Dong B. An Algorithm for Semantic Web Services Composition Based onOutput and Input Matching [M]//Wang W. Integration and Innovation Orient toE-Society Volume2. Springer.2008:297-307.
[24]Blankenburg B, Botelho L, et al. Service Composition [M]//Schumacher M, Helin H,Schuldt H. CASCOM: Intelligent Service Coordination in the Semantic Web.Birkh user Basel.2008:235-262.
[25]Karakoc E, Senkul P. Composing Semantic Web Services Under Constraints [J].Expert Systems with Applications,2009.doi:10.1016/j.eswa.2009.02.098.
[26]Hamadi R, Benatallah B. A Petri Net-based Model for Web Service Composition [C],Proceedings of the14th Australasian Database Conference, Adelaide, South Australia,2003,191-200.
[27]W3C.Web Services Architecture [M/OL].2003,http://www.w3.org/TR/ws-arch/
[28]Alonso G, Casati F, et al. Web Services: Concepts, Architecture and Applications [M].London: Springer-Verlag,2004.
[29]Wang H, Huang J, et al. Web services: problems and future directions [J]. Journal ofWeb Semantics,2004,1:309–320.
[30]Bray T,Paoli J,Sperberg-McQueen C M, Maler E.Extensible Markup Language (XML)1.0(Second Edition)[M/OL].2000,http://www.w3.org/TR/2000/REC-xml-20001006.
[31]W3C. Web services description language(WSDL) version2.0Part1,:Corelanguage[M/OL].2007,http://www.w3c.org/tr/REC-dl20-20070626
[32]UDDI. The UDDI technical white paper [M/OL].2002.http://uddi.org/pubs/uddi-v3.00-published-20020719.htm.
[33]T. Berners-Lee,J.Hendler,et al.The semantic Web[J]. Scientific American,2001,279(5):34-43.
[34]Martin D, Burstein M, et al. OWL-S: Semantic markup for web services [M/OL].W3C member submission,2004. http://www.w3.org/Submission/OWL-S/.
[35]de Bruijn J, Bussler C, et al. Web service modeling ontology (WSMO)[M/OL]. W3CMember Submission,2005. http://www.wsmo.org/.
[36]Battle S, Bernstein A, et al. Semantic Web service ontology (SWSO)[M/OL]. W3Cmember submission,2005. http://www.w3.org/Submission/SWSF-SWSO/.
[37]Klein M, Konig-Ries B, Mussig M. What is needed for semantic service descriptions-A proposal for suitable language constructs [J]. International Journal of Web GridService,2005,1(3-4):328-364.
[38]Akkiraju R, Farrell J, et al. Web service semantics-WSDL-S [M/OL]. W3C MemberSubmission,2005. http://www.w3.org/Submission/WSDL-S/.
[39]S. Ran. A model for Web services discovery with QoS [J]. ACM SIGecom Exchangs,2003,4(1):1-10.
[40]L.Z.Zeng,B. Benatallah, Anne H. H. Ngu, et al. QoS-aware middleware for Webservices composition [J]. IEEE Transactions on Software Engineering,2004,30(5):311-327.
[41]Wang Y, Vassileva J. Toward Trust and Reputation Based Web Service Selection: ASurvey [J]. International Transactions on Systems Science and Applications,2007,3(2):118-132.
[42]Bonabeau E,Dorigo M,Theraulaz G. Swarm Intelligence:From Natural to ArtificialSystem[M].New York: Oxford University Press,1999.
[43]Dorigo M, Bonabeau E, Theraulaz G. Ant algorithms and stigmergy[J].FutureGeneration Computer Systems,2000,16(9):851-871.
[44]Passino, K M，Biomimicry of bacterial foraging for distributed optimization andcontrol[J].IEEE Control Systems Magazine,2002,22(3):52-67.
[45]Eusuff M M, Lansey K E.Optimization of Water Distribution Network Design UsingShuffled Frog Leaping Algorithm[J]. Journal of Water Resources Planning andManagement，2003，129(3):210-225.
[46]Dervis Karaboga,Bahriye Akay.A comparative study of Artificial Bee ColonyAlgorithm[J].Applied Mathematics and Computation,2009,214:108-132.
[47]李晓磊，邵之江，钱积新.一种基于动物自治体的寻优模式:鱼群算法[J].系统工程理论与实践，2002,22(11):32-38.
[48]Kennedy J, Eberhart R C. Particle swarm optimization[C]. Proceeding Soft the IEEEInternational Conference on Neural Networks, Perth, Australia. Piscataway, NJ:IEEEPress,1995，1942-1948.
[49]Colorni A,Dorigo M,Maniezzo V,et al.Distributed Optimization by Ant colonies
[C].Proceedings of the1st European Conference on Artificial Life,1991:134-142.
[50]Solnon C. Ants Can Solve Constraint Satisfaction Problems[J]. IEEE Transactions onEvolutionary Computation,2002,6(4):347-357.
[51]Tavares Neto R F,Godinho Fiho M.An Ant Colony Optimization Approach to aPermutational Flowshop Scheduling Problem with Outsourcing allowed [J].Computers&Operations Research,2011,38(9):1286-1293.
[52]Mousa A A,Abd EI-Wahed W F,Rizk-Allah R M. A Hybrid Ant Colony OptimizationApproach Based Local Search Scheme for Multi-objective Design Optimizations[J].Electric Power Systems Research,2011,81(4):1014-1023.
[53]Samarghandi H,Taabayan P,Jahantigh F F.A Particle Swarm Optimization for theSingle Row Facility Layout Problem[J]. Computers&Industrial Engineering,2010,58(4):529-534.
[54]Wang H,Moon L,Yang S,et al. A Memetic Particle Swarm Optimization Algorithm forMultimode Optimization Problems[J]. Information Science,2012,197(15):38-52.
[55]Karaboga D. An Idea Based on Honey Bee Swarm for Numerical Optimization
[R].Technical Report-TR06, Erciyes. University, Engineering Faculty, ComputerEngineering Department,2005.
[56]J. Cardoso, J. Miller, A. Sheth, and J. Arnold. Quality of service for workflows andweb service processes[J]. Journal of Web Semantics,2004,1:281–308.
[57]D.A. Menascé, et al. On optimal service selection in Service Oriented Architectures[J]. Performance Evaluation,2010,67(8):659-675.
[58]E. Al-Masri, and Q.H.Mahmooud, Investigating Web Services on the World WideWeb[A],17th International Conference on World Wide Web (WWW,2008)[C].Beijing, April2008, pp.795-804.
[59]C.W. Zhang, S. Su, J. L. Chen. DiGA: Population diversity handling geneticalgorithm for QoS-aware web services selection[J]. Computer Communications,30(5):1082-1090,2007
[60]X.Q. Fan, X.W. Fang, C.J. Jiang. Research on Web service selection based oncooperative evolution[J]. Expert Systems with Applications,2011,38(8):9736-9743.
[61]夏亚梅，程渤等.基于改进蚁群算法的服务组合优化[J].计算机学报,2012，35(2):270-281.
[62]Rodriguez F J, Lozano M, García-Martínez C, et al. An artificial bee colonyalgorithm for the maximally diverse grouping problem[J]. Information Sciences,2013,230:183-196.
[63]Karaboga D, Akay B. A modified artificial bee colony (ABC) algorithm forconstrained optimization problems. Applied Soft Computing,2011,11(3):3021-3031.
[64]Pan Q K, Wang L, Mao K, et al. An effective artificial bee colony algorithm for areal-world hybrid flowshop problem in steelmaking process[J]. IEEE Transactions onAutomation Science and Engineering,2013,10(2):307-322.
[65]Pan Q K, Fatih Tasgetiren M, Suganthan P N, et al. A discrete artificial bee colonyalgorithm for the lot-streaming flow shop scheduling problem[J]. InformationSciences,2011,181(12):2455-2468.
[66]Mohammad A, Dimitrios S, Thomas R. Selecting skyline services for QoS based webservice composition[C]//WWW2010. Raleigh,2010:11-20.
[67]Adomavicius G, Tuzhilin A. Toward the next generation of recommender systems: Asurvey of the state-of-the-art and possible extensions[J]. Knowledge and DataEngineering, IEEE Transactions on.2005,17(6):734-749.
[68]Goldberg D, Nichols D, Oki B.M, Terry D. Using collaborative filtering to weave aninformation Tapestry[J]. Communications of the ACM,1992,35(12):61-70.
[69]Resnick P, lacovou N, Suchak M,et al. Grouplens: An open architecture forcollaborative filtering of netnews[C]. Proceedings of the1994ACM conference onComputer supported cooperative work,1994:175-186.
[70]Shardanand U, Maes P. Social information filtering: algorithms for automating “wordof mouth”[C]. Proceedings of the SIGCHI conference on Human factors incomputing systems,1995. ACM Press/Addison-Wesley Publishing Co.,1995:210-217.
[71]Adomavicius G,Tuzhilin A.Towards the Next Generation of RecommenderSystems:A Survey of the State-of-the-Art and Possible Extensions [J]. IEEETransactions on Knowledge and Data Engineering,2005,17(6):734-749.
[72]Sarwar B,Karypis G, Konstan J, et al. Item-Based Collaborative FilteringRecommendation Algorithms[C]. In Proceedings of the10th Int’1World Wide WebConf. New York: ACM Press,2001,285-295.
[73]Sarwar B,Karypis G,Konstan J,et al.Analysis of Recommendation Algorithms forE-commerce[C]. In Proceedings of the2ndACM Conf. On Electronic Commerce.NewYork: ACM Press,2001,158-167.
[74]Koren Y. Factor in the neighbors:Scalable and accurate collaborative filtering[J].ACM Transactions on Knowledge Discovery from Data,2010,4(1):1-24.
[75]Huang Z, Chen H，Zeng D. Applying associative retrieval techniques to alleviate thesparsity problem in collaborative filtering [J].ACM Transactions on InformationSystems,2004，22(1):116-142.
[76]ROBIN B. Hybrid recommender systems: survey and experiments [J]. User Modelingand User-Adapted Interaction,2002,12(4):331-370.
[77]Breese J S, Heckertnan D, Kadie C. Empirical analysis of predictive algorithms forcollaborative filtering[C]. Proceedings of the14th conference on Uncertainty inArtificial Intelligence,1998:43-52.
[78]Bell R,Koren Y,Volinsky C. The BellKor2008Solution to the Netflix Prize.2008.http://www.netflixprize.com/community/viewtopic.php?id=1193.
[79]Yu C,Xu,J R, Du X Y.Recommendation algorithm combining the user-basedclassified regression and the item-based filtering[C]. Proceedings of the8thlnternational Conference on Electronic Commerce: The New E-commercelnnovations for Conquering Current Barriers, Obstacles and Limitations toConducting Successful Business on the lnternet. New York:ACM Press,2006:574-578.
[80]Sarwar B,Karypis G,Konstan,J，et al.Analysis of recommendation algoritlims fore-commerce[C].Proceedings of the2ndACM Conference on electronic Commerce.New York:ACM Press,2000:158-167.
[81]Sarwar B, Karypis G, Konstan J, et al. Item-based collaborative filteringrecommendation algorithms[C]. Proceedings of the10th international conference onWorld Wide Web,2001:285-295.
[82]Wang J, De Vries A P, Reinders M J T. Unifying user-based and item-basedcollaborative filtering approaches by similarity fusion[C]. Proceedings of the29thannual international ACM SIGIR conference on Research and development ininformation retrieval,2006:501-508.
[83]J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl. Evaluating collaborativefiltering recommender systems[J].ACM Transactions on Information Systems,2004,22(1):5-53.
[84]Shannon C.E. The mathematical theory of communication[J]. The Bell SystemTechnical Journal,1948, Vol.27,379-423.
[85]Ouzzani, Bouguettaya. Efficient access to Web services[j]. IEEE Internet Computing.2004,8(2):34-44.
[86]Yin Y, Zhang B, Zhang X Z. An active and opportunistic service replacementalgorithm orienting transactional composite service dynamic adaptation[J]. JisuanjiXuebao(Chinese Journal of Computers),2010,33(11):2147-2162.
[87]Yan Gao, Shang-xin Zhang, Bin Zhang, et al. SOA Based Web Services CompositionSystem[J]. Journal of Chinese Computer System,2007,28(4):729-733.
[88]J.H.Holland.Adaptation in Natural and Artificial Systems[M].University of MichiganPress,1975.
[89]Goldberg D E. Genetic Algorithms in Search, Optimization and Machine Learning
[M]. Addison-Wesley,1989.
[90]Cardellini V, Casalicchio E, Grassi V, et al. Moses: A framework for qos drivenruntime adaptation of service-oriented systems[J]. IEEE Transactions on SoftwareEngineering, vol.38, no.5, Sept-Oct2012, pp.1138-1159.
[91]Yu T, Zhang Y, Lin K J. Efficient algorithms for Web services selection withend-to-end QoS constraints[J]. ACM Transactions on the Web (TWEB),2007,1(1):6.
[92]Calinescu R, Grunske L, Kwiatkowska M, et al. Dynamic QoS management andoptimization in service-based systems[J]. Software Engineering, IEEE Transactionson,2011,37(3):387-409.
[93]Ming-wei Z, Bin Z, Xin-zhe Z, et al. A Division Based Composite Service SelectionApproach[J]. Journal of Computer Research and Development,2012,49(5):1005-1017.
[94]Wada H, Suzuki J, Yamano Y, et al. E3: A Multiobjective Optimization Framework forSLA-Aware Service Composition[J]. Services Computing, IEEE Transactions on,2012,5(3):358-372.
[95]Deb K, Pratap A, Agarwal S, et al. A fast and elitist multiobjective genetic algorithm:NSGA-II[J]. Evolutionary Computation, IEEE Transactions on,2002,6(2):182-197.
[96]While L, Bradstreet L, Barone L. A fast way of calculating exact hypervolumes[J].Evolutionary Computation, IEEE Transactions on,2012,16(1):86-95.
[97]Johnson B W. Design and Analysis of Fault-tolerant Digital Systems[M].A.W. Publishing Company,1989:17-41.
[98]E.Alwagait, S.Ghandeharizadeh. DeW: A dependable Web services framework[J].Proceedings of14th International Workshop on Research Issues on Data Engineering.USA,2004.111-118.
[99]Deepal Jayasinghe. FAWS-A client-transparent fault tolerant system for SOAP-basedWeb services.[EB/OL].[2005.10].http://www-128.ibm.com/developerworks/Webservices/library/ws-faws/
[100] L. Deron, C.Fang, C. Chen, F. Lin. Fault-tolerant Web service[C]. Proceedings of10th Asia-Pacific Software Engineering Conference (APSWC'03). Chiang Mai,Thailand,2003.310-319.
[101] Yu T, Lin K J. Adaptive algorithms for finding replacement services inautonomic distributed business processes[C].Proc. of the7th InternationalSymposium on Autonomous Decentralized Systems (ISADS).2005.
[102] Liu Y, Ngu A H, Zeng L. QoS computation and policing in dynamic web serviceselection[C]. In Proceedings of the13th International Conference on World WideWeb (WWW), ACM Press, New York, USA,2004,66-73.
[103] Canfora G Penta M Di, Esposito R, et al. A lightweight approach for QoS-awareservice composition[C]. In Proc.2nd International Conference on Service OrientedComputing (ICSOC'04), New York, USA,2004,36-47.
[104] Jorge Cardoso, Amit P. Sheth, John A. Miller, Jonathan Arnold, KrysKochut.Quality of service for workflows and web service processes[J]. Journal ofWeb Semantics,2004,1(3):281-308.