Constraint-based QoS-aware Web Service Composition in Cross-organizational Collaboration

doi:10.19678/j.issn.1000-3428.0050812

计算机工程 ›› 2018, Vol. 44 ›› Issue (11): 67-75. doi: 10.19678/j.issn.1000-3428.0050812

Constraint-based QoS-aware Web Service Composition in Cross-organizational Collaboration

TAN Wenan1,ZHAO Yao2,JIN Ting2

1.School of Computer and Information Engineering,Shanghai Polytechnic University,Shanghai 201209,China; 2.School of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China

收稿日期:2018-03-16 出版日期:2018-11-15 发布日期:2018-11-15
作者简介:TAN Wenan(1965—),male,professor,doctor,main research direction is collaborative computing;ZHAO Yao,JIN Ting,master.
基金资助:
National Natural Science Fund of China (61672022,61272036);Graduate Innovation Program of Shanghai Polytechnic University (A01GY17F022);Key Discipline of Shanghai Polytechnic University(XXKZD1604).

Constraint-based QoS-aware Web Service Composition in Cross-organizational Collaboration

TAN Wenan ¹,ZHAO Yao ²,JIN Ting ²

1.School of Computer and Information Engineering,Shanghai Polytechnic University,Shanghai 201209,China; 2.School of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China

Received:2018-03-16 Online:2018-11-15 Published:2018-11-15
Supported by:
National Natural Science Fund of China (61672022,61272036);Graduate Innovation Program of Shanghai Polytechnic University (A01GY17F022);Key Discipline of Shanghai Polytechnic University(XXKZD1604).

摘要/Abstract

摘要：

To improve the Quality of Service (QoS)-aware Web service compositions considering constraints between cross-organizational business,this paper analyzes the types of constraints,and accordingly proposes a Chaos Genetic Algorithm (CGA).The algorithm creates an initial population of service compositions based on the chaos theory,and then processes individuals which violate constraints in the initial population using repair strategies.Next,a new fitness function is designed to gradually eliminate the infeasible compositions generated in evolution.Finally,the algorithm makes minor chaotic disturbances on the evolved group to accelerate convergence and avoid local optimum.Experimental results demonstrate the effectiveness of the proposed algorithm.

关键词: cross-organizational, chaos, genetic algorithm, Quality of Service (QoS), Web service composition, constraint, disturbance

Abstract:

Key words: cross-organizational, chaos, genetic algorithm, Quality of Service (QoS), Web service composition, constraint, disturbance

中图分类号:

TP312

TAN Wenan,ZHAO Yao,JIN Ting. Constraint-based QoS-aware Web Service Composition in Cross-organizational Collaboration[J]. 计算机工程, 2018, 44(11): 67-75.

TAN Wenan,ZHAO Yao,JIN Ting. Constraint-based QoS-aware Web Service Composition in Cross-organizational Collaboration[J]. Computer Engineering, 2018, 44(11): 67-75.

http://www.ecice06.com/CN/Y2018/V44/I11/67

参考文献

［1］SUN H,HUANG S,FAN Y.SOA-based collaborative modeling method for cross-organizational business process integration［J］.Lecture Notes in Computer Science,2007,4537:522-527.
［2］CASATI F,SHAN M C.Dynamic and adaptive composition of e-services［J］.Information Systems,2001,26(3):143-163.
［3］HAN X,LIU Y,XU B,et al.A survey on QoS-aware dynamic web service selection［C］//Proceedings of the 7th International Conference on Wireless Communications,Networking and Mobile Computing.Washington D.C.,USA:IEEE Press,2011:1-5.
［4］FU X,BULTAN T,SU J.Analysis of interacting BPEL Web services［C］//Proceedings of the 13th International Conference on World Wide Web.New York,USA:ACM Press,2004:621-630.
［5］LIU L,ZSU M T.QoS-based Web services composi-tion［J］.Discrete Applied Mathematics,2009,196(C):100-114.
［6］LAN C W,CHEN R C S,SU A Y S,et al.A multiple objectives optimization approach for QoS-based web services compositions［C］//Proceedings of IEEE International Conference on e-Business Engineering.Washington D.C.,USA:IEEE Press,2009:121-128.
［7］CHEN M,YAN Y.Redundant service removal in QoS-aware service composition［C］//Proceedings of IEEE the 19th International Conference on Web Services.Washington D.C.,USA:IEEE Press,2012:431-439.
［8］GOLDBERG D E.Genetic algorithms in search,optimization and machine learning［M］.Boston,USA:Addison-Wesley Pub.Co.,1989:2104-2116.
［9］JIAQIANG E,WANG C,WANG Y,et al.A new adaptive mutative scale chaos optimization algorithm and its application［J］.Journal of Control Theory and Applications,2008,6(2):141-145.
［10］JIN H,YAO X,CHEN Y.Correlation-aware QoS modeling and manufacturing cloud service composition［J］.Journal of Intelligent Manufacturing,2017,28(8):1947-1960.
［11］GAO A,YANG D,TANG S,et al.QoS-driven web service composition with inter service conflicts［C］//Proceedings of Asia-Pacific Web Conference on Frontiers of WWW Research and Development.Berlin,Germany:Springer-Verlag Press,2006:121-132.
［12］LI Y,WEN T.Quality and relation driven service selection for Web services composition［C］//Proceedings of International Conference on New Trends in Information and Service Science.Washington D.C.,USA:IEEE Press,2009:152-155.
［13］AI L,TANG M.A penalty-based genetic algorithm for QoS-aware Web service composition with inter-service dependencies and conflicts［C］//Proceedings of International Conference on Computational Intelligence for Modelling Control & Automation.Washington D.C.,USA:IEEE Press,2008:738-743.
［14］AI L,TANG M.QoS-based Web service composition accommodating inter-service dependencies using minimal-conflict hill-climbing repair genetic algorithm［C］//Proceedings of IEEE the 4th International Conference on eScience.Washington D.C.,USA:IEEE Press,2008:119-126.
［15］WU Y,WANG X.Applying multi-objective genetic algorithms to QoS-aware Web service global selection［J］.Advances in Information Sciences and Service Sciences,2011,3(11):134-144.
［16］TANG M,AI L.A hybrid genetic algorithm for the optimal constrained Web service selection problem in web service composition［C］//Proceedings of 2010 IEEE Congress on Evolutionary Computation.Washington D.C.,USA:IEEE,2010:1-8.
［17］YOUSEFI M,YOUSEFI M,FERREIRA R P M,et al.Chaotic genetic algorithm and Adaboost ensemble metamodeling approach for optimum resource planning in emergency departments［J］.Artificial Intelligence in Medicine,2017,84:23-33.
［18］DOGAN S.A new data hiding method based on chaos embedded genetic algorithm for color image［J］.Artificial Intelligence Review,2016,46(1):129-143.
［19］RU M,YIN S,QU Z.Power and spectrum allocation in D2D networks based on coloring and chaos genetic algorithm［J］.Procedia Computer Science,2016,107:183-189.
［20］SNASELOVA P,ZBORIL F.Genetic algorithm using theory of chaos［J］.Procedia Computer Science,2015,51(1):316-325.
［21］HE W,WANG E,XIONG T.Intelligent face recognition based on manifold learning and genetic-chaos algorithm optimized kernel extreme learning machine［J］.Journal of Communications,2013,8(10):658-664.
［22］MA Z.Chaotic populations in genetic algorithms［J］.Applied Soft Computing Journal,2012,12(8):2409-2424.
［23］ZHANG C,MA Y.Genetic algorithm for QoS-aware Web service selection based on chaotic sequences［C］//Proceedings of International Conference on Network-Based Information Systems.Washington D.C.,USA:IEEE Press,2009:410-416.
［24］CHARFI A,MEZINI M.Hybrid Web service composition:business processes meet business rules［C］//Proceedings of International Conference on Service Oriented Computing.New York,USA:ACM Press,2004:30-38.
［25］ORRIENS B,YANG J,PAPAZOGLOU M P.A framework for business rule driven service composition［C］//Proceedings of Conceptual Modeling for Novel Application Domains.Berlin,Germany:Springer-Verlag Press,2003:52-64.
［26］VERMA K,AKKIRAJU R,GOODWIN R,et al.On accommodating inter service dependencies in web process flow composition［C］//Proceedings of AAAI Spring Symposium on Semantic Web Services.［S.l.］ AAAI Press,2004:37-43.
［27］PAN W,SUN Y,JING Y.Artillery firepower selection based on chaos genetic algorithm［C］//Proceedings of Control and Decision Conference.Washington D.C.,USA:IEEE Press,2016:1588-1593.
［28］PHATAK S C,RAO S S.Logistic map:a possible random-number generator［J］.Physical Review E,1995,51(4):3670.
［29］CAMBEL A B.Applied chaos theory - A paradigm for complexity［M］.Pittsburgh,USA:Academic Press,1993.
［30］BAKHSHI M,HASHEMI M.User-centric optimization for constraint Web service composition using a fuzzy-guided genetic algorithm system［EB/OL］.［2018-2-28］.http://cn.arxiv.org/ftp/arxiv/papers/1210/1210.3604.pdf.
［31］ARDAGNA D,PERNICI B.Global and local QoS guarantee in web service selection［C］//Proceedings of International Conference on Business Process Management.Berlin,Germany: Springer,2005:32-46.
［32］CARDOSO J,SHETH A,MILLER J,et al.Quality of service for workflows and Web service processes［J］.Web Semantics:Science,Services and Agents on the World Wide Web,2004,1(3):281-308.
［33］Gao J,Xiao M,Zhang W.A rapid chaos genetic algorithm［C］//Proceedings of International Conference in Swarm Intelligence.Berlin,Germany:Springer,2010:425-431.
［34］AL-MASRI E,MAHMOUD Q H.QoS-based discovery and ranking of Web services［C］//Proceedings of International Conference on Computer Communications and Networks.Washington D.C.,USA:IEEE Press,2007:529-534.

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