A clustering network-based approach to service composition in cloud manufacturing

Cloud manufacturing (CMfg) is a new business paradigm that aims to provide manufacturing resources as services over the Internet in a convenient pay-as-you-go mode. Service composition is a critical means for achieving value adding and synergy of resources in CMfg. There are a vast number of services in a CMfg platform, which makes traditional service composition methods incapable and low efficiency in solving the problem of service composition. Most of existing research on CMfg was devoted to the optimal selection of services under a specific composition flow and a group of given candidate pools. Hence, there is a need to explore effective approaches to obtaining service composition flow and candidate sets. The network-based web service composition approach has recently drawn much attention and has been proved effective in coping with the challenge of large-scale services. Inspired by this, a novel approach called service clustering network-based service composition is proposed in this paper. In this method, services are first clustered into abstract services, and then a clustering network of the abstract services is established. In this way, service composition paths and corresponding candidate sets for fulfilling manufacturing tasks can be quickly obtained without requiring task decomposition, which decreases the difficulty and improves efficiency of service composition. Effectiveness and efficiency of the approach are verified through simulation experiments.

[1]  Gang Ma,et al.  Study on multi-task oriented services composition and optimisation with the ‘Multi-Composition for Each Task’ pattern in cloud manufacturing systems , 2013, Int. J. Comput. Integr. Manuf..

[2]  Hui Wang,et al.  Service network: An infrastructure of web services , 2009, 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems.

[3]  Samir Tata,et al.  Functionality-Driven Clustering of Web Service Registries , 2010, 2010 IEEE International Conference on Services Computing.

[4]  Mehrdad Jalali,et al.  An Optimized Semantic Web Service Composition Method Based on Clustering and Ant Colony Algorithm , 2014, ArXiv.

[5]  Zhang Li,et al.  Modeling framework for product lifecycle information , 2010 .

[6]  Chi-Chun Lo,et al.  Fuzzy Similarity Clustering for Consumer-Centric QoS-Aware Selection of Web Services , 2009, 2009 International Conference on Complex, Intelligent and Software Intensive Systems.

[7]  Dechen Zhan,et al.  Cloud manufacturing service composition based on QoS with geo-perspective transportation using an improved Artificial Bee Colony optimisation algorithm , 2015 .

[8]  Markus Buschle,et al.  Enterprise architecture availability analysis using fault trees and stakeholder interviews , 2014, Enterp. Inf. Syst..

[9]  Soundar R. T. Kumara,et al.  Effective Web Service Composition in Diverse and Large-Scale Service Networks , 2008, IEEE Transactions on Services Computing.

[10]  Patrick Martin,et al.  Clustering WSDL Documents to Bootstrap the Discovery of Web Services , 2010, 2010 IEEE International Conference on Web Services.

[11]  Fuyuki Ishikawa,et al.  QoS-Aware Automatic Service Composition by Applying Functional Clustering , 2011, 2011 IEEE International Conference on Web Services.

[12]  Lei Ren,et al.  A modeling and description method of multidimensional information for manufacturing capability in cloud manufacturing system , 2013 .

[13]  Lei Ren,et al.  Cloud manufacturing: a new manufacturing paradigm , 2014, Enterp. Inf. Syst..

[14]  Fei Tao,et al.  An Evolving Web Service Interaction Network Model , 2014 .

[15]  F. Tao,et al.  Cloud Manufacturing , 2011 .

[16]  Baghdad Atmani,et al.  Applying CBR Over an AI Planner for Dynamic Web Service Composition , 2013, Int. J. Inf. Technol. Web Eng..

[17]  Djamil Aïssani,et al.  Semantic annotations for web services discovery and composition , 2009, Comput. Stand. Interfaces.

[18]  Fei Tao,et al.  Modelling of combinable relationship-based composition service network and the theoretical proof of its scale-free characteristics , 2012, Enterp. Inf. Syst..

[19]  Elad Harison,et al.  Critical Success Factors of Business Intelligence System Implementations: Evidence from the Energy Sector , 2012, Int. J. Enterp. Inf. Syst..

[20]  Zibin Zheng,et al.  A Clustering-Based QoS Prediction Approach for Web Service Recommendation , 2012, 2012 IEEE 15th International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing Workshops.

[21]  Fei Tao,et al.  Research on measurement method of resource service composition flexibility in service-oriented manufacturing system , 2012, Int. J. Comput. Integr. Manuf..

[22]  Xu Cheng Method for complex product collaborative design based on cloud service , 2011 .

[23]  Zakaria Maamar,et al.  Using Social Networks for Web Services Discovery , 2011, IEEE Internet Computing.

[24]  Lifeng Ai,et al.  A hybrid genetic algorithm for the optimal constrained web service selection problem in web service composition , 2010, IEEE Congress on Evolutionary Computation.

[25]  Bo Liu,et al.  A conceptual framework for dynamic manufacturing resource service composition and optimization in service-oriented networked manufacturing , 2011, 2011 International Conference on Cloud and Service Computing.

[26]  Lei Ren,et al.  Cloud manufacturing: from concept to practice , 2015, Enterp. Inf. Syst..

[27]  Chai Xu-dong,et al.  Cloud manufacturing:a new service-oriented networked manufacturing model , 2010 .

[28]  Lei Wang,et al.  Research on the Clustering and Composition of P2P-Based Web Services , 2009, 2009 2nd International Conference on Biomedical Engineering and Informatics.

[29]  van der Wmp Wil Aalst,et al.  On scientific workflow , 2007 .

[30]  Dhiah Al-Shammary,et al.  Clustering SOAP Web Services on Internet Computing Using Fast Fractals , 2011, 2011 IEEE 10th International Symposium on Network Computing and Applications.

[31]  Jano I. van Hemert,et al.  Scientific Workflow: A Survey and Research Directions , 2007, PPAM.

[32]  Martha Palmer,et al.  Verb Semantics and Lexical Selection , 1994, ACL.

[33]  Jean François Santucci,et al.  A Community Based Algorithm for Large Scale Web Service Composition , 2013, ArXiv.

[34]  Fuyuki Ishikawa,et al.  Towards network-aware service composition in the cloud , 2012, WWW.

[35]  Jean François Santucci,et al.  A Comparative Study of Web Services Composition Networks , 2012, 2012 Eighth International Conference on Signal Image Technology and Internet Based Systems.

[36]  Fei Tao,et al.  An Extensible Model for Multitask-Oriented Service Composition and Scheduling in Cloud Manufacturing , 2016, Journal of Computing and Information Science in Engineering.

[37]  Yan Li,et al.  The Research of Service Network Based on Complex Network , 2010, 2010 International Conference on Service Sciences.