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Dynamic Model for Service Composition and Optimal Selection in Cloud Manufacturing Environment

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Recent Advances in Intelligent Manufacturing (ICSEE 2018, IMIOT 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 923))

Abstract

A classification model is proposed to allocate, search and match services in cloud environment for Service Composition and Optimal Selection (SCOS). Unlike cloud computing, the services in cloud manufacturing (CMfg) include real time manufacturing resources besides computing services, which makes CMfg environment complicated for allocation of services to the respective tasks. Thus, problem is not having adequate tools for the fast and effective searching and allocation of services for implementation of SCOS. The method described in this paper is to achieve SCOS by organizing the services by an approach named pedigree. For this method to be applied, calculation of semantic cosine distance along with analyzing relationship between different services are required to support the collaboration for managing and matching services in pedigree. Examples are done for service registration along with searching and selecting the services which shows this method to be effective for service composition and optimal selection.

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Acknowledgement

The research work was granted by the National Natural Science Foundation, China. (No. 71501020).

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Authors and Affiliations

  1. School of Mechanical Engineering, Chongqing University, Chongqing, 400044, China

    Jawad Ul Hassan, Peihan Wen, Pan Wang & Qian Zhang

  2. The State Key Laboratory of Mechanical Transmission, School of Mechanical Engineering, Chongqing University, Chongqing, 400044, China

    Farrukh Saleem & M. Usman Nisar

Authors
  1. Jawad Ul Hassan
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  2. Peihan Wen
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  3. Pan Wang
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  4. Qian Zhang
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  5. Farrukh Saleem
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  6. M. Usman Nisar
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Corresponding author

Correspondence to Peihan Wen .

Editor information

Editors and Affiliations

  1. Chongqing University, Chongqing, China

    Shilong Wang

  2. Queen’s University Belfast, Belfast, UK

    Mark Price

  3. Chongqing University, Chongqing, China

    Ming K. Lim

  4. Queen’s University Belfast, Belfast, UK

    Yan Jin

  5. Chongqing University, Chongqing, China

    Yuanxin Luo

  6. Chongqing University, Chongqing, China

    Rui Chen

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Ul Hassan, J., Wen, P., Wang, P., Zhang, Q., Saleem, F., Nisar, M.U. (2018). Dynamic Model for Service Composition and Optimal Selection in Cloud Manufacturing Environment. In: Wang, S., Price, M., Lim, M., Jin, Y., Luo, Y., Chen, R. (eds) Recent Advances in Intelligent Manufacturing . ICSEE IMIOT 2018 2018. Communications in Computer and Information Science, vol 923. Springer, Singapore. https://doi.org/10.1007/978-981-13-2396-6_5

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  • DOI: https://doi.org/10.1007/978-981-13-2396-6_5

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2395-9

  • Online ISBN: 978-981-13-2396-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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