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Correlation-aware QoS modeling and manufacturing cloud service composition

Author

Listed:
  • Hong Jin

    (South China University of Technology)

  • Xifan Yao

    (South China University of Technology)

  • Yong Chen

    (South China University of Technology)

Abstract

Recently, cloud manufacturing has attracted much attention from both academic and industry communities. Manufacturing cloud service composition and optimization is critical to the optimal resources allocation in cloud manufacturing. Since there are many manufacturing cloud services available with similar functions but different quality of service (QoS), and with potential quality correlations among them, such correlations must to be considered for manufacturing cloud service composition. In this paper, a correlation-aware manufacturing cloud service description model is presented to characterize the QoS dependence of an individual service on other related services. Based on such a model, a service correlation mapping model is proposed for getting correlation QoS values among services automatically. In addition, an effective approach for the correlation-aware optimal service selection is proposed based on a genetic algorithm. A case study indicates that services composition of higher quality can be obtained when such correlations are considered. And the effectiveness and efficiency of the proposed approach are demonstrated via simulation studies.

Suggested Citation

  • Hong Jin & Xifan Yao & Yong Chen, 2017. "Correlation-aware QoS modeling and manufacturing cloud service composition," Journal of Intelligent Manufacturing, Springer, vol. 28(8), pages 1947-1960, December.
    • Handle: RePEc:spr:joinma:v:28:y:2017:i:8:d:10.1007_s10845-015-1080-2
    DOI: 10.1007/s10845-015-1080-2
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    References listed on IDEAS

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    1. Tao, Fei & Zhao, Dongming & Yefa, Hu & Zhou, Zude, 2010. "Correlation-aware resource service composition and optimal-selection in manufacturing grid," European Journal of Operational Research, Elsevier, vol. 201(1), pages 129-143, February.
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    Cited by:

    1. Asma Talhi & Virginie Fortineau & Jean-Charles Huet & Samir Lamouri, 2019. "Ontology for cloud manufacturing based Product Lifecycle Management," Journal of Intelligent Manufacturing, Springer, vol. 30(5), pages 2171-2192, June.
    2. Hao Li & Shanghua Mi & Qifeng Li & Xiaoyu Wen & Dongping Qiao & Guofu Luo, 2020. "A scheduling optimization method for maintenance, repair and operations service resources of complex products," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1673-1691, October.
    3. Yu Feng & Biqing Huang, 2020. "Cloud manufacturing service QoS prediction based on neighbourhood enhanced matrix factorization," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1649-1660, October.
    4. Yinan Wu & Gongzhuang Peng & Hongwei Wang & Heming Zhang, 2019. "A Heuristic Algorithm for Optimal Service Composition in Complex Manufacturing Networks," Complexity, Hindawi, vol. 2019, pages 1-20, April.
    5. Ali Salmasnia & Zahra Kiapasha & Melika Pashaeenejad, 2024. "Subtasks scheduling of tasks with different structures in cloud manufacturing systems under maintenance policy and focusing on logistics, tardiness, and earliness aspects," Operational Research, Springer, vol. 24(3), pages 1-37, September.
    6. Baodong Li & Yu Yang & Jiafu Su & Zhichao Liang & Sheng Wang, 2020. "Two-sided matching decision-making model with hesitant fuzzy preference information for configuring cloud manufacturing tasks and resources," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 2033-2047, December.
    7. Yankai Wang & Shilong Wang & Bo Yang & Bo Gao & Sibao Wang, 2022. "An effective adaptive adjustment method for service composition exception handling in cloud manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 735-751, March.
    8. Tianyang Li & Ting He & Zhongjie Wang & Yufeng Zhang, 2020. "SDF-GA: a service domain feature-oriented approach for manufacturing cloud service composition," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 681-702, March.

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