SDF-GA: a service domain feature-oriented approach for manufacturing cloud service composition

  • Tianyang Li
  • Ting HeEmail author
  • Zhongjie Wang
  • Yufeng Zhang


Cloud manufacturing (CMfg) is a new service-oriented manufacturing paradigm in which shared resources are integrated and encapsulated as manufacturing services. When a single service is not able to meet some manufacturing requirement, a composition of multiple services is then required via CMfg. Service composition and optimal selection (SCOS) is a key technique for creating an on-demand quality of service (QoS)-optimal efficient manufacturing service composition to satisfy various user requirements. Given the number of services with the same functionality and a similar level of QoS, SCOS has been seen as a key challenge in CMfg research. One effective approach to solving SCOS problems is to use service domain features (SDF) through investigating the probability of services being used for a specific requirement from multiple perspectives. The approach can result in a division of the service space and then help streamline the service space with large-scale candidate services. The approach can also search for optimal subspaces that most likely contribute to an overall optimal solution. Accordingly, this paper develops an SDF-oriented genetic algorithm to effectively create a manufacturing service composition with large-scale candidate services. Fine-grained SDF definitions are developed to divide the service space. SDF-based optimization strategies are adopted. The novelty of the proposed algorithm is presented based on Bayes’ theorem. The effectiveness of the proposed algorithm is validated by solving three real-world SCOS problems in a private CMfg.


Service domain features (SDFs) Service composition and optimal selection (SCOS) Cloud manufacturing (CMfg) Manufacturing cloud service composition Genetic algorithm (GA) 



This work has been supported in part by the research projects the National Natural Science Foundation of China (NSFC) (No. 71571056) and the Scientific Research Funds of Huaqiao University (16BS304).


  1. Bravo, M. (2014). Similarity measures for web service composition models. International Journal on Web Service Computing, 5, 495–505.Google Scholar
  2. Chen, F., Dou, R., Li, M., & Wu, H. (2016a). A flexible QoS-aware Web service composition method by multi-objective optimization in cloud manufacturing. Computers & Industrial Engineering, 99, 423–431.Google Scholar
  3. Chen, R., Guo, J., & Bao, F. (2016b). Trust management for SOA-based IoT and its application to service composition. IEEE Transactions on Services Computing, 9(3), 482–495.Google Scholar
  4. Fatahi Valilai, O., & Houshmand, M. (2014). A platform for optimisation in distributed manufacturing enterprises based on cloud manufacturing paradigm. International Journal of Computer Integrated Manufacturing, 27(11), 1031–1054.Google Scholar
  5. Hua, G., Zhang, L., Liu, Y., Tao, F., Shu, M., & Mu, S. (2014). A discovery method of service-correlation for service composition in virtual enterprise. European Journal of Industrial Engineering, 8(5), 579–618.Google Scholar
  6. Huang, J., Li, S., Duan, Q., Yu, R., & Yu, S. (2016). QoS correlation-aware service composition for unified network-cloud service provisioning. In Global communications conference (GLOBECOM), 2016 IEEE (pp. 1–6). IEEE.Google Scholar
  7. Huang, B., Li, C., & Tao, F. (2014). A chaos control optimal algorithm for QoS-based service composition selection in cloud manufacturing system. Enterprise Information Systems, 8(4), 445–463.Google Scholar
  8. Jiang, Y. Z., Hao, Z. F., Zhang, Y. S., Huang, H., Wang, Y. L., & He, H. J. (2014). Bayesian forecasting evolutionary algorithm. Chinese Journal of Computers, 37(8), 1846–1858.Google Scholar
  9. Jin, H., Yao, X., & Chen, Y. (2017). Correlation-aware QoS modeling and manufacturing cloud service composition. Journal of Intelligent Manufacturing, 28(8), 1947–1960.Google Scholar
  10. Kai, C., Guohu, C., & Hua, J. (2014). Guided self-adaptive evolutionary genetic algorithm. Journal of Electronics & Information Technology, 36(8), 1884–1890.Google Scholar
  11. Karim, R., Ding, C., & Miri, A. (2015). End-to-end QoS prediction of vertical service composition in the cloud. In 2015 IEEE 8th international conference on cloud computing (CLOUD) (pp. 229–236). IEEE.Google Scholar
  12. Kubler, S., Holmström, J., Främling, K., & Turkama, P. (2016). Technological theory of cloud manufacturing. Service orientation in holonic and multi-agent manufacturing. Berlin: Springer.Google Scholar
  13. Lemos, A. L., Daniel, F., & Benatallah, B. (2016). Web service composition: A survey of techniques and tools. ACM Computing Surveys (CSUR), 48(3), 33.Google Scholar
  14. Li, B. H., Zhang, L., Wang, S. L., Tao, F., Cao, J. W., JiangXD, Song X, et al. (2010). Cloud manufacturing: A new service-oriented networked manufacturing model. Computer Integrated Manufacturing Systems, 16(1), 1–16.Google Scholar
  15. Liu, J., Hao, S., Zhang, X., Wang, C., Sun, J., Yu, H., & Li, Z. (2016). Research on web service dynamic composition based on execution dependency relationship. In 2016 IEEE world congress on services (SERVICES) (pp. 113–117). IEEE.Google Scholar
  16. Liu, Z., & Xu, X. (2014). S-ABC-A Service-oriented artificial bee colony algorithm for global optimal services selection in concurrent requests environment. In 2014 IEEE international conference on web services (ICWS) (pp. 503–509). IEEE.Google Scholar
  17. Lu, Y., & Xu, X. (2017). A semantic web-based framework for service composition in a cloud manufacturing environment. Journal of Manufacturing Systems, 42, 69–81.Google Scholar
  18. Morgan, J., & O’Donnell, G. E. (2017). Enabling a ubiquitous and cloud manufacturing foundation with field-level service-oriented architecture. International Journal of Computer Integrated Manufacturing, 30(4–5), 442–458.Google Scholar
  19. Pisching, M. A., Junqueira, F., Filho, D. J. S., & Miyagi, P. E. (2015). Service composition in the cloud-based manufacturing focused on the industry 4.0. Technological innovation for cloud-based engineering systems. Berlin: Springer.Google Scholar
  20. Ren, L., Zhang, L., Wang, L., et al. (2017). Cloud manufacturing: Key characteristics and applications[J]. International Journal of Computer Integrated Manufacturing, 30(6), 501–515.Google Scholar
  21. Seghir, F., & Khababa, A. (2016). A hybrid approach using genetic and fruit fly optimization algorithms for QoS-aware cloud service composition. Journal of Intelligent Manufacturing, 29, 1–20.Google Scholar
  22. Tao, F., Cheng, Y., Da Xu, L., Zhang, L., & Li, B. H. (2014a). CCIoT-CMfg: Cloud computing and internet of things-based cloud manufacturing service system. IEEE Transactions on Industrial Informatics, 10(2), 1435–1442.Google Scholar
  23. Tao, F., Cheng, Y., Zhang, L., & Nee, A. Y. C. (2017). Advanced manufacturing systems: Socialization characteristics and trends. Journal of Intelligent Manufacturing, 28(5), 1079–1094.Google Scholar
  24. Tao, F., LaiLi, Y., Xu, L., & Zhang, L. (2013). FC-PACO-RM: A parallel method for service composition optimal-selection in cloud manufacturing system. IEEE Transactions on Industrial Informatics, 9(4), 2023–2033.Google Scholar
  25. Tao, F., Zhang, L., Liu, Y., Cheng, Y., Wang, L., & Xu, X. (2015). Manufacturing service management in cloud manufacturing: Overview and future research directions. Journal of Manufacturing Science and Engineering, 137(4), 040912.Google Scholar
  26. Tao, F., Zhao, D., Yefa, H., & Zhou, Z. (2010). Correlation-aware resource service composition and optimal-selection in manufacturing grid. European Journal of Operational Research, 201(1), 129–143.Google Scholar
  27. Tao, F., Zuo, Y., Da Xu, L., & Zhang, L. (2014b). IoT-based intelligent perception and access of manufacturing resource toward cloud manufacturing. IEEE Transactions on Industrial Informatics, 10(2), 1547–1557.Google Scholar
  28. Van Nguyen, S., Vo, H. D., & Hung, P. N. (2015). A correlation-aware negotiation approach for service composition. In Proceedings of the sixth international symposium on information and communication technology (pp. 210–216). ACM.Google Scholar
  29. Wu, Q., Zhu, Q., & Zhou, M. (2014). A correlation-driven optimal service selection approach for virtual enterprise establishment. Journal of Intelligent Manufacturing, 25(6), 1441–1453.Google Scholar
  30. Xiang, F., Jiang, G., Xu, L., & Wang, N. (2016). The case-library method for service composition and optimal selection of big manufacturing data in cloud manufacturing system. The International Journal of Advanced Manufacturing Technology, 84(1–4), 59–70.Google Scholar
  31. Xu, X. (2012). From cloud computing to cloud manufacturing. Robotics and Computer-Integrated Manufacturing, 28(1), 75–86.Google Scholar
  32. Xu, X., Liu, Z., Wang, Z., Sheng, Q. Z., Yu, J., & Wang, X. (2017). S-ABC: A paradigm of service domain-oriented artificial bee colony algorithms for service selection and composition. Future Generation Computer Systems, 68, 304–319.Google Scholar
  33. Xue, X., Liu, Z. Z., & Wang, S. F. (2016). Manufacturing service composition for the mass customised production. International Journal of Computer Integrated Manufacturing, 29(2), 119–135.Google Scholar
  34. Ye, Z., Mistry, S., Bouguettaya, A., & Dong, H. (2016). Long-term QoS-aware cloud service composition using multivariate time series analysis. IEEE Transactions on Services Computing, 9(3), 382–393.Google Scholar
  35. Zhang, M. W., Wei, W. J., Zhang, B., Zhang, X. Z., & Zhu, Z. L. (2008). Research on service selection approach based on composite service execution information. Chinese Journal of Computers, 31(8), 1398–1411.Google Scholar
  36. Zheng, H., Feng, Y., & Tan, J. (2016). A fuzzy QoS-aware resource service selection considering design preference in cloud manufacturing system. International Journal of Advanced Manufacturing Technology, 84(1–4), 371–379.Google Scholar
  37. Zhou, J., & Yao, X. (2017). DE-caABC: Differential evolution enhanced context-aware artificial bee colony algorithm for service composition and optimal selection in cloud manufacturing. The International Journal of Advanced Manufacturing Technology, 90(1–4), 1085–1103.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Tianyang Li
    • 1
    • 2
  • Ting He
    • 3
    Email author
  • Zhongjie Wang
    • 2
  • Yufeng Zhang
    • 4
  1. 1.School of Computer ScienceNortheast Electric Power University, JilinJilinChina
  2. 2.School of Computer Science and TechnologyHarbin Institute of TechnologyHarbinChina
  3. 3.College of Computer Science and TechnologyHuaqiao UniversityXiamenChina
  4. 4.Birmingham Business SchoolUniversity of BirminghamBirminghamUK

Personalised recommendations