A Semantic Composition Framework for Simulation Model Service

  • Tian Bai
  • Lin ZhangEmail author
  • Fei Wang
  • Tingyu Lin
  • Yingying Xiao
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 946)


In order to solve the large-scale model service composition problem in the cloud of simulation, this paper proposes a simulation model service composition framework which considers the characteristics of the cloud. This simulation model service composition framework adopts an ontology-based simulation model service description strategy (MSDS). Based on MSDS, the composite service composed of several model services with complex topology connection relationships is generated by the Input/Output semantic connection strength and simulation capability. A contrast experiment is conducted for the empirical verification.


Semantic service composition Simulation Cloud 



Authors gratefully acknowledge the support of National Natural Science Foundation of China (Grant No. 61374199); Natural Science Foundation of Beijing (No. 4142031).


  1. 1.
    Bell, D., Cesare, S.D., Lycett, M., et al.: Semantic web service architecture for simulation model reuse. In: IEEE International Symposium on Distributed Simulation and Real-Time Applications, pp. 129–136. IEEE Computer Society (2007)Google Scholar
  2. 2.
    Shin, D.H., Lee, K.H., Suda, T.: Automated generation of composite web services based on functional semantics. Web Semant. Sci. Serv. Agents World Wide Web 7(4), 332–343 (2009)CrossRefGoogle Scholar
  3. 3.
    Calheiros, R.N., Ranjan, R., Beloglazov, A., et al.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract. Exp. 41(1), 23–50 (2010)CrossRefGoogle Scholar
  4. 4.
    Klusch, M., Kapahnke, P.: iSeM: approximated reasoning for adaptive hybrid selection of semantic services. In: Aroyo, L., et al. (eds.) ESWC 2010. LNCS, vol. 6089, pp. 30–44. Springer, Heidelberg (2010). Scholar
  5. 5.
    Chu, D., Han, J., Li, J., Zhao, Y.: XSSD: a fast hybrid semantic web services discovery method. In: 3rd International Conference on Computer Technology and Development, (ICCTD 2011) (2011)Google Scholar
  6. 6.
    Tao, F., Laili, Y.J., Xu, L., Zhang, L.: FC-PACO-RM: a parallel method for service composition optimal-selection in cloud manufacturing system. IEEE Trans. Ind. Inform. 9(4), 2023–2033 (2013)CrossRefGoogle Scholar
  7. 7.
    Rodriguez-Mier, P., Pedrinaci, C., Lama, M., et al.: An integrated semantic web service discovery and composition framework. IEEE Trans. Serv. Comput. 9(4), 537–550 (2016)CrossRefGoogle Scholar
  8. 8.
    Fki, E., Tazi, S., Drira, K.: Automated and flexible composition based on abstract services for a better adaptation to user intentions. Futur. Gener. Comput. Syst. 68, 376–390 (2016)CrossRefGoogle Scholar
  9. 9.
    Liu, Y., Xun, X., Zhang, L., Tao, F.: An extensible model for multi-task oriented service composition and scheduling in cloud manufacturing. J. Comput. Inf. Sci. Eng. 16, 041009 (2016)CrossRefGoogle Scholar
  10. 10.
    Jatoth, C., Gangadharan, G.R., Buyya, R.: Computational intelligence based QoS-aware web service composition: a systematic literature review. IEEE Trans. Serv. Comput. 10(3), 475–492 (2017)CrossRefGoogle Scholar
  11. 11.
    Li, F., Zhang, L., Liu, Y., et al.: A clustering network-based approach to service composition in cloud manufacturing. Int. J. Comput. Integr. Manuf. 30(3), 1–12 (2017)CrossRefGoogle Scholar
  12. 12.
    Li, F., LaiLi, Y., Zhang, L., Hu, X., Zeigler, B.P.: Service composition and scheduling in cloud-based simulation environment. In: SpringSim 2018, 15–18 April, Maryland, Baltimore, USA (2018)Google Scholar
  13. 13.
    Que, Y., Zhong, W., Chen, H., et al.: Improved adaptive immune genetic algorithm for optimal QoS-aware service composition selection in cloud manufacturing. Int. J. Adv. Manuf. Technol. 10, 1–11 (2018)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Tian Bai
    • 1
    • 2
  • Lin Zhang
    • 1
    • 2
    Email author
  • Fei Wang
    • 1
    • 2
  • Tingyu Lin
    • 3
  • Yingying Xiao
    • 3
  1. 1.School of Automation Science and Electrical EngineeringBeihang UniversityBeijingChina
  2. 2.Engineering Research Center of Complex Product Advanced Manufacturing SystemsMinistry of EducationBeijingChina
  3. 3.Beijing Simulation CenterBeijingChina

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