GP-Based Approach to Comprehensive Quality-Aware Automated Semantic Web Service Composition

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10593)

Abstract

Comprehensive quality-aware semantic web service composition aims to optimise semantic matchmaking quality and Quality of service (QoS) simultaneously. It is an NP-hard problem due to its huge search space. Therefore, heuristics have to be employed to generate near-optimal solutions. Existing works employ Evolutionary Computation (EC) techniques to solve combinatorial optimisation problems in web service composition. In particular, Genetic Programming (GP) has shown its promise. The tree-based representation utilised in GP is flexible to represent different composition constructs as inner nodes, but the semantic matchmaking information can not be directly obtained from the representation. To overcome this disadvantage, we propose a tree-like representation to directly cope with semantic matchmaking information. Meanwhile, a GP-based approach to comprehensive quality-aware semantic web service composition is proposed with explicit support for our representation. We also design specific genetic operation that effectively maintain the correctness of solutions during the evolutionary process. We conduct experiments to explore the effectiveness and efficiency of our GP-based approach using a benchmark dataset with real-world composition tasks.

References

  1. 1.
    Blum, A.L., Furst, M.L.: Fast planning through planning graph analysis. Artif. Intell. 90(1), 281–300 (1997)CrossRefMATHGoogle Scholar
  2. 2.
    Feng, Y., Ngan, L.D., Kanagasabai, R.: Dynamic service composition with service-dependent QoS attributes. In: 2013 IEEE 20th International Conference on Web Services (ICWS), pp. 10–17. IEEE (2013)Google Scholar
  3. 3.
    Gupta, I.K., Kumar, J., Rai, P.: Optimization to quality-of-service-driven web service composition using modified genetic algorithm. In: 2015 International Conference on Computer, Communication and Control (IC4), pp. 1–6. IEEE (2015)Google Scholar
  4. 4.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection, vol. 1. MIT press, Cambridge (1992)MATHGoogle Scholar
  5. 5.
    Küster, U., König-Ries, B., Krug, A.: Opossum-an online portal to collect and share SWS descriptions. In: 2008 IEEE International Conference on Semantic Computing, pp. 480–481. IEEE (2008)Google Scholar
  6. 6.
    Lécué, F.: Optimizing QoS-aware semantic web service composition. In: Bernstein, A., Karger, D.R., Heath, T., Feigenbaum, L., Maynard, D., Motta, E., Thirunarayan, K. (eds.) ISWC 2009. LNCS, vol. 5823, pp. 375–391. Springer, Heidelberg (2009). doi:10.1007/978-3-642-04930-9_24 CrossRefGoogle Scholar
  7. 7.
    Lécué, F., Delteil, A., Léger, A.: Optimizing causal link based web service composition. In: ECAI. pp. 45–49 (2008)Google Scholar
  8. 8.
    Ma, H., Schewe, K.D., Thalheim, B., Wang, Q.: A formal model for the interoperability of service clouds. SOCA 6(3), 189–205 (2012)CrossRefGoogle Scholar
  9. 9.
    Ma, H., Wang, A., Zhang, M.: A hybrid approach using genetic programming and greedy search for QoS-aware web service composition. In: Hameurlain, A., Küng, J., Wagner, R., Decker, H., Lhotska, L., Link, S. (eds.) Transactions on Large-Scale Data- and Knowledge-Centered Systems XVIII. LNCS, vol. 8980, pp. 180–205. Springer, Heidelberg (2015). doi:10.1007/978-3-662-46485-4_7 Google Scholar
  10. 10.
    Paolucci, M., Kawamura, T., Payne, T.R., Sycara, K.: Semantic matching of web services capabilities. In: Horrocks, I., Hendler, J. (eds.) ISWC 2002. LNCS, vol. 2342, pp. 333–347. Springer, Heidelberg (2002). doi:10.1007/3-540-48005-6_26 CrossRefGoogle Scholar
  11. 11.
    Peer, J.: Web Service Composition as AI planning: A Survey. University of St. Gallen, Switzerland (2005)Google Scholar
  12. 12.
    Qi, L., Tang, Y., Dou, W., Chen, J.: Combining local optimization and enumeration for QoS-aware web service composition. In: 2010 International Conference on Web Services (ICWS), pp. 34–41 (2010)Google Scholar
  13. 13.
    Rao, J., Su, X.: A survey of automated web service composition methods. In: Cardoso, J., Sheth, A. (eds.) SWSWPC 2004. LNCS, vol. 3387, pp. 43–54. Springer, Heidelberg (2005). doi:10.1007/978-3-540-30581-1_5 CrossRefGoogle Scholar
  14. 14.
    Rodriguez-Mier, P., Mucientes, M., Lama, M., Couto, M.I.: Composition of web services through genetic programming. Evol. Intell. 3(3–4), 171–186 (2010)CrossRefGoogle Scholar
  15. 15.
    Shet, K., Acharya, U.D., et al.: A new similarity measure for taxonomy based on edge counting (2012). arXiv preprint . arxiv:1211.4709
  16. 16.
    da Silva, A.S., Ma, H., Zhang, M.: GraphEvol: a graph evolution technique for web service composition. In: Chen, Q., Hameurlain, A., Toumani, F., Wagner, R., Decker, H. (eds.) DEXA 2015. LNCS, vol. 9262, pp. 134–142. Springer, Cham (2015). doi:10.1007/978-3-319-22852-5_12 CrossRefGoogle Scholar
  17. 17.
    da Silva, A.S., Ma, H., Zhang, M.: Genetic programming for QoS-aware web service composition and selection. Soft Comput. 20, 1–17 (2016)CrossRefGoogle Scholar
  18. 18.
    da Silva, A.S., Mei, Y., Ma, H., Zhang, M.: Particle swarm optimisation with sequence-like indirect representation for web service composition. In: Chicano, F., Hu, B., García-Sánchez, P. (eds.) EvoCOP 2016. LNCS, vol. 9595, pp. 202–218. Springer, Cham (2016). doi:10.1007/978-3-319-30698-8_14 CrossRefGoogle Scholar
  19. 19.
    Wang, C., Ma, H., Chen, A., Hartmann, S.: Comprehensive quality-aware automated semantic web service composition. In: Peng, W., Alahakoon, D., Li, X. (eds.) AI 2017. LNCS, vol. 10400, pp. 195–207. Springer, Cham (2017). doi:10.1007/978-3-319-63004-5_16 CrossRefGoogle Scholar
  20. 20.
    Yu, Y., Ma, H., Zhang, M.: An adaptive genetic programming approach to QoS-aware web services composition. In: 2013 IEEE Congress on Evolutionary Computation, pp. 1740–1747. IEEE (2013)Google Scholar
  21. 21.
    Zeng, L., Benatallah, B., Dumas, M., Kalagnanam, J., Sheng, Q.Z.: Quality driven web services composition. In: Proceedings of the 12th international conference on World Wide Web, pp. 411–421. ACM (2003)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Victoria University of WellingtonWellingtonNew Zealand
  2. 2.Clausthal University of TechnologyClausthal-zellerfeldGermany

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