Mobile Networks and Applications

, Volume 18, Issue 1, pp 116–121 | Cite as

Particle Swarm Optimization with Skyline Operator for Fast Cloud-based Web Service Composition

  • Shangguang Wang
  • Qibo Sun
  • Hua Zou
  • Fangchun Yang


Quality of Services play an increasingly important role during the procedure of Cloud-based web service composition for seamless and dynamic integration of business applications. However, as Cloud-based web services (CWSs) proliferate, it becomes difficult to facilitate service composition quickly in Cloud computing environment. In this paper, based on the notion of Skyline, we propose a fast CWS composition approach. This approach adopts Skyline operator to prune redundant CWS candidates and then employs Particle Swarm Optimization to select CWS from amount of candidates for composing single service into a more powerful composite service. Based on a real dataset, we conduct an experiment to evaluate our proposed approach. Experimental results show that our proposed approach is effective and efficient for CWS composition.


cloud computing cloud-based web service service composition skyline operator particle swarm optimization 



The work presented in this study is supported by the 863 program (2011AA01A102); the CPSF (2011M500226); the RFDP (20110005130001) and the NCET (100263).


  1. 1.
    Fenza G, Senatore S (2010) Friendly web services selection exploiting fuzzy formal concept analysis. Soft Comput 14:811–819CrossRefGoogle Scholar
  2. 2.
    Wang P, Chao KM, Lo CC (2009) On optimal decision for QoS-aware composite service selection. Expert Syst Appl 37:440–449CrossRefGoogle Scholar
  3. 3.
    Yang FC, Su S, Li Z (2008) Hybrid QoS-aware semantic web service composition strategies. Sci China Ser F-Information Sciences 51:1822–1840CrossRefGoogle Scholar
  4. 4.
    Chuang SN, Chan ATS (2008) Dynamic QoS adaptation for mobile middleware. IEEE Trans Softw Eng 34:738–752CrossRefGoogle Scholar
  5. 5.
    Zeng L, Benatallah B, Dumas M, Kalagnanam J, Sheng QZ (2003) Quality driven web services composition. In: Proceedings of the 12th international conference on World Wide Web (WWW’03), pp 411–421Google Scholar
  6. 6.
    Alrifai M, Skoutas D, Risse T (2010) Selecting skyline services for QoS-based web service composition. In: Proceedings of the 19th international conference on World Wide Web (WWW’10), pp 11–20Google Scholar
  7. 7.
    Cardellini V, Casalicchio E, Grassi V, Lo Presti F (2007) Flow-based service selection for web service composition supporting multiple QoS classes. In: Proceedings of the 2007 IEEE International Conference on Web Services (ICWS’07), pp 743–750Google Scholar
  8. 8.
    Funk C, Schultheis A, Linnhoff-Popien C, Mitic J, Kuhmunch C (2007) Adaptation of composite services in pervasive computing environments. In: Proceedings of the 2007 IEEE International Conference on Pervasive Services (ICPS’07), pp 242–249Google Scholar
  9. 9.
    Alrifai M, Risse T (2009) Combining global optimization with local selection for efficient QoS-aware service composition. In: Proceedings of the 18th international conference on World Wide Web (WWW’09), pp 881–890Google Scholar
  10. 10.
    Ardagna D, Pernici B (2007) Adaptive service composition in flexible processes. IEEE Trans Softw Eng 33:369–384CrossRefGoogle Scholar
  11. 11.
    Yu T, Zhang Y, Lin K-J (2007) Efficient algorithms for web services selection with end-to-end QoS constraints. ACM Trans Web 1:1–26CrossRefGoogle Scholar
  12. 12.
    Wang SG, Sun QB, Yang FC (2010) Towards web service selection based on QoS estimation. Int J Web Grid Serv 6:424–443CrossRefGoogle Scholar
  13. 13.
    Börzsönyi S, Kossmann D, Stocker K (2001) The skyline operator. In: Proceedings of the 17th international conference on data engineering (ICDE’01), pp 421–430Google Scholar
  14. 14.
    Papadias D, Tao Y, Fu G, Seeger B (2005) Progressive skyline computation in database systems. ACM Trans Database Syst 30:41–82CrossRefGoogle Scholar
  15. 15.
    Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the 1995 IEEE international conference on neural networks (ICNN’95), pp 1942–1948Google Scholar
  16. 16.
    del Valle Y, Venayagamoorthy GK, Mohagheghi S, Hernandez JCH, RG (2008) Particle swarm optimization: basic concepts, variants and applications in power systems. IEEE Trans Evol Comput 12:171–195CrossRefGoogle Scholar
  17. 17.
    Hai-bing G, Chi Z, Liang C (2005) General particle swarm optimization model. Chin J Comput 28:1980–1987Google Scholar
  18. 18.
    AlRashidi MR, El-Hawary ME (2007) Hybrid particle swarm optimization approach for solving the discrete OPF problem considering the valve loading effects. IEEE Trans Power Syst 22:2030–2038CrossRefGoogle Scholar
  19. 19.
    Qibo S, Shangguang W, Fangchun Y (2010) Quick service selection approach based on particle swarm optimization In: Proceedings of the 2010 IEEE international conference on bio-inspired computing: theories and applications (BIC-TA’10), pp 278–284Google Scholar
  20. 20.
    Al-Masri E, Mahmoud QH (2008) Investigating web services on the world wide web. In: Proceedings of the 17th international conference on World Wide Web (WWW’08), pp 795–804Google Scholar
  21. 21.
    Al-Masri E, Mahmoud QH (2007) QoS-based discovery and ranking of web services. In: 16th International Conference on Computer Communications and Networks (ICCCN 2007), pp 529–534Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Shangguang Wang
    • 1
  • Qibo Sun
    • 1
  • Hua Zou
    • 1
  • Fangchun Yang
    • 1
  1. 1.State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijingChina

Personalised recommendations