Skip to main content
Log in

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

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Notes

  1. http://www.computer.org/portal/web/computingnow

  2. http://en.wikipedia.org/wiki/Cloud_computing

  3. http://aws.amazon.com/ec2/

  4. http://code.google.com/appengine

  5. http://www.andrelouis.com/qws

References

  1. Fenza G, Senatore S (2010) Friendly web services selection exploiting fuzzy formal concept analysis. Soft Comput 14:811–819

    Article  Google Scholar 

  2. Wang P, Chao KM, Lo CC (2009) On optimal decision for QoS-aware composite service selection. Expert Syst Appl 37:440–449

    Article  Google Scholar 

  3. Yang FC, Su S, Li Z (2008) Hybrid QoS-aware semantic web service composition strategies. Sci China Ser F-Information Sciences 51:1822–1840

    Article  Google Scholar 

  4. Chuang SN, Chan ATS (2008) Dynamic QoS adaptation for mobile middleware. IEEE Trans Softw Eng 34:738–752

    Article  Google Scholar 

  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 411–421

  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 11–20

  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 743–750

  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 242–249

  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 881–890

  10. Ardagna D, Pernici B (2007) Adaptive service composition in flexible processes. IEEE Trans Softw Eng 33:369–384

    Article  Google Scholar 

  11. Yu T, Zhang Y, Lin K-J (2007) Efficient algorithms for web services selection with end-to-end QoS constraints. ACM Trans Web 1:1–26

    Article  Google Scholar 

  12. Wang SG, Sun QB, Yang FC (2010) Towards web service selection based on QoS estimation. Int J Web Grid Serv 6:424–443

    Article  Google Scholar 

  13. Börzsönyi S, Kossmann D, Stocker K (2001) The skyline operator. In: Proceedings of the 17th international conference on data engineering (ICDE’01), pp 421–430

  14. Papadias D, Tao Y, Fu G, Seeger B (2005) Progressive skyline computation in database systems. ACM Trans Database Syst 30:41–82

    Article  Google Scholar 

  15. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the 1995 IEEE international conference on neural networks (ICNN’95), pp 1942–1948

  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 12:171–195

    Article  Google Scholar 

  17. Hai-bing G, Chi Z, Liang C (2005) General particle swarm optimization model. Chin J Comput 28:1980–1987

    Google Scholar 

  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 22:2030–2038

    Article  Google Scholar 

  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 278–284

  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 795–804

  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 529–534

Download references

Acknowledgements

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

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shangguang Wang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wang, S., Sun, Q., Zou, H. et al. Particle Swarm Optimization with Skyline Operator for Fast Cloud-based Web Service Composition. Mobile Netw Appl 18, 116–121 (2013). https://doi.org/10.1007/s11036-012-0373-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-012-0373-3

Keywords

Navigation