Dynamic Multi Granularity Service Composition

Authors

  • A. Ravi  Department of CSE, JNTUACE, Anatapur, Andhra Pradesh, India
  • Dr. B. Lalitha  Assistant Professor, Department of CSE, JNTUACE, Anatapur, Andhra Pradesh, India

Keywords:

Service Selection, Service Composition , Cloud Computing

Abstract

The trend is for enterprises to outsource parts of their services, in order to concentrate on their own core `businesses. Meanwhile, users usually need to compose multiple different services to create a sophisticated application. Through the service-oriented architecture paradigm, users can compose elementary services to form new value added services through the process of service composition. In template-based service composition, an abstract composite service, consisting of a collection of abstract services orchestrated by workflow patterns, is first defined and then instantiated and executed at run time by binding abstract services to concrete ones. This dynamic binding ensures a loose -coupling of services and all so-called QoS-aware service composition problem. In existing work, to expand the selection scope using the concept of generalized component services, a backtracking-based algorithm and an extended genetic algorithm(GA) has been applied for finding an optimized solution and near-optimal solution respectively in composition service The proposed work, will adopt the multi-granularity service composition automatically at run time. This will be useful to study how to extend other Meta-heuristic algorithms along with Tabu-search algorithm used for efficient optimization service selection.

References

  1. M. P. Papazoglou, P. Traverso, S. Dustdar, and F. Leymann, "Serviceoriented computing: State of the art and research challenges," Computer, vol. 40, no. 11, pp. 38-45, Nov. 2007.
  2. Q. Z. Sheng et al., "Web services composition: A decade's overview," Inf. Sci., vol. 280, pp. 218-238, Oct. 2014.
  3. L. Zeng et al., "QoS-aware middleware for Web services composition," IEEE Trans. Softw. Eng., vol. 30, no. 5, pp. 311-327, May 2004.
  4. G. Canfora, M. Di Penta, R. Esposito, and M. L. Villani, "An approach for QoS-aware service composition based on genetic algorithms," in Proc. GECCO, Washington, DC, USA, 2005, pp. 1069-1075.
  5. T. Yu and K.-J. Lin, "Service selection algorithms for composing complex services with multiple QoS constraints," in Proc. ICSOC, Amsterdam, The Netherlands, 2005, pp. 130-143.
  6. M. Alrifai, T. Risse, and W. Nejdl, "A hybrid approach for efficient Web service composition with end-to-end QoS constraints," ACM Trans. Web, vol. 6, no. 2, 2012, Art. ID 7.
  7. A. Klein, F. Ishikawa, and S. Honiden, "SanGA: A self-adaptive network-aware approach to service composition," IEEE Trans. Serv. Comput., vol. 7, no. 3, pp. 452-464, Jul./Sep. 2014.
  8. H. Wada, J. Suzuki, Y. Yamano, and K. Oba, "E3: A multiobjective optimization framework for SLA-aware service composition," IEEE Trans. Serv. Comput., vol. 5, no. 3, pp. 358-372, Sep. 2012.
  9. H. Ma, F. Bastani, I.-L. Yen, and H. Mei, "QoS-driven service composition with reconfigurable services," IEEE Trans. Serv. Comput., vol. 6, no. 1, pp. 20-34, Mar. 2013.
  10. Y. Zhang, Z. Zheng, and M. R. Lyu, "An online performance prediction framework for service-oriented systems," IEEE Trans. Syst., Man, Cybern., Syst., vol. 44, no. 9, pp. 1169-1181, Sep. 2014.
  11. A.Mislove, B. Viswanath, K. P. Gummadi, and P. Druschel, "You are who you know: Inferring user profiles in online social networks," in WSDM, 2010. 
  12. R. Zafarani and H. Liu, "Connecting corresponding identities across communities," in ICWSM, 2009.
  13. Y. Zhang and M. Pennacchiotti, "Recommending branded products from social media," in Seventh ACM Conference on Recommender Systems, RecSys '13, Hong Kong, China, October 12-16, 2013, 2013, pp. 77-84. 
  14. "Predicting purchase behaviors from social media," in 22nd International World Wide Web Conference, WWW '13, Rio de Janeiro, Brazil, May 13-17, 2013, 2013, pp. 1521-1532. 
  15. Strohmaier, M. and Kroll, M. 2012. Acquiring knowledge about human goals from search query logs. Information Processing and Management 48, 1. 
  16. F. Cheng, C. Liu, J. Jiang, W. Lu, W. Li, G. Liu, W. Zhou, J. Huang, and Y. Tang. Prediction of drug-target interactions and drug repositioning via network-based inference.PLoS Computational Biology, 8:e1002503, 2012. 17E. Constantinides. Influencing the online consumer's behavior: the web experience.Internet research, 14:111-126, 2004.
  17. J. L. Herlocker, J. A. Konstan, and J. Riedl.Explaining collaborative filtering recommendations. In Proceedings of the 2000 ACM conference on Computer supported cooperative work, pages 241-250. ACM,2000.
  18. C. Jayawardhena, L. T. Wright, and C. Dennis. Consumers online: intentions, orientations and segmentation. International Journal of Retail &Distribution Management, 35:515-526, 2007.
  19. A. Karatzoglou. Collaborative temporal order modeling. In Proceedings of the _fth ACM conferenceon Recommender systems, pages 313-316, 2011.
  20. I. Konstas, V. Stathopoulos, and J. Jose. On social networks and collaborative recommendation.InProceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 195-202. ACM, 2009.
  21. A. Liaw and M. Wiener.Classification and regression by randomforest.R news, 2:18-22, 2002.
  22. C.-H. Park and Y.-G. Kim. Identifying key factors affecting consumer purchase behavior in an online shopping context.International Journal of Retail & Distribution Management, 31:16-29, 2003.
  23. Daoud, M., Naqvi, S. K., & Ahmad, A. (2014). Opinion Observer: Recommendation System on Ecommerce Website. International Journal of Computer Applications, 105.
  24. Daoud, M., Naqvi, S. K., & Jha, A. N. Semantic Analysis of Context Aware Recommendation techniques.
  25. Daoud, M., Naqvi, S. K. (2015). Recommendation System Techniques in Ecommerce System.
  26. Optmizing service selection in Combinatorial Auction by resolving Non-Linear programming constraints

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2017-08-31

Issue

Volume 2, Issue 4, July-August-2017

Section

Research Articles

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How to Cite

[1]
A. Ravi, Dr. B. Lalitha, " Dynamic Multi Granularity Service Composition" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 4, pp.584-590, July-August-2017.