International Journal of Modern Education and Computer Science (IJMECS)
IJMECS Vol. 7, No. 2, 8 Feb. 2015
Cover page and Table of Contents: PDF (size: 405KB)
A GA-Tabu Based User Centric Approach for Discovering Optimal Qos Composition
Full Text (PDF, 405KB), PP.56-62
Views: 0 Downloads: 0
Author(s)
Vivek Gaur
1,*
Praveen Dhyani
2
O. P. Rishi
3
Index Terms
Cloud services, service utility, QoS, ge-netic algorithm, Tabu search
Abstract
Cloud computing is an emerging internet-based paradigm of rendering services on pay- as -per -use basis. Increasing growth of cloud service providers and services creates the need to provide a tool for retrieval of the high-quality optimal cloud services composition with relevance to the user priorities. Quality of Service rank-ings provides valuable information for making optimal cloud service selection from a set of functionally equiva-lent service candidates. To obtain weighted user-centric Quality of Service Composition, real-world invocations on the service candidates are usually required. To avoid the time-consuming and expensive real-world service invocations, this paper proposes framework for predic-tion of optimal composition of services requested by the user. Taking advantage of the past service usage experi-ences of the consumers more cost effective results are achieved. Our proposed framework enables the end user to determine the optimal service composition based on the input weight for individual service Quality of Service. The Genetic algorithm and basic Tabu search is applied for the user-centric Quality of Service ranking prediction and the optimal service composition. The experimental results proves that our approaches outperform other competing approaches.
Cite This Paper
Vivek Gaur, Praveen Dhyani, O. P. Rishi, "A GA-Tabu Based User Centric Approach for Discovering Optimal Qos Composition", International Journal of Modern Education and Computer Science (IJMECS), vol.7, no.2, pp.56-62, 2015. DOI:10.5815/ijmecs.2015.02.08
Reference
[1]M. Alrifai, D. Skoutas and T. Risse, "Selecting skyline services for QoS-based web services composition," Pro-ceedings of the 19th International conference on World Wide Web (WWW 2010), 2010, pp. 11-20.
[2]V Cardellini, E. Casalicchio, V.Grassi, and F.Lo Presti, "Flow-Based Service Selection for Web Service Composi-tion Supporting Multiple QoS Classes," Proceedings of the 5th IEEE International Conference on Web Services (ICWS) 2007, 2007, pp.743-750.
[3]A. Goscinski and M. Brock, " Toward dynamic and at-tribute based publication, discovery and selection for cloud computing," Future Generation Computer Systems, vol. 26, pp. 947-970, 2010.
[4]D. Ardagna and B. Pernici, “Adaptive service composition in flexible processes," IEEE Transactions on Software Engineering, vol. 33 pp.369-384, 2007.
[5]S.Y. Hwang, E.P.Lim, C.H. Lee and C.H. Chen, "Dy-namic Web service selection for reliable web service composition," IEEE Transactions on services computing, vol.1, pp. 104-116, 2008.
[6]T. Yu, Y. Zhang, and K.J. Lin, "Efficient algorithm for web service selection with end-to-end QoS constraints," ACM Transactions on the web, Vol. 1, pp. 1-26, 2007.
[7]M. Alrifai and T. Risse, "Combining global optimization with local selection for efficient QoS -aware service com-position," Proceedings of the 18th International conference on world wide web (www 2009), 2009, pp.881-890.
[8]P.A. Bonatti and P. Festa, "On Optimal Service Selection," Proc. 14th Int’l Conf. World Wide Web (WWW ’05), pp. 530-538, 2005.
[9]L. Zeng, B. Benatallah, A.H. Ngu, M. Dumas, J. Kalagnanam, and H. Chang, "QoS-Aware Middleware for Web Services Composition," IEEE Trans. Software Eng., vol. 30, no. 5, pp. 311-327, May 2004.
[10]N.N. Liu and Q. Yang, "Eigen rank: A Ranking-Oriented Approach to Collaborative Filtering," Proc. 31st Int’l ACM SIGIR Conf. Research and Development in Infor-mation Retrieval (SIGIR ’08), pp. 83-90, 2008.
[11]C. Yang, B. Wei, J. Wu, Y. Zhang, and L. Zhang, "Cares: A Ranking-Oriented Cadal Recommender System," Proc. Ninth ACM/IEEE-CS Joint Conf. Digital Libraries (JCDL’09), pp. 203-212, 2009.
[12]L. Wang, J. Shen and J. Yung, "A Survey on Bio-inspired Algorithms for Web Service Composition," Proc. of the 16th International Conference on Computer Supported Cooperative Work in Design, pp.569-574, 2012.
[13]S.Bahdori, S. Kafi, K. Zamani far, M Reza Khayyambashi, "Optimal web service composition using hybrid GA-TABU search," Journal of Theoretical and Applied Infor-mation Technology, Vol.9,No.1, pp.10-15,2009.
[14]Jose Antonio Parejo, PabloFernandez, Antonio Ruiz Cor-tes," QoS-aware services composition using Tabu search and hybrid genetic algorithms," Actas de Tallers de Inge-niera del software y Bases de Datos Vol. 2. No.1, pp.55 -66, 2008.
[15]S.Wang, Z.Zheng, Q.Sun, H.Zou and F. Yang," Cloud Model for Service Selection," Workshop on Cloud Com-puting, IEEE INFOCOM 2011, pp.677 - 682, 2011.
[16]J.H.Jang, D.H.Shin and K.H. Lee, "Fast quality driven selection of composite Web Services, "Proceedings of the 4th European Conference on Web Services (ECOWS 2006), 2006, pp.87-96.
[17]Wenying Zeng, Yuelong Zhao and Junwei Zeng, “Cloud service and service selection algorithm research”, GEC Summit, pp 1045-1048. ACM, 2009.
[18]Ahmed F. Ali, "Genetic local search algorithm with self-adaptive population resizing for solving global optimization problems," I.J. Information Engineering and Electronic Business, pp 51-63, 2014.