A Neuro-Fuzzy Model for QoS Based Selection of Web Service

Abdallah Missaoui, Kamel Barkaoui
DOI: 10.4236/jsea.2010.36068                

Abstract

The automatic selection and composition of Web services rely strongly on the manner to deal with ambiguity inherent to the description of functionalities of these services and the client’s requests. Quality of Service (QoS) criteria become crucial in Web services selection and the problem of checking that a web service satisfies a given level of QOS is considered in recent research works. This paper presents a QoS based automatic classification method of web services. These services give generally similar functionalities and are offered by different providers. The main feature of our Web service selection model is to take advantage of the neuro-fuzzy logic for coping with the imprecision of QoS constraints values.

Keywords

Web Service, Selection, Neuro-Fuzzy, QoS, Constraint

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	J. S. Jang, “ANFIS: Adaptive-Network-Based Fuzzy In- ference System,” IEEE Transactions on Systems, Man, and Cybernetics, Vol. 23, No. 3, 1993, pp. 665-684.
[2]	J. R. Jang and C. T. Sun, “Neuro-Fuzzy and Soft Com- puting: A Computational Approach to Learning and Machine Intelligence,” Prentice-Hall, Inc., Upper Saddle River, New Jersy, 1997.
[3]	V. Diamadopoulou, C. Makris, Y. Panagis and E. Sakko- poulos, “Techniques to Support Web Service Selection and Consumption with QoS Characteristics,” Journal of Network and Computer Applications, Vol. 31, No. 2, 2008, pp. 108-130.
[4]	A. F. M. Huang, C. W. Lan and S. J. H. Yang, “An Optimal QoS-Based Web Service Selection Scheme,” Information Sciences, Vol. 179, No. 19, 2009, pp. 3309-3322.
[5]	L. Zeng, B. Benatallah, A. H. H. Ngu, M. Dumas, J. Kalagnanam and H. Chang, “QoS-Aware Middleware for Web Services Composition,” IEEE Transactions on Software Engineering, 2004, pp. 311-327.
[6]	D. A. Menascé, H. Ruan and H. Gomaa, “QoS Manage- ment in Service-Oriented Architectures,” Journal of Per- formance Evaluation, Vol. 64, No. 7-8, 2007, pp. 646-663.
[7]	D. A. Menasce, “QoS Issues in Web Services,” IEEE Internet Computing, Vol. 6, No. 6, 2002, pp. 72-75.
[8]	M. Sultana, M. M. Akbar and M. Rouf, “Network Flow Heuristic Algorithm for a Distributed Web Service Selection Problem,” IEEE Conference on Communications, Compu- ters and Signal Processing, 2009, pp. 465-470.
[9]	D. Tsesmetzis, I. Roussaki and E. Sykas, “QoS-Aware Service Evaluation and Selection,” European Journal of Operational Research, Vol. 191, No. 3, 2008, pp. 1101- 1112.
[10]	S. Chaari, Y. Badr and F. Biennier, “Enhancing Web Ser- vice Selection by QOS-Based Ontology and WS-Policy,”
[11]	Proceeding of the 23rd ACM Symposium on Applied Computing, Ceará, 2008, pp. 2426-2431.
[12]	D. A. Menascé, E. Casalicchio and V. Dubey, “On Optimal Service Selection in Service Oriented Archi- tectures,” Performance Evaluation Journal, Vol. 67, No. 8, 2010, pp. 659-675.
[13]	H. Pfeffer, S. Krüssel and S. Steglich, “A Fuzzy Logic based Model for Representing and Evaluating Service Composition Properties,” The Third International Con- ference on Systems and Networks Communications, Bangalore, 2009.
[14]	M. Lin, J. Xie, H. Guo and H. Wang, “Solving Qos-Driven Web Service Dynamic Composition as Fuzzy Constraint Satisfaction,” IEEE International Conference on e-Tech- nology, e-Commerce and e-Service, Hong Kong, 2005.
[15]	P. Wang, K. Chao, C. Lo, C. Huang and Y. Li, “A Fuzzy Model for Selection of QoS-Aware Web Services,” IEEE International Conference on e-Business Engineering, IEEE Computer Society, Shanghai, 2006, pp. 585-593.
[16]	K. M. Chao, M. Younas, C. C. Lo and T. H. Tan, “Fuzzy Atchmaking for Web Services,” The 19th International Conference on Advanced Information Networking and Applications, Taipei, 2005.
[17]	L. Zhuang, Y. F. Huang, W. G. Jian, J. B. Zhou and H. Q. Guo, “Solving Fuzzy QoS Constraint Satisfaction Tech- nique for Web Service Selection,” International Con- ference on Computational Intelligence and Security Work- shops, Harbin, 2007.
[18]	H. Tong and S. Zhang, “A Fuzzy Multi-Attribute Decision Making Algorithm for Web Services Selection Based on QoS,” The IEEE Asia-Pacific Conference on Services Computing, Guangzhou, 2006.
[19]	M. A. Denai, F. Palis and A. Zeghbib, “ANFIS Based Modelling and Control of Non-Linear Systems: A Tu- torial,” IEEE International Conference on Systems, Man and Cybernetics, Vol. 4, 2004, pp. 3433-3438.
[20]	O. Nelles, A. Fink, R. Babuka and M. Setnes, “Com- parison of Two Construction Algorithms for Takagi- Sugeno Fuzzy Models,” International Journal of Applied Mathematics and Computer Science, 2000, pp. 835-855.
[21]	P. Werbos, “The Toots of the Back Propagation: From Ordered Derivatives to Neural Networks and Political Forecasting,” John Wiley and Sons, Inc, New York, 1993.