Computational intelligence tools for next generation quality of service management

In this paper we explore the interest of computational intelligence tools in the management of heterogeneous communication networks, specifically to predict congestion, failures and other anomalies in the network that may eventually lead to degradation of the quality of offered services. We show two different applications based on neural and neuro-fuzzy systems for quality of service (QoS) management in next generation networks for voice and video service over heterogeneous Internet protocol (V2oIP) services. The two examples explained in this paper attempt to predict the communication network resources for new incoming calls, and visualizing the QoS of a communication network by means of self-organizing maps.

[1]  Ian H. Witten,et al.  WEKA: a machine learning workbench , 1994, Proceedings of ANZIIS '94 - Australian New Zealnd Intelligent Information Systems Conference.

[2]  Konstantina Papagiannaki,et al.  Long-term forecasting of Internet backbone traffic: observations and initial models , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[3]  San-qi Li,et al.  A predictability analysis of network traffic , 2002, Comput. Networks.

[4]  Nikola K. Kasabov,et al.  Evolving fuzzy neural networks for supervised/unsupervised online knowledge-based learning , 2001, IEEE Trans. Syst. Man Cybern. Part B.

[5]  Ajith Abraham,et al.  Neuro Fuzzy Systems: Sate-of-the-Art Modeling Techniques , 2001, IWANN.

[6]  Zheng Wang,et al.  Internet QoS: Architectures and Mechanisms for Quality of Service , 2001 .

[7]  Mikel Izal,et al.  On linear prediction of Internet traffic for packet and burst switching networks , 2001, Proceedings Tenth International Conference on Computer Communications and Networks (Cat. No.01EX495).

[8]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[9]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[10]  Fabien Moutarde,et al.  U*F clustering: a new performant "cluster-mining" method based on segmentation of Self-Organizing Maps , 2005 .

[11]  E. Mizutani,et al.  Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.

[12]  M. P. Hollier,et al.  Non-intrusive perceptual quality measurement for quality assurance in NGN and 3G networks , 2003 .