A new wavenet-based network congestion predictor - WBCP

In this paper, a wavelet neural network (WNN) (or wavenet) predictor is used to predict the congestion state for each link in the computer network. The proposed WBCP predictor generates the congestion state for each link based on the utilization values of each link measured in the previous time intervals. WNNs possess the learning and generalization capabilities of the traditional neural networks together with the local characteristics of wavelet functions that enhance network ability to deal with sudden changes and burst network load in efficient manner. The proposed predictor can be used in the context of active congestion control techniques to provide the congestion state of each computer network link.

[1]  M. Pushpalatha,et al.  Generalization Analysis of Wavelet Frame based Neural Network for Function Representations using Compactly supported Gaussian Wavelets , 2009 .

[2]  Chokri Ben Amar,et al.  Comparison between Beta Wavelets Neural Networks, RBF Neural Networks and Polynomial Approximation for 1D, 2DFunctions Approximation , 2008 .

[3]  Zhiguo Zhang,et al.  Adaptive wavelet neural network for prediction of hourly NO/sub X/ and NO/sub 2/ concentrations , 2004, Proceedings of the 2004 Winter Simulation Conference, 2004..

[4]  Boleslaw K. Szymanski,et al.  Network Congestion Arbitration and Source Problem Prediction Using Neural Networks , 2002 .

[5]  K. Minu,et al.  Wavelet Neural Networks for Nonlinear Time Series Analysis , 2010 .

[6]  Jiwen Dong,et al.  A local linear wavelet neural network , 2004, Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788).

[7]  Xin Wang,et al.  Speed estimation and stimulation of DTC system based on wavelet neural network , 2003, Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693).

[8]  Cheng-Jian Lin.  Wavelet Neural Networks with a Hybrid Learning Approach , 2006, J. Inf. Sci. Eng..

[9]  Samira Chabaa,et al.  Predicting Packet Transmission Data over IP Networks Using Adaptive Neuro-Fuzzy Inference Systems , 2009 .

[11]  Behrouz A. Forouzan,et al.  Data Communications and Networking , 2000 .

[12]  Jiwen Dong,et al.  Time-series prediction using a local linear wavelet neural network , 2006, Neurocomputing.

[13]  Yao Liang,et al.  Improving Signal Prediction Performance of Neural Networks Through Multiresolution Learning Approach , 2006, IEEE Trans. Syst. Man Cybern. Part B.

[14]  Paul Dan Cristea,et al.  Time series prediction with wavelet neural networks , 2000, Proceedings of the 5th Seminar on Neural Network Applications in Electrical Engineering. NEUREL 2000 (IEEE Cat. No.00EX287).

[15]  John M. Jordan.  Data and Communications , 2012 .

[16]  Khan M. Iftekharuddin.  Orthogonal wavelets in nonlinear speckle reduction for improved target recognition , 2000 .

[17]  Gaviphat Lekutai,et al.  Adaptive Self-Tuning Neuro Wavelet Network Controllers , 1997 .

[18]  P. Kostka,et al.  WAVELET-NEURAL SYSTEMS AS APPROXIMATORS OF AN UNKNOWN FUNCTION - A COMPARISON OF BIOMEDICAL SIGNAL CLASSIFIERS , 2004 .

[19]  Eric A. Rying,et al.  Focused local learning with wavelet neural networks , 2002, IEEE Trans. Neural Networks.

[20]  Sheng-Tun Li,et al.  On noise-immune RBF networks , 2001 .

[21]  M. Moraud.  Wavelet Networks , 2018, Foundations of Wavelet Networks and Applications.