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Neural Network Based Approaches for Network Traffic Prediction

  • Flávio Henrique Teles VieiraEmail author
  • Victor Hugo Teles Costa
  • Bruno Henrique Pereira Gonçalves
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 427)

Abstract

In this chapter, we review some learning strategies for neural networks such as MLP (Multilayer Perceptron), RBF (Radial Basis Function) and Recurrent Networks applied to computer network traffic prediction. That is, the considered neural networks and training algorithms are used to predict the traffic volume of a computer network. Some methods of improving the prediction performance of neural networks are also considered such as application of Wavelet Transform. We discuss about using the Wavelet Transform in supervised training of neural networks by decomposing the traffic process into approximation and detail processes. We present some results involving the application of the Orthogonal Least Squares (OLS) algorithm in RBF networks for traffic prediction. Regarding the Recurrent neural networks, we verify their traffic prediction performance when trained with the Extended Kalman Filter (EKF) and the RTRL (Real Time Recurrent Learning). Real network traffic traces are used in the simulations in order to verify the prediction performance of the neural network algorithms.

Keywords

Neural network Traffic prediction Recurrent Network RBF neural network Wavelets 
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References

  1. 1.
    Adas, A.: Supporting real time VBR video using dynamic reservation based on linear prediction. In: Proc. IEEE INFOCOMM 1996, pp. 1476–1483 (1996)Google Scholar
  2. 2.
    Adas, A., Mukherjee, A.: On resource management and QoS guarantees for long range dependent traffic. In: Proc. IEEE INFOCOMM, pp. 779–787 (April 1995)Google Scholar
  3. 3.
    Ando, T.: Bayesian Model Selection and Statistical Modeling (Statistics: A Series of Textbooks and Monographs), 1st edn. Chapman and Hall/CRC (2010)Google Scholar
  4. 4.
    Aussem, A., Murtag, F.: Combining Neural Network Forecasts on Wavelet-Transformed Time Series. Connection Science 9, 113–121 (1997)CrossRefGoogle Scholar
  5. 5.
    Aussem, A., Murtag, F., Sarazin, M.: Dynamical Recurrent Neural Networks- towards environmental time series prediction. International Journal on Neural Systems 6, 145–170 (1995)CrossRefGoogle Scholar
  6. 6.
    Bengio, Y., Frasconi, P., Gori, M.: Recurrent Neural Networks for Adaptive Temporal processing. Universitá di Firenze (1993)Google Scholar
  7. 7.
    Box, G.E., Jenkins, G.M.: Time Series Analysis: Forecasting and Control. Holden Day, San Francisco (1976)zbMATHGoogle Scholar
  8. 8.
    de Pádua Braga, A., de Leon, F., de Carvalho, A., Ludermir, T.B.: Fundamentos de Redes Neurais Artificiais. DCC/IM, COPPE/Sistemas NCE/UFRJ, Rio de Janeiro (1998)Google Scholar
  9. 9.
    Broomhead, D.S., Lowe, D.: Multivariable functional interpolation and adaptive networks. Complex Systems 2, 321–355 (1988)MathSciNetzbMATHGoogle Scholar
  10. 10.
    Carpenter, G.A., Grossberg, S.: Adaptive Resonance Theory, 2nd edn. The Handbook of Brain Theory and Neural Networks. MIT Press (2003)Google Scholar
  11. 11.
    Chen, S.: Orthogonal least square learning algorithm for radial basis function networks. IEEE Transactions on Neural Networks 2(2), 335–356 (1989)Google Scholar
  12. 12.
    Chen, S., Billings, S.A.: Neural networks for nonlinear dynamic system modeling and identification. International Journal of Control 56(2), 319–346 (1992)MathSciNetzbMATHCrossRefGoogle Scholar
  13. 13.
    Chen, B.-S., Peng, S.-C., Ku-Chen: Traffic Modeling, Prediction and Congestion Control for High-Speed Networks: A Fuzzy AR Approach. IEEE Transactions in Fuzzy Systems 8(5) (2000)Google Scholar
  14. 14.
    Chui, C.K.: An Introduction to wavelets. Department of Mathematics Texas. A&M University College Station, Texas (1992)Google Scholar
  15. 15.
    Connor, J.T., Martin, R.D., Atlas, L.E.: Recurrent Neural Networks and Robust Time Series Prediction. IEEE Transactions on Neural Networks 5(2), 240–253 (1994)CrossRefGoogle Scholar
  16. 16.
    Cybenko, G.: Approximation by superposition of a sigmoid function. Mathematics of Control, Signals and Systems 2, 303–314 (1989)MathSciNetzbMATHCrossRefGoogle Scholar
  17. 17.
    Doulamis, A.D., Doulamis, N.D., Kollias, S.D.: An Adaptable Neural-Network Model for Recursive Nonlinear Traffic Prediction and Modeling of MPEGVideo Sources. IEEE Transactions on Neural Networks 14(1) (2003)Google Scholar
  18. 18.
    Gomes, J., Velho, L., Goldstein, S.: Wavelets: Teoria, Software e Aplicações. IMPA, Rio de Janeiro (1997)Google Scholar
  19. 19.
    Güler, O.: Foundations of Optimization (Graduate Texts in Mathematics), 1st edn. Springer (2010)Google Scholar
  20. 20.
    Haykin, S.: Modern filters. Macmillan Publishing Company (1989)Google Scholar
  21. 21.
    Haykin, S.: Neural Networks - A Comprehensive Foundation. Prentice Hall (1994)Google Scholar
  22. 22.
    Haykin, S.: Adaptive Filter Theory, 4th edn. Prentice-Hall (2002)Google Scholar
  23. 23.
    He, X., Lapedes, A.: Nonlinear modeling and prediction by successive approximations using radial basis functions. Technical report, Los Alamos National Laboratory (1991)Google Scholar
  24. 24.
    Jagannathan, S., Talluri, J.: Adaptive Predictive Congestion Control of High-Speed ATM Networks. IEEE Transactions on Broadband 48(2) (2002)Google Scholar
  25. 25.
    Jamg, J.-S.R., Sun, C.-T.: Functional equivalence between radial basis function networks and fuzzy inference systems. IEEE Transactions on Neural Networks 4, 156–159 (1993)CrossRefGoogle Scholar
  26. 26.
    Kalman, R.E.: A New Approach to Linear Filtering and Prediction Problems. Transaction of the ASME - Journal of Basic Engineering, 35–45 (1960)Google Scholar
  27. 27.
    Mathews, M.B.: Neural networks nonlinear adaptive filtering using the extended Kalman filtering algorithm. In: Proceedings of the International Neural Networks Conference, Paris, vol. 1, pp. 115–119 (1990)Google Scholar
  28. 28.
    Orr, M.J.L.: Regularization in the Selection of Radial Basis Function Centres. Neural Computation 7(3), 606–623 (1995)CrossRefGoogle Scholar
  29. 29.
    Page, M.: Connectionist modelling in psychology: a localist manifesto. Behavioral and Brain Sciences 23, 443–512 (2000)CrossRefGoogle Scholar
  30. 30.
    Papoulis, A., Pillai, S.: Probability, Random Variables and Stochastic Processes, 4th edn. McGraw-Hill (2001)Google Scholar
  31. 31.
    Park, K., Kim, G., Crovella, M.: On the Relation between File Sizes, Transport Protocols and Self-similar Network Traffic. In: Proc. IEEE Int’l. Conf. Network Protocols (1996)Google Scholar
  32. 32.
    Park, K., Kim, G., Crovella, M.: On the Effect of Traffic Self-similarity on Network Performance. In: Proc. SPIE Int’l. Conf. Perf. and Control of Network Sys., pp. 296–310 (1997)Google Scholar
  33. 33.
    Pollen, D.A.: On the neural correlates of visual perception. Cerebral Cortex 9, 4–19 (1999)CrossRefGoogle Scholar
  34. 34.
    Potts, M.A.S., Broomhead, D.S.: Time series prediciotn with a radial basis function neural network. In: SPIE Adaptive Signal Processing, vol. 1565, pp. 255–266 (1991)Google Scholar
  35. 35.
    Powell, M.J.D.: Radial basis function approximations to polynomials. In: Proc. 12th Hiennial Numerical Analysis Conf. (Dundee), pp. 223-241 (1987)Google Scholar
  36. 36.
    Puskorius, G.V., Feldkamp, L.A.: Decoupled extended Kalman filter training of feedforward layered networks. In: Proceedings of the International Joint Conference on Neural Networks, pp. 771–777 (1991)Google Scholar
  37. 37.
    Puskorius, G.V., Feldkamp, L.A.: Neurocontrol of nonlinear dynamical systems with Kalman filter trained recurrent networks. IEEE Transactions on Neural Networks 5, 279–297 (1994)CrossRefGoogle Scholar
  38. 38.
    Qiu, L., Jiang, D., Hanlen, L.: Neural network prediction of radio propagation. In: Proceedings of Communications Theory Workshop, pp. 272–277 (2005)Google Scholar
  39. 39.
    Riedi, R.H., Crouse, M.S., Ribeiro, V., Baraniuk, R.G.: A multifractal wavelet model with application to network traffic. IEEE Trans. Info. Theory 45(3), 992–1018 (1999)MathSciNetzbMATHCrossRefGoogle Scholar
  40. 40.
    Roberts, J.W.: Engineering for quality of service. Self-similar network traffic and performance evaluation. John Wiley & Sons (2000)Google Scholar
  41. 41.
    Sahinoglu, Z., Tekinay, S.: On Multimedia Networks: Self-similar Traffic and Network performance. IEEE Communications Magazine (January 1999)Google Scholar
  42. 42.
    Sang, A., Li, S.Q.: A predictability analysis of network traffic. In: Conference on Computer Communications. IEEE Infocom, New York (2000)Google Scholar
  43. 43.
    Singhal, S., Wu, L.: Training multilayer perceptrons with the extended Kalman filter algorithm. In: Advances in Neural Information Processing Systems, pp. 133–140 (1989)Google Scholar
  44. 44.
    Stallings, W.: High Speed Networks: TCP/IP, ATM Design Principles, pp. 181–207. Prentice-Hall (1998)Google Scholar
  45. 45.
    Takens, F.: Detecting Strange Attractors in Turbulance. In: Dynamical Systems and Turbulance. Warwick 1980. Lectures Notes of Mathematics, vol. 898, pp. 366–381. Springer (1981)Google Scholar
  46. 46.
    Tuan, T., Park, K.: Congestion Control for Self-similar Network Traffic. Dept.of Comp. Science, Purdue Univ., CSD-TR 98-014 (1998)Google Scholar
  47. 47.
    Veitch, D., Abry, P.: Wavelet analysis of long-range dependent traffic. IEEE Trans. Info. Theory 44(1), 2–15 (1998)MathSciNetzbMATHCrossRefGoogle Scholar
  48. 48.
    Veitch, D., Abry, P.: A wavelet based joint estimator of the parameters of long-range dependence. IEEE Trans. Inform. Theory-Special Issue on Multiscale Statistical Signal Analysis and Its Applications 45(3) (1999)Google Scholar
  49. 49.
    Vieira, F.H.T.: Predição de tráfego em redes de comunicações utilizando redes neurais e análise wavelet- Alocação dinâmica de largura de faixa. Dissertação de mestrado. Universidade Federal de Goiás, Goiânia, Goiás, BrasilGoogle Scholar
  50. 50.
    Vieira, F.H.T., Bianchi, G.R., Lee, L.L.: A network traffic prediction approach based on multifractal modeling. J. High Speed Networks 17(2), 83–96 (2010)Google Scholar
  51. 51.
    Vieira, F.H.T., Lemos, R.P., Lee, L.L.: Aplicação de Redes Neurais RBF Treinadas com Algoritmo ROLS e Análise Wavelet na Predição de Tráfego em Redes Ethernet. In: Proceedings of the VI Brazilian Conference on Neural Networks, SP-Brasil, pp. 145–150 (2003)Google Scholar
  52. 52.
    Wan, E.A.: Time series prediction by using a connectionist network with internal delay lines. In: Time Series Prediction: Forecasting the Future and Understanding the past, pp. 195–217. Addison-Wesley (1994)Google Scholar
  53. 53.
    Williams, R.J.: Training Recurrent Networks Using the Extended Kalman Filter. In: Proceedings of the International Joint Conference on Neural Networks, Baltimore, vol. IV, pp. 241–246 (1992)Google Scholar
  54. 54.
    Williams, R.J., Zipser, D.: A learning algorithm for continually running fully recurrent networks. Neural Computation 1, 270–280 (1989)CrossRefGoogle Scholar
  55. 55.
    Williams, R.J., Zipser, D.: An efficient gradient-based algorithm for on-line training of recurrent network trajectories. Neural Computation 2, 490–501 (1989)CrossRefGoogle Scholar
  56. 56.
    Yee, P.V., Haykin, S.: Regularized Radial Basis Function Networks: Theory and Applications. John Wiley (2001)Google Scholar
  57. 57.
    Yule, G.: On a Method of Investigating Periodicity in Disturbed Series with Special Reference to Wofer’s Sunspot Numbers. Phil. Trans. Roy. Soc. London A226, 267–298 (1927)Google Scholar
  58. 58.
    Zarchan, P., Musoff, H.: Fundamentals of Kalman Filtering: A Practical Approach, Revised 2nd edn. AIAA (2005)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2013

Authors and Affiliations

  • Flávio Henrique Teles Vieira
    • 1
    Email author
  • Victor Hugo Teles Costa
    • 2
  • Bruno Henrique Pereira Gonçalves
    • 2
  1. 1.School of Electrical and Computer Engineering (EEEC)Federal University of Goiás (UFG)GoiâniaBrazil
  2. 2.Federal University of GoiásGoiâniaBrazil

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