Advertisement

Efficiency of the Prediction of High Priority Traffic in Enhancing the Rate Based Control of Low Priority Traffic

Chapter
  • 190 Downloads
Part of the IFIP — The International Federation for Information Processing book series (IFIPAICT, volume 50)

Abstract

In this paper, we study how effectively the rate based control of a low priority data transmission service in a packet switched backbone network can be implemented, when the control decisions are based on the predictions of the amount of high priority traffic. ANFIS (adaptive neuro-fuzzy inference systems) predictors are used for traffic prediction. In our service model, all control functions of the low priority data transmission service are distributed to the edge switches of the backbone network. The routes and data rates of the low priority data flows are iteratively controlled by the control system, according to the predicted data rate variations of the high priority data flows. The efficiency of the service model has been tested by simulations. The emphasis is on the effects of different traffic distributions of the high priority data flows and on the impact and importance of the amount of overhead caused by the traffic prediction and the transmission of control information.

Key words

Flow control traffic prediction packet switched networks fuzzy logic 

References

  1. [1]
    Rosenberg, S.; Aissaoui, M.; Galway, K.; Giroux, N., Functionality at the edge: designing scalable multiservice ATM networks, IEEE Communications Magazine, pages: 88–90, 95–9, May 1998, Vol. 36Google Scholar
  2. [2]
    Ahmet Sekercioglu, Y.; Pitsillides A., Intelligent control techniques for efficient regulation of ABR queue length in ATM switches, Proceedings Second IEEE Symposium on Computers and Communication (Cat. No. 97TB 100137 ), p 80–84Google Scholar
  3. [3]
    Randhawa, T.S.; Hardy, R.H.S. Estimation and prediction of VBR traffic in high-speed networks using LMS filters. International Conference on Communications, 1998. ICC 98. Pages: 253–258 vol. 1Google Scholar
  4. [4]
    Randhawa, T.S.; Hardy, R.H.S. Application of AR based model in proactive management of VBR traffic. 1998 1st IEEE International Conference on ATM. ICATM-98. Pages: 234–241Google Scholar
  5. [5]
    Bin Qiu; Liren Zhang; Wu, H.R. Fuzzy multi-step ahead prediction of VBR video sources. 1997 International Conference on Information, Communications and Signal Processing, 1997. ICICS. Pages: 1623–1626 vol. 3Google Scholar
  6. [6]
    Qixiang Pang; Shiduan Cheng; Peng Zhang. Adaptive fuzzy traffic predictor and its applications in ATM networks. 1998 IEEE International Conference on Communications, 1998. ICC 98. Pages: 1759–1763 vol. 3Google Scholar
  7. [7]
    Hall, J.; Mars, P. The limitations of artificial neural networks for traffic prediction. Third IEEE Symposium on Computers and Communications, 1998. ISCC ‘88. Pages: 8–12.Google Scholar
  8. [8]
    Callon, R. Predictions for the core of the network. IEEE Internet Computing. Jan.-Feb. 2000. Vol. 4. Pages: 60–61.Google Scholar
  9. [9]
    J.-S. R. Jang. ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Transactions on Systems, Man, and Cybernetics. Vol. 23 no. 3. May/June 1993. Issue 3, Pages: 665–685.Google Scholar
  10. [10]
    J.-S.R. lang, C.-T. Sun, Predicting chaotic time series with fuzzy if-then rules. Second IEEE International Conference on Fuzzy Systems 1993. Pages 1079–1084. vol 2.Google Scholar
  11. [11]
    Mischa Schwartz. Broadband Integrated Networks. Prentice-Hall, A Simon and Schuster Company, Upper Saddle River, New Jersey. Section 3. 5Google Scholar
  12. [12]
    D. Bertsekas, R. Gallager. Data Networks. Second edition. Prentice-Hall, Englewood Cliffs, New Jersey, 1992. Section 6. 5. 2.Google Scholar
  13. [13]
    J.-S. R. Jang, C.-T. Sun, E. Mizutani. Neuro-Fuzzy and Soft Computing — A Computational Approach to Learning and Machine Intelligence, Prentice Hall, Upper Saddle River, NJ 07458.Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2000

Authors and Affiliations

  1. 1.Telecommunications LaboratoryTampere University of TechnologyTampereFinland

Personalised recommendations