Traffic Control Scheme of ABR Service Using NLMS in ATM Network

  • Kwang-Ok Lee
  • Sang-Hyun Bae
  • Jin-Gwang Koh
  • Chang-Hee Kwon
  • Chong-Soo Cheung
  • In-Ho Ra
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3043)

Abstract

ATM ABR service controls network traffic using feedback information on the network congestion situation in order to guarantee the demanded service qualities and the available cell rates. In this paper we apply the control method using queue length prediction to the formation of feedback information for more efficient ABR traffic control. If backward node receive the longer delayed feedback information on the impending congestion, the switch can be already congested from the uncontrolled arriving traffic and the fluctuation of queue length can be inefficiently high in the continuing time intervals.

The feedback control method proposed in this paper predicts the queue length in the switch using the slope of queue length prediction function and queue length changes in time-series. The predicted congestion information is backward to the node. NLMS and neural network are used as the predictive control functions, and they are compared from performance on the queue length prediction. Simulation results show the efficiency of the proposed method compared to the feedback control method without the prediction. Therefore, we conclude that the efficient congestion and stability of the queue length controls are possible using the prediction scheme that can resolve the problems caused from the longer delays of the feedback information.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Adas, A.: Supporting Real Time VBR Video Using Dynamic Reservation Based on Linear Prediction. In: Infocom 1996 (1996) Google Scholar
  2. 2.
    ATM Forum, Traffic Management v.4.0 (August 1996)Google Scholar
  3. 3.
    Black, Uyless: ATM Foundation for Broadband Networks, Febuary 1999, vol. 1. Prentice Hall PTR, New Jersey (1999)Google Scholar
  4. 4.
    Hayes, M.H.: Statistical Signal Processing and Modeling. John Wiley & Sons, Chichester (1996)Google Scholar
  5. 5.
    Haykin, S.: Adaptive Filter Theory. Prentice Hall, Englewood Cliffs (1991)MATHGoogle Scholar
  6. 6.
    Jang, B., Kim, B.G., Pecelli, G.: A Prediction Algorithm for Feedback Control Models with Long Delays. In: IEEE BSS (1997) Google Scholar
  7. 7.
    Mascolo, S., Cavendish, D., Gerla, M.: ATM Rate Based Congestion Control Using a Smith Predictor: an EPRCA Implementation. Infocom (1996)Google Scholar
  8. 8.
    Ritter, M.: Network Buffer Requirements of the Rate-based Control Mechanism for ABR Services. Infocom (1996) Google Scholar
  9. 9.
    Zurada, J.M.: Introduction to Artificial Neural Systems. WEST, MN (1992)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Kwang-Ok Lee
    • 1
  • Sang-Hyun Bae
    • 1
  • Jin-Gwang Koh
    • 2
  • Chang-Hee Kwon
    • 3
  • Chong-Soo Cheung
    • 3
  • In-Ho Ra
    • 4
  1. 1.Dept. of Computer Science & StatisticsChosun UniversityKorea
  2. 2.Dept. of Computer ScienceSunchon National UniversityKorea
  3. 3.Division of IT, Computer Engineering, and E-CommerceHansei UniversityKorea
  4. 4.School of Electronic and Information EngineeringKunsan National University 

Personalised recommendations