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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Adas, A.: Supporting Real Time VBR Video Using Dynamic Reservation Based on Linear Prediction. In: Infocom 1996 (1996)
ATM Forum, Traffic Management v.4.0 (August 1996)
Black, Uyless: ATM Foundation for Broadband Networks, Febuary 1999, vol. 1. Prentice Hall PTR, New Jersey (1999)
Hayes, M.H.: Statistical Signal Processing and Modeling. John Wiley & Sons, Chichester (1996)
Haykin, S.: Adaptive Filter Theory. Prentice Hall, Englewood Cliffs (1991)
Jang, B., Kim, B.G., Pecelli, G.: A Prediction Algorithm for Feedback Control Models with Long Delays. In: IEEE BSS (1997)
Mascolo, S., Cavendish, D., Gerla, M.: ATM Rate Based Congestion Control Using a Smith Predictor: an EPRCA Implementation. Infocom (1996)
Ritter, M.: Network Buffer Requirements of the Rate-based Control Mechanism for ABR Services. Infocom (1996)
Zurada, J.M.: Introduction to Artificial Neural Systems. WEST, MN (1992)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lee, KO., Bae, SH., Koh, JG., Kwon, CH., Cheung, CS., Ra, IH. (2004). Traffic Control Scheme of ABR Service Using NLMS in ATM Network. In: Laganá, A., Gavrilova, M.L., Kumar, V., Mun, Y., Tan, C.J.K., Gervasi, O. (eds) Computational Science and Its Applications – ICCSA 2004. ICCSA 2004. Lecture Notes in Computer Science, vol 3043. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24707-4_39
Download citation
DOI: https://doi.org/10.1007/978-3-540-24707-4_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22054-1
Online ISBN: 978-3-540-24707-4
eBook Packages: Springer Book Archive