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Traffic and Congestion Control in ATM Networks Using Neuro-Fuzzy Approach

Published on March 2012 by Suriti Gupta, Vinod Kumar
journal_cover_thumbnail

Communication Security
Foundation of Computer Science USA
COMNETCS - Number 1
March 2012
Authors: Suriti Gupta, Vinod Kumar
471009d6-3b11-4750-8596-d8b88f194ddc

Suriti Gupta, Vinod Kumar . Traffic and Congestion Control in ATM Networks Using Neuro-Fuzzy Approach. Communication Security. COMNETCS, 1 (March 2012), 45-49.

@article{
author = { Suriti Gupta, Vinod Kumar },
title = { Traffic and Congestion Control in ATM Networks Using Neuro-Fuzzy Approach },
journal = { Communication Security },
issue_date = { March 2012 },
volume = { COMNETCS },
number = { 1 },
month = { March },
year = { 2012 },
issn = 0975-8887,
pages = { 45-49 },
numpages = 5,
url = { /specialissues/comnetcs/number1/5481-1009/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Special Issue Article
%1 Communication Security
%A Suriti Gupta
%A Vinod Kumar
%T Traffic and Congestion Control in ATM Networks Using Neuro-Fuzzy Approach
%J Communication Security
%@ 0975-8887
%V COMNETCS
%N 1
%P 45-49
%D 2012
%I International Journal of Computer Applications
Abstract

In this paper, a neuro-fuzzy based Call Admission Control (CAC) algorithm for ATM networks has been simulated. The algorithm presented employs neuro-fuzzy approach to calculate the bandwidth require to support multimedia traffic with QoS requirements. The neuro-fuzzy based CAC calculates bandwidth required per call using measurements of the traffic via its count-process, instead of relying on simple parameters such as the peak, average bit rate and burst length. Furthermore, to enhance the statistical multiplexing gain, the controller calculates the gain obtained from multiplexing multiple streams of traffic supported on separate virtual (i.e, class multiplexing).

References
  1. F. Bonomi and K. Fendick, “The ratebased flow control framework for the ABR ATM service,” IEEE Network, vo1.9, n0.2, pp. 25-39, 1995.
  2. F. Bonomi and K. Fendick, "The rate-based flow control framework for the ABR ATM service," IEEE Nerwork, vo1.9,no.2, pp. 25-39,1995.
  3. B. Chen, Y. Zhang, J. Yen, and W. Zhao, "Fuzzy adaptive connection admission control for real-time applications in ATM-based heterogeneous networks," Jotirnal of Intelligent and Fmq~ Systems, vo1.7, no.2, 1999.
  4. R.-G. Cheng, C.-J. Chang, and L.-F. Lin, "A QoS provisioning neural fuzzy connection admission controller for multimedia high-speed networks," IEEG'ACM Transaction on Networking, ~01.7, no.1, pp. 1 1 1-121, 1999.
  5. A. Hiramatsu, "ATM communications network control by neural networks," IEEE Transaction on netiraf networks, vol.l,no.l,pp.122-130, 1990.
  6. T.V.Lakshman, P. P. Mishra and K.K. Ramakrishnan, "Transporting compressed video over ATM networks with explicit-rate feedback control," IEEE/ACM Transactions on Networking, Vol. 7,No. 5, October 1999.
  7. Parag Jain, Sandip Vijay and S. C. Gupta “Fuzzy Congestion Control Scheme in ATM Networks”. Global Journal of Computer Science and Technology, Vol. 9 Issue 5 (Ver. 2.0), January 2010 PP 68.
Index Terms

Computer Science
Information Sciences

Keywords

Call Admission Control (CAC) ATM networks Neuro-fuzzy control