Full Text:
<3400>
CLC number: TP393
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 0000-00-00
Cited: 1
Clicked: 10977
Congestion control for ATM multiplexers using neural networks: multiple sources/single buffer scenario
| |||||||||||||
DU Shu-xin, YUAN Shi-yong. Congestion control for ATM multiplexers using neural networks: multiple sources/single buffer scenario[J]. Journal of Zhejiang University Science A, 2004, 5(9): 1124-1129.
@article{title="Congestion control for ATM multiplexers using neural networks: multiple sources/single buffer scenario",
author="DU Shu-xin, YUAN Shi-yong",
journal="Journal of Zhejiang University Science A",
volume="5",
number="9",
pages="1124-1129",
year="2004",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2004.1124"
}
%0 Journal Article
%T Congestion control for ATM multiplexers using neural networks: multiple sources/single buffer scenario
%A DU Shu-xin
%A YUAN Shi-yong
%J Journal of Zhejiang University SCIENCE A
%V 5
%N 9
%P 1124-1129
%@ 1869-1951
%D 2004
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2004.1124
TY - JOUR
T1 - Congestion control for ATM multiplexers using neural networks: multiple sources/single buffer scenario
A1 - DU Shu-xin
A1 - YUAN Shi-yong
J0 - Journal of Zhejiang University Science A
VL - 5
IS - 9
SP - 1124
EP - 1129
%@ 1869-1951
Y1 - 2004
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2004.1124
Abstract: A new neural network based method for solving the problem of congestion control arising at the user network interface (UNI) of ATM networks is proposed in this paper. Unlike the previous methods where the coding rate for all traffic sources as controller output signals is tuned in a body, the proposed method adjusts the coding rate for only a part of the traffic sources while the remainder sources send the cells in the previous coding rate in case of occurrence of congestion. The controller output signals include the source coding rate and the percentage of the sources that send cells at the corresponding coding rate. The control methods not only minimize the cell loss rate but also guarantee the quality of information (such as voice sources) fed into the multiplexer buffer. Simulations with 150 ADPCM voice sources fed into the multiplexer buffer showed that the proposed methods have advantage over the previous methods in the aspect of the performance indices such as cell loss rate (CLR) and voice quality.
[1] Diagle, J., Langford, J., 1986. Models for analysis packet voice communications systems. IEEE Journal on Selected Areas in Communications, 4(6):847-855.
[2] Fan, Z., Mars, P., 1997. Access flow control scheme for ATM networks using neural-network-based traffic prediction. IEE Proc. Commun., 144(5):295-300.
[3] Habib, I.W., Sasdawi, T.N., 1995. Access control of bursty voice traffic in ATM networks. Computer Networks and ISDN Systems, 27(10):1411-1427.
[4] Habib, I.W., Tarraf, A., Saadawi, T., 1997. A neural network controller for congestion control in ATM multipexers. Computer Networks and ISDN Systems, 29(3):325-334.
[5] Haykin, S., 1994. Neural Networks. Macmillan, New York.
[6] Jagannathan, S., Talluri, J., 2002. Adaptive predictive congestion control of high-speed ATM networks. IEEE Trans. Broadcasting, 48(2):129-139.
[7] Lee, S.J., Hou, C.L., 2000. A neural-fuzzy system for congestion control in ATM networks. IEEE Trans. System, Man, and Cybernetics(Part B: Cybernetics, 30(1):2-9.
[8] Tarraf, A.A., Habib, W., Saadawi, T.N., 1995. Congestion Control Mechanism for ATM Networks Using Neural Networks. Proceeding of IEEE International Conference on Communications, IEEE, p.206-210.
Open peer comments: Debate/Discuss/Question/Opinion
<1>