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Published Articles >> Table of Contents >> Abstract
5th IEEE Workshop on Future Trends of Distributed Computing Systems
p. 0369
Intelligent Congestion Control in ATM Networks
Young-Keun Park, University of Minnesota
Gyungho Lee, University of Minnesota
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/FTDCS.1995.525006
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| Abstract |
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In large modern telecommunication networks, the amount of traffic and the number of nodes and links are so large that the traditional network control may not be effective due to high complexity. These networks need adaptive and intelligent systems in order to provide high network reliability, accurate traffic prediction, efficient use of channel bandwidth, and optimized network management in relation to various, dynamically changing environments. Neural networks can contribute to this emerging new telecommunication infrastructure by providing fast, flexible, adaptive, and intelligent control that cannot be performed sufficiently well by digital computers. In this paper, we discuss the neural network approaches for solving various control problems in high-speed communication networks, and present our proposed neural network model for the optimized control of input queues in ATM switches.
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 Additional Information
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Index Terms- ATM Network, B-ISDN, Bypass Queueing, Congestion Control, Neural Network
Citation:
Young-Keun Park, Gyungho Lee,
"Intelligent Congestion Control in ATM Networks,"
ftdcs,
p. 0369,
5th IEEE Workshop on Future Trends of Distributed Computing Systems,
1995
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