Intelligent congestion control in ATM networks

Young Keun Park, Gyungho Lee

Research output: Contribution to conferencePaper

4 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages369-375
Number of pages7
Publication statusPublished - 1995
Externally publishedYes
EventProceedings of the 5th IEEE Computer Society Workshop on Future Trends of Distributed Computing Systems - Cheju Island, South Korea
Duration: 1995 Aug 281995 Aug 30

Other

OtherProceedings of the 5th IEEE Computer Society Workshop on Future Trends of Distributed Computing Systems
CityCheju Island, South Korea
Period95/8/2895/8/30

ASJC Scopus subject areas

  • Computer Science(all)
  • Engineering(all)

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    Park, Y. K., & Lee, G. (1995). Intelligent congestion control in ATM networks. 369-375. Paper presented at Proceedings of the 5th IEEE Computer Society Workshop on Future Trends of Distributed Computing Systems, Cheju Island, South Korea, .