Intelligent congestion control in ATM networks

Young Keun Park, Kyung Ho Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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
Title of host publicationProceedings of the IEEE Computer Society Workshop on Future Trends of Distributed Computing Systems
Place of PublicationLos Alamitos, CA, United States
PublisherIEEE
Pages369-375
Number of pages7
Publication statusPublished - 1995 Jan 1
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

Fingerprint

Congestion control (communication)
Asynchronous transfer mode
Neural networks
Telecommunication networks
Intelligent control
Adaptive systems
Network management
Automatic teller machines
Digital computers
Intelligent systems
Telecommunication traffic
Telecommunication
Switches
Bandwidth

ASJC Scopus subject areas

  • Computer Science(all)
  • Engineering(all)

Cite this

Park, Y. K., & Lee, K. H. (1995). Intelligent congestion control in ATM networks. In Proceedings of the IEEE Computer Society Workshop on Future Trends of Distributed Computing Systems (pp. 369-375). Los Alamitos, CA, United States: IEEE.

Intelligent congestion control in ATM networks. / Park, Young Keun; Lee, Kyung Ho.

Proceedings of the IEEE Computer Society Workshop on Future Trends of Distributed Computing Systems. Los Alamitos, CA, United States : IEEE, 1995. p. 369-375.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Park, YK & Lee, KH 1995, Intelligent congestion control in ATM networks. in Proceedings of the IEEE Computer Society Workshop on Future Trends of Distributed Computing Systems. IEEE, Los Alamitos, CA, United States, pp. 369-375, Proceedings of the 5th IEEE Computer Society Workshop on Future Trends of Distributed Computing Systems, Cheju Island, South Korea, 95/8/28.
Park YK, Lee KH. Intelligent congestion control in ATM networks. In Proceedings of the IEEE Computer Society Workshop on Future Trends of Distributed Computing Systems. Los Alamitos, CA, United States: IEEE. 1995. p. 369-375
Park, Young Keun ; Lee, Kyung Ho. / Intelligent congestion control in ATM networks. Proceedings of the IEEE Computer Society Workshop on Future Trends of Distributed Computing Systems. Los Alamitos, CA, United States : IEEE, 1995. pp. 369-375
@inproceedings{017fc5c7513e4a0883682cca6052d18b,
title = "Intelligent congestion control in ATM networks",
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.",
author = "Park, {Young Keun} and Lee, {Kyung Ho}",
year = "1995",
month = "1",
day = "1",
language = "English",
pages = "369--375",
booktitle = "Proceedings of the IEEE Computer Society Workshop on Future Trends of Distributed Computing Systems",
publisher = "IEEE",

}

TY - GEN

T1 - Intelligent congestion control in ATM networks

AU - Park, Young Keun

AU - Lee, Kyung Ho

PY - 1995/1/1

Y1 - 1995/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=0029201869&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0029201869&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0029201869

SP - 369

EP - 375

BT - Proceedings of the IEEE Computer Society Workshop on Future Trends of Distributed Computing Systems

PB - IEEE

CY - Los Alamitos, CA, United States

ER -