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Neural network based routing optimization for ATM switching networks

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Date Issued:
1996
Summary:
This dissertation proposes amodular Artificial Neural Network (ANN) based buffer allocation and routing control model for ATM switching networks. The proposed model considers limited buffer capacity which can adversely impact the switching performance of ATM switching networks. The proposed ANN based approach takes advantage of the favorable control characteristics of neural networks such as high adaptability and high speed collective computing power for effective buffer utilization. The proposed model uses complete sharing buffer allocation strategy and enhances its performance for high traffic loads by regulating the buffer allocation process dynamically via a neural network based controller. In this study, we considered the buffer allocation problem in the context of routing optimization in ATM networks. The modular structure of the proposed model separates the buffer allocation from the actual routing of ATM cells through the switching fabric and allows adaptation of the neural control for routing to different switching structures. The influence of limited buffer capacity, routing conflicts, statistical correlation between arriving ATM cells and cell burst length on ATM switching performance are analyzed and illustrated through computer simulation.
Title: Neural network based routing optimization for ATM switching networks.
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Name(s): Sen, Ercan.
Florida Atlantic University, Degree grantor
Pandya, Abhijit S., Thesis advisor
College of Engineering and Computer Science
Department of Computer and Electrical Engineering and Computer Science
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Issuance: monographic
Date Issued: 1996
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 127 p.
Language(s): English
Summary: This dissertation proposes amodular Artificial Neural Network (ANN) based buffer allocation and routing control model for ATM switching networks. The proposed model considers limited buffer capacity which can adversely impact the switching performance of ATM switching networks. The proposed ANN based approach takes advantage of the favorable control characteristics of neural networks such as high adaptability and high speed collective computing power for effective buffer utilization. The proposed model uses complete sharing buffer allocation strategy and enhances its performance for high traffic loads by regulating the buffer allocation process dynamically via a neural network based controller. In this study, we considered the buffer allocation problem in the context of routing optimization in ATM networks. The modular structure of the proposed model separates the buffer allocation from the actual routing of ATM cells through the switching fabric and allows adaptation of the neural control for routing to different switching structures. The influence of limited buffer capacity, routing conflicts, statistical correlation between arriving ATM cells and cell burst length on ATM switching performance are analyzed and illustrated through computer simulation.
Identifier: 9780591161403 (isbn), 12491 (digitool), FADT12491 (IID), fau:9383 (fedora)
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): College of Engineering and Computer Science
Thesis (Ph.D.)--Florida Atlantic University, 1996.
Subject(s): Asynchronous transfer mode
Packet switching (Data transmission)
Neural networks (Computer science)
Held by: Florida Atlantic University Libraries
Persistent Link to This Record: http://purl.flvc.org/fcla/dt/12491
Sublocation: Digital Library
Use and Reproduction: Copyright © is held by the author, with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Use and Reproduction: http://rightsstatements.org/vocab/InC/1.0/
Host Institution: FAU
Is Part of Series: Florida Atlantic University Digital Library Collections.