Doctoral Dissertations
Abstract
"This dissertation proposes a new methodology for modeling and predicting network traffic. It features an adaptive architecture based on artificial neural networks and is especially suited for large-scale, global, network-centric systems. Accurate characterization and prediction of network traffic is essential for network resource sizing and real-time network traffic management. As networks continue to increase in size and complexity, the task has become increasingly difficult and current methodology is not sufficiently adaptable or scaleable. Current methods model network traffic with express mathematical equations which are not easily maintained or adjusted. The accuracy of these models is based on detailed characterization of the traffic stream which is measured at points along the network where the data is often subject to constant variation and rapid evolution. The main contribution of this dissertation is development of a methodology that allows utilization of artificial neural networks with increased capability for adaptation and scalability. Application on an operating global, broadband network, the Connexion by Boeingʼ network, was evaluated to establish feasibility. A simulation model was constructed and testing was conducted with operational scenarios to demonstrate applicability on the case study network and to evaluate improvements in accuracy over existing methods"--Abstract, page iii.
Advisor(s)
Dagli, Cihan H., 1949-
Committee Member(s)
Schachter, Paul J.
Enke, David Lee, 1965-
Ramakrishnan, Sreeram
Grasman, Scott E. (Scott Erwin)
Miller, Ann K.
Department(s)
Engineering Management and Systems Engineering
Degree Name
Ph. D. in Systems Engineering
Publisher
University of Missouri--Rolla
Publication Date
Fall 2007
Pagination
xxiv, 281 pages
Note about bibliography
Includes bibliographical references (pages 268-280).
Rights
© 2007 Douglas Keith Swift, All rights reserved.
Document Type
Dissertation - Open Access
File Type
text
Language
English
Subject Headings
Artificial intelligence
Netcentric computing
Network performance (Telecommunication)
Neural networks (Computer science)
Telecommunication -- Traffic -- Evaluation
Thesis Number
T 9693
Print OCLC #
750491503
Electronic OCLC #
758367260
Link to Catalog Record
Recommended Citation
Swift, Douglas K., "Modeling network traffic on a global network-centric system with artificial neural networks" (2007). Doctoral Dissertations. 2006.
https://scholarsmine.mst.edu/doctoral_dissertations/2006