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Decision Support Systems
Volume 35, Issue 3, June 2003, Pages 335-351
 
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doi:10.1016/S0167-9236(02)00112-4    How to Cite or Link Using DOI (Opens New Window)
Copyright © 2002 Published by Elsevier Science B.V.

Tradeoff decisions in the design of a backbone computer network using visualization

Indranil Bosea, Corresponding Author Contact Information, E-mail The Corresponding Author, Kemal AltinkemerE-mail The Corresponding Author, b and Alok ChaturvediE-mail The Corresponding Author, b

a Department of Decision and Information Sciences, Warrington College of Business Administration, University of Florida, 351 Stuzin Hall, P.O. Box 117169, Gainesville, FL 32611, USA b Krannert School of Management, Purdue University, West Lafayette, IN 47907, USA

Accepted 1 April 2002. ;
Available online 2 August 2002.

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Abstract

Visualization provides a useful tool for analyzing large, complex data sets. In the design of backbone computer networks, rough-cut design decisions can gain from a visual analysis of the generated solution with respect to design parameters such as average message delay, delay cost, average message length and total network operating costs. In this paper, we show how two-dimensional and three-dimensional surface and glyph representations can be used for understanding the cost–delay tradeoffs involved in the network design problem, and an idea of the ‘efficient frontier’ where the user may choose to operate. It provides an opportunity to revisit relationships that exist between the different network design parameters as well as discover new ones.

Author Keywords: Computer network design; Mathematical programming; Visualization

Article Outline

1. Introduction
2. Use of visualization in optimization problems
3. Prior work in network visualization
4. Problem statement
5. Data generation
6. Visual techniques
7. Visualization of design parameters
7.1. Relationship between total cost, message length, message delay and delay cost in 3D
7.2. Relationship between total cost, message delay and delay cost in 2D
7.3. Relationship between fixed, variable and queuing costs and message length in 3D
7.4. Special cases
8. Conclusion
Appendix A
References
Vitae











 
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