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Performance Evaluation
Volume 61, Issues 2-3, July 2005, Pages 203-223
Long Range Dependence and Heavy tail Distributions
 
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doi:10.1016/j.peva.2004.11.007    
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Copyright © 2005 Elsevier B.V. All rights reserved.

Effective bandwidth for a single server queueing system with fractional Brownian input

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Sunggon Kima, Corresponding Author Contact Information, E-mail The Corresponding Author, Seung Yeob Namb, E-mail The Corresponding Author and Dan Keun Sungc, E-mail The Corresponding Author

aDepartment of Information Statistics, Gyeongsang National University, Jinju 660-701, Republic of Korea

bCyLab, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA

cDepartment of Electrical Engineering and Computer Science, KAIST, Daejeon 305-701, Republic of Korea


Available online 1 January 2005.

Abstract

The traffic patterns of today’s IP networks exhibit two important properties: self-similarity and long-range dependence. The fractional Brownian motion is widely used for representing the traffic model with the properties. We consider a single server fluid queueing system with input process of a fractional Brownian motion type. Packet-loss probability and mean delay are considered as QoS. We show that there is a scaling property among the stationary queue-length distributions of different input parameters and service rates. We also evaluate the scaling factor. From the scaling property, we drive formulas for the effective bandwidth to guarantee the QoS in a single source and multiple sources cases. The formulas indicate that it is essential to evaluate the distribution functions of a type of random variables. For multiple sources the shape of an admissible region and multiplexing gain are analyzed. Finally, numerical examples are shown to validate the proposed scheme.

Keywords: Effective bandwidth; Fractional Brownian motion; Fluid queueing system; Admissible region; Multiplexing gain

Article Outline

1. Introduction
2. The fractional Brownian model
3. Single source case
4. Multiple source case
5. Numerical evaluation of the distribution FH
6. Numerical study
Appendix A. Proof of Theorem 3
References
Vitae




Corresponding Author Contact InformationCorresponding author.

Performance Evaluation
Volume 61, Issues 2-3, July 2005, Pages 203-223
Long Range Dependence and Heavy tail Distributions
 
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