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4th Annual Communication Networks and Services Research Conference (CNSR'06)
Traffic Modeling with Gamma Mixtures and Dynamical Bandwidth Provisioning
Moncton, New Brunswick, Canada
May 24-May 25
ISBN: 0-7695-2578-4
ASCII Text
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Z. Liu, J. Almhana, V. Choulakian, R. McGorman, "Traffic Modeling with Gamma Mixtures and Dynamical Bandwidth Provisioning," Communication Networks and Services Research, Annual Conference on, pp. 123-130, 4th Annual Communication Networks and Services Research Conference (CNSR'06), 2006.
 
 
BibTex
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@article{ 10.1109/CNSR.2006.54,
author = {Z. Liu and J. Almhana and V. Choulakian and R. McGorman},
title = {Traffic Modeling with Gamma Mixtures and Dynamical Bandwidth Provisioning},
journal ={Communication Networks and Services Research, Annual Conference on},
volume = {0},
year = {2006},
isbn = {0-7695-2578-4},
pages = {123-130},
doi = {http://doi.ieeecomputersociety.org/10.1109/CNSR.2006.54},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Communication Networks and Services Research, Annual Conference on
TI - Traffic Modeling with Gamma Mixtures and Dynamical Bandwidth Provisioning
SN - 0-7695-2578-4
SP123
EP130
A1 - Z. Liu,
A1 - J. Almhana,
A1 - V. Choulakian,
A1 - R. McGorman,
PY - 2006
KW - Gamma Mixture distribution
KW - EM algorithm
KW - Internet trajfic
KW - stochastic approximation.
VL - 0
JA - Communication Networks and Services Research, Annual Conference on
ER -
 
Z. Liu, University of Moncton, Canada
J. Almhana, University of Moncton, Canada
V. Choulakian, University of Moncton, Canada
R. McGorman, Lodex International Consulting, Canada
Dynamic bandwidth provisioning for data service is an efJicient way to optimize network resource utilization. For the purpose of dynamical bandwidth provisioning, we need a model that can capture online the trafJic characteristics and facilitate mathematical analysis. A mixture of Gamma distributions can approximate any distribution with nonnegative support as closely as desired. It can not only characterizejield data well, but also facilitate analytically tractable results forpedormance evaluation. This paper uses a Gamma mixture to model Znternet trafJic and proposes an online algorithm to fit the model to actual Internet trafJic. The jitted model is applied to provision bandwidth dynamically, and many simulation and experimental results are also provided.
Index Terms:
Gamma Mixture distribution, EM algorithm, Internet trajfic, stochastic approximation.
Citation:
Z. Liu, J. Almhana, V. Choulakian, R. McGorman, "Traffic Modeling with Gamma Mixtures and Dynamical Bandwidth Provisioning," cnsr, pp.123-130, 4th Annual Communication Networks and Services Research Conference (CNSR'06), 2006
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