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4th Annual Communication Networks and Services Research Conference (CNSR'06)
Periodic Data Traffic Modeling and Predicition-Based Bandwith Allocation
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, "Periodic Data Traffic Modeling and Predicition-Based Bandwith Allocation," Communication Networks and Services Research, Annual Conference on, pp. 131-138, 4th Annual Communication Networks and Services Research Conference (CNSR'06), 2006.
 
 
BibTex
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@article{ 10.1109/CNSR.2006.41,
author = {Z. Liu and J. Almhana and V. Choulakian and R. McGorman},
title = {Periodic Data Traffic Modeling and Predicition-Based Bandwith Allocation},
journal ={Communication Networks and Services Research, Annual Conference on},
volume = {0},
year = {2006},
isbn = {0-7695-2578-4},
pages = {131-138},
doi = {http://doi.ieeecomputersociety.org/10.1109/CNSR.2006.41},
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 - Periodic Data Traffic Modeling and Predicition-Based Bandwith Allocation
SN - 0-7695-2578-4
SP131
EP138
A1 - Z. Liu,
A1 - J. Almhana,
A1 - V. Choulakian,
A1 - R. McGorman,
PY - 2006
KW - Internet trafic
KW - periodicity transfomt
KW - G-and-H distribution.
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
For the purpose of provisioning bandwidth for Internet access, we need to model the trafic at large time scales, over which the trafJic shows evident periodicity, long correlation and a non-Gaussian marginal distribution. To capture these characteristics simultaneously, in this paper we use a periodicity transform to identify the most sign$cant periods of the trafic and use an autoregressive time series to capture the autocorrelation and apply the G-and-H distribution to model the marginal distribution. A prediction-based bandwidth provisioning scheme is proposed and many experimental results on real Internet traces are also provided. Key words: Internet trafic, periodicity
Index Terms:
Internet trafic, periodicity transfomt, G-and-H distribution.
Citation:
Z. Liu, J. Almhana, V. Choulakian, R. McGorman, "Periodic Data Traffic Modeling and Predicition-Based Bandwith Allocation," cnsr, pp.131-138, 4th Annual Communication Networks and Services Research Conference (CNSR'06), 2006
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