A shifted gamma distribution model for long-range dependent Internet traffic

Sunggon Kim; Ju Yong Lee; Dan Keun Sung

doi:10.1109/LCOMM.2002.808400

A shifted gamma distribution model for long-range dependent Internet traffic

Sunggon Kim
, Ju Yong Lee
, Dan Keun Sung

Department of Statistics

Korea Advanced Institute of Science and Technology

Research output: Contribution to journal › Article › peer-review

49 Scopus citations

Abstract

It is important to characterize the distributional property and the correlation structure of traffic arrival processes in modeling internet traffic. The conventional fractional Gaussian noise(fGn) model fails in characterizing the distributional property when the distribution of the input traffic rates is nongaussian. We propose a shifted gamma distribution model which can solve this problem. A linear-time generation algorithm is also given.

Original language	English
Pages (from-to)	124-126
Number of pages	3
Journal	IEEE Communications Letters
Volume	7
Issue number	3
DOIs	https://doi.org/10.1109/LCOMM.2002.808400
State	Published - Mar 2003

Keywords

Fractional Gaussian noise
Internet traffic modeling
Long-range dependence
Shifted gamma distribution

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10.1109/LCOMM.2002.808400

Cite this

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title = "A shifted gamma distribution model for long-range dependent Internet traffic",

abstract = "It is important to characterize the distributional property and the correlation structure of traffic arrival processes in modeling internet traffic. The conventional fractional Gaussian noise(fGn) model fails in characterizing the distributional property when the distribution of the input traffic rates is nongaussian. We propose a shifted gamma distribution model which can solve this problem. A linear-time generation algorithm is also given.",

keywords = "Fractional Gaussian noise, Internet traffic modeling, Long-range dependence, Shifted gamma distribution",

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year = "2003",

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AU - Lee, Ju Yong

AU - Sung, Dan Keun

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N2 - It is important to characterize the distributional property and the correlation structure of traffic arrival processes in modeling internet traffic. The conventional fractional Gaussian noise(fGn) model fails in characterizing the distributional property when the distribution of the input traffic rates is nongaussian. We propose a shifted gamma distribution model which can solve this problem. A linear-time generation algorithm is also given.

AB - It is important to characterize the distributional property and the correlation structure of traffic arrival processes in modeling internet traffic. The conventional fractional Gaussian noise(fGn) model fails in characterizing the distributional property when the distribution of the input traffic rates is nongaussian. We propose a shifted gamma distribution model which can solve this problem. A linear-time generation algorithm is also given.

KW - Fractional Gaussian noise

KW - Internet traffic modeling

KW - Long-range dependence

KW - Shifted gamma distribution

UR - https://www.scopus.com/pages/publications/0037349351

U2 - 10.1109/LCOMM.2002.808400

DO - 10.1109/LCOMM.2002.808400

M3 - Article

AN - SCOPUS:0037349351

SN - 1089-7798

VL - 7

SP - 124

EP - 126

JO - IEEE Communications Letters

JF - IEEE Communications Letters

IS - 3

ER -

A shifted gamma distribution model for long-range dependent Internet traffic

Abstract

Keywords

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