Modeling VBR traffic with autoregressive Gaussian processes

Jung Shian Li

doi:10.1109/ICON.2000.875835

Modeling VBR traffic with autoregressive Gaussian processes

Jung Shian Li

Institute of Computer and Communication Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Recent studies about network traffic measurement show that today's network traffic exhibits long-range dependence (LRD). The computation effort of generating LRD traffic is directly proportional to the length of the traces. This paper presents a traces-generating framework based on TES (transform-expand-samples) and synthetic autoregressive Gaussian processes. The proposed scheme can fit both the probability density function and the autocorrelation of the empirical traces. Besides, the computation effort of this scheme is independent of the length of the LRD traces.

Original language	English
Title of host publication	Proceedings - IEEE International Conference on Networks 2000
Subtitle of host publication	Networking Trends and Challenges in the New Millennium, ICON 2000
Number of pages	1
DOIs	https://doi.org/10.1109/ICON.2000.875835
Publication status	Published - 2000 Dec 1
Event	2000 IEEE International Conference on Networks: Networking Trends and Challenges in the New Millennium, ICON 2000 - Singapore, Singapore Duration: 2000 Sep 5 → 2000 Sep 8

Publication series

Name	IEEE International Conference on Networks, ICON
ISSN (Print)	1556-6463

Other

Other	2000 IEEE International Conference on Networks: Networking Trends and Challenges in the New Millennium, ICON 2000
Country/Territory	Singapore
City	Singapore
Period	00-09-05 → 00-09-08

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Software
Electrical and Electronic Engineering
Safety, Risk, Reliability and Quality

Access to Document

10.1109/ICON.2000.875835

Cite this

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title = "Modeling VBR traffic with autoregressive Gaussian processes",

abstract = "Recent studies about network traffic measurement show that today's network traffic exhibits long-range dependence (LRD). The computation effort of generating LRD traffic is directly proportional to the length of the traces. This paper presents a traces-generating framework based on TES (transform-expand-samples) and synthetic autoregressive Gaussian processes. The proposed scheme can fit both the probability density function and the autocorrelation of the empirical traces. Besides, the computation effort of this scheme is independent of the length of the LRD traces.",

author = "Li, {Jung Shian}",

year = "2000",

month = dec,

day = "1",

doi = "10.1109/ICON.2000.875835",

language = "English",

isbn = "0769507778",

series = "IEEE International Conference on Networks, ICON",

booktitle = "Proceedings - IEEE International Conference on Networks 2000",

note = "2000 IEEE International Conference on Networks: Networking Trends and Challenges in the New Millennium, ICON 2000 ; Conference date: 05-09-2000 Through 08-09-2000",

}

Li, JS 2000, Modeling VBR traffic with autoregressive Gaussian processes. in Proceedings - IEEE International Conference on Networks 2000: Networking Trends and Challenges in the New Millennium, ICON 2000., 875835, IEEE International Conference on Networks, ICON, 2000 IEEE International Conference on Networks: Networking Trends and Challenges in the New Millennium, ICON 2000, Singapore, Singapore, 00-09-05. https://doi.org/10.1109/ICON.2000.875835

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N2 - Recent studies about network traffic measurement show that today's network traffic exhibits long-range dependence (LRD). The computation effort of generating LRD traffic is directly proportional to the length of the traces. This paper presents a traces-generating framework based on TES (transform-expand-samples) and synthetic autoregressive Gaussian processes. The proposed scheme can fit both the probability density function and the autocorrelation of the empirical traces. Besides, the computation effort of this scheme is independent of the length of the LRD traces.

AB - Recent studies about network traffic measurement show that today's network traffic exhibits long-range dependence (LRD). The computation effort of generating LRD traffic is directly proportional to the length of the traces. This paper presents a traces-generating framework based on TES (transform-expand-samples) and synthetic autoregressive Gaussian processes. The proposed scheme can fit both the probability density function and the autocorrelation of the empirical traces. Besides, the computation effort of this scheme is independent of the length of the LRD traces.

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DO - 10.1109/ICON.2000.875835

M3 - Conference contribution

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T3 - IEEE International Conference on Networks, ICON

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T2 - 2000 IEEE International Conference on Networks: Networking Trends and Challenges in the New Millennium, ICON 2000

Y2 - 5 September 2000 through 8 September 2000

ER -

Modeling VBR traffic with autoregressive Gaussian processes

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