On Generator of Network Arrivals with Self-Similar Nature
碩士 === 國立交通大學 === 電信工程系 === 90 === Recent empirical studies have shown that the modern computer network traffic is much more appropriately modeled by long range dependent self-similar processes than traditional short range dependent processes such as Poisson. Hence, if long range dependen...
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ndltd-TW-090NCTU04350222015-10-13T10:05:22Z http://ndltd.ncl.edu.tw/handle/50372995399863317923 On Generator of Network Arrivals with Self-Similar Nature 自我類化網路訊務產生器之研究 Kai-Lung Hwa 花凱龍 碩士 國立交通大學 電信工程系 90 Recent empirical studies have shown that the modern computer network traffic is much more appropriately modeled by long range dependent self-similar processes than traditional short range dependent processes such as Poisson. Hence, if long range dependence is not considered for synthesizing experimental network traffic, it will lead to incorrect assessments of performance evaluation in network system. This arises the need of a well synthesizing trace with long range dependence. In this thesis, we present a filter-based method for synthesizing self-similar network traffic. This method improves the well-known methods of Paxson Fourier Transform and Random Midpoint Displacement in that the length of the synthesized traffic sequence does not need to pre-specify, and also the synthesized sequence is always non-negative. Although our method may have the drawback of becoming non-self-similar when the generated trace is aggregated under a very large window, this phenomenon turns out to match the measured behavior of true network traffic, where the self-similar nature only lasts beyond a practically manageable range, but disappears as the considered aggregated window is much further extended. Po-Ning Chen 陳伯寧 2002 學位論文 ; thesis 80 en_US |
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碩士 === 國立交通大學 === 電信工程系 === 90 === Recent empirical studies have shown that the modern computer network traffic is much more appropriately modeled by long range dependent self-similar processes than traditional short range dependent processes such as Poisson. Hence, if long range dependence is not considered for synthesizing experimental network traffic, it will lead to incorrect assessments of performance evaluation in network system. This arises the need of a well synthesizing trace with long range dependence.
In this thesis, we present a filter-based method for synthesizing self-similar network traffic. This method improves the well-known methods of Paxson Fourier Transform and Random Midpoint Displacement in that the length of the synthesized traffic sequence does not need to pre-specify, and also the synthesized sequence is always non-negative. Although our method may have the drawback of becoming non-self-similar when the generated trace is aggregated under a very large window, this phenomenon turns out to match the measured behavior of true network traffic, where the self-similar nature only lasts beyond a practically manageable range, but disappears as the considered aggregated window is much further extended.
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Po-Ning Chen |
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Po-Ning Chen Kai-Lung Hwa 花凱龍 |
author |
Kai-Lung Hwa 花凱龍 |
spellingShingle |
Kai-Lung Hwa 花凱龍 On Generator of Network Arrivals with Self-Similar Nature |
author_sort |
Kai-Lung Hwa |
title |
On Generator of Network Arrivals with Self-Similar Nature |
title_short |
On Generator of Network Arrivals with Self-Similar Nature |
title_full |
On Generator of Network Arrivals with Self-Similar Nature |
title_fullStr |
On Generator of Network Arrivals with Self-Similar Nature |
title_full_unstemmed |
On Generator of Network Arrivals with Self-Similar Nature |
title_sort |
on generator of network arrivals with self-similar nature |
publishDate |
2002 |
url |
http://ndltd.ncl.edu.tw/handle/50372995399863317923 |
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