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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/7675

    Authors: 尤家祥;Chia-Hsiang Yu
    Contributors: 統計研究所
    Keywords: 長範圍相關;分數綜合自還原移動平均;封包;long-range-dependence;Fractional ARIMA;packet
    Date: 2006-06-05
    Issue Date: 2009-09-22 11:02:04 (UTC+8)
    Publisher: 國立中央大學圖書館
    Abstract: 有別於過去電路交換網路的系統,現今的電信網路大都是改成了封包交換網路之網路系統。過去十多年來已有許多的文獻發現了封包交換網路系統中的封包傳輸的量並不符合一個Poisson過程取而代之的是,現在的封包資料顯現出兩個重要的特徵: self-similar與long-range-dependence。這也代表著我們有必要採用一個新的模型以保留封包資料的特徵。在這份研究中,我們主要是採用一個稱之為fractional ARIMA(FARIMA)的模型,此模型主要是利用傳統時間序列模型的概念並加以衍生。採用FARIMA模型最主要的好處就是它能在同一時間捕捉到資料中的long-range-dependence與short-range-dependence的特性。 在論文的最後,將以真實的網路資料來做分析。 本篇研究中的資料主要是由美國伊利諾州Naperville的Lucent Technologies的區域網路所收集得到。我們將針對乙太網路中各種不同的網路服務型態的封包資料(如:http與ftp的封包資料)配適為一FARIMA模型,並利用一些統計方法來檢查資料的long-range-dependence特性以及FARIMA模型中的參數估計。 Departing from the circuit-switched scheme, the transition of internet traffic has been designed to be packet switched. Over the last decade, fairly rich studies have shown that the transition of internet packets is not Poisson like. Instead, these packets reveal to hav two important features: self-similarity and long-range-dependence. This suggests that a new modeling technique for the internet traffice is necessary. By utilizing the ideas from traditional time series models, in this study we consider a method called fractional ARIMA(FARIMA). The main benefit we gain by using FARIMA models is that it captures well both the long-range-dependence and the short-range-dependence of the data under consideration. For application purposed, we also construct the FARIMA models for various types of Ethernet traffic (e.g. http and ftp data) gathered on a LAN at Lucent Technologies in NAperville, IL. Statistical techniques are used to detect the long-range-dependence property and alsoto estimate the parameters in the proposed FARIMA model.
    Appears in Collections:[統計研究所] 博碩士論文

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