本文關(guān)鍵詞： 流量建模 時(shí)序分析 節(jié)假日影響　出處：《華中科技大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：網(wǎng)絡(luò)流量的時(shí)序特征分析是網(wǎng)絡(luò)測(cè)量領(lǐng)域的傳統(tǒng)問題。一般認(rèn)為，大量用戶的行為變化可以導(dǎo)致網(wǎng)絡(luò)流量發(fā)生相應(yīng)的變化。在有些移動(dòng)通信網(wǎng)絡(luò)中已經(jīng)觀測(cè)到用戶每天或者每周行為產(chǎn)生的流量周期性特征。在本文中，我們以一個(gè)IP骨干網(wǎng)絡(luò)的流量作為研究對(duì)象，通過時(shí)序分析的方法考察中國(guó)節(jié)假日對(duì)流量的影響問題。 本文從某電信運(yùn)營(yíng)商處獲取了一組IP骨干網(wǎng)的為期523天網(wǎng)絡(luò)流量，包括12個(gè)骨干節(jié)點(diǎn)的日峰均值。我們開發(fā)了一套數(shù)據(jù)分析算法，試圖分析獲得骨干節(jié)點(diǎn)流量在中國(guó)春節(jié)和國(guó)慶節(jié)時(shí)的變化特征。我們首先對(duì)網(wǎng)絡(luò)流量數(shù)據(jù)進(jìn)行了預(yù)處理，通過小波變換和小波變換模極大值法尋找流量數(shù)據(jù)的奇異點(diǎn)，進(jìn)而對(duì)缺失或者異常的數(shù)據(jù)進(jìn)行了修復(fù)性的處理。接下來，我們對(duì)網(wǎng)絡(luò)流量進(jìn)行流量分解，獲取其整體增長(zhǎng)趨勢(shì)和周期性波動(dòng)分量。我們疊加網(wǎng)絡(luò)的周期性分量獲得了各骨干節(jié)點(diǎn)的3周和5周的周期流量模板。最后，我們對(duì)數(shù)據(jù)集內(nèi)的春節(jié)和國(guó)慶節(jié)期間的流量數(shù)據(jù)和節(jié)點(diǎn)模板進(jìn)行對(duì)比，分析節(jié)假日期間網(wǎng)絡(luò)流量的波動(dòng)情況。 結(jié)果表明，七天的中國(guó)假期會(huì)對(duì)各節(jié)點(diǎn)的常規(guī)性周期分量有所影響。進(jìn)入春節(jié)之后，各節(jié)點(diǎn)的流量會(huì)不同程度的有所下降；究其原因與中國(guó)春節(jié)的人群遷移性有關(guān)，，在節(jié)日開始階段，大部分人可能在忙于與家人團(tuán)聚，相應(yīng)的上網(wǎng)流量比平時(shí)有所減少。進(jìn)入國(guó)慶節(jié)之后，各節(jié)點(diǎn)的流量會(huì)不同程度的有所上升，但是基本上沒有改變流量的每周的周期性流量；究其原因，人們?cè)诩偃掌陂g有更多的時(shí)間可以上網(wǎng)，因此流量呈現(xiàn)較為均勻的提升效果本文的研究結(jié)果，一個(gè)星期的周期流量的峰值和谷值特征受到國(guó)慶節(jié)放假的影響不大。
[Abstract]:The temporal characteristic analysis of network traffic is a traditional problem in the field of network measurement. The behavior changes of a large number of users can lead to corresponding changes in network traffic. In some mobile communication networks, the periodic characteristics of traffic generated by users' daily or weekly behavior have been observed. We take the traffic of an IP backbone network as the research object and investigate the impact of Chinese holidays on the traffic through the method of time series analysis. In this paper, we obtain a group of IP backbone network traffic for 523 days from a telecom operator, including the daily peak mean of 12 backbone nodes. We develop a set of data analysis algorithm. This paper tries to analyze the changing characteristics of the traffic of backbone nodes in Chinese Spring Festival and National Day. Firstly, we preprocess the network traffic data. Through wavelet transform and wavelet transform modulus maximum method to find the singular point of traffic data, then the missing or abnormal data are repaired. Next, we decompose the network traffic. We get the whole growth trend and the periodic fluctuation component. The periodic components of the superimposed network are used to obtain the three and five week periodic flow templates of each backbone node. Finally. We compare the traffic data and node templates between Spring Festival and National Day in the data set, and analyze the fluctuation of network traffic during holidays. The results show that seven days of Chinese holiday will have an effect on the regular periodic component of each node. After entering the Spring Festival, the flow rate of each node will decrease to some extent; The reason is related to the migration of people in Chinese Spring Festival. At the beginning of the festival, most people may be busy with family reunion, the corresponding Internet traffic is lower than usual. The flow of each node will increase to a certain extent, but basically does not change the flow of the weekly periodic flow; The reason is that people have more time to access the Internet during the holidays, so the traffic presents a more uniform enhancement effect of the results of this paper. The peak and valley characteristics of a week's cycle flow are not affected by the National Day holiday.
【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN929.5

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