本文選題：流量矩陣 + 流量工程　； 參考：《華中師范大學(xué)》2014年碩士論文

【摘要】：網(wǎng)絡(luò)流量矩陣反映了網(wǎng)絡(luò)內(nèi)部每一對(duì)源至目的網(wǎng)絡(luò)節(jié)點(diǎn)間的流量大小。許許多多網(wǎng)絡(luò)工程和網(wǎng)絡(luò)管理項(xiàng)目如負(fù)載均衡、擁塞控制、網(wǎng)絡(luò)安全等都建立在流量矩陣基礎(chǔ)之上。因此,流量矩陣具有非常大的實(shí)際意義。但是,在目前的實(shí)際網(wǎng)絡(luò)中,由于不同網(wǎng)絡(luò)設(shè)備廠商所生產(chǎn)的設(shè)備對(duì)于流量測(cè)量功能的不同支持,通過(guò)直接測(cè)量獲得準(zhǔn)確的流量矩陣是十分耗時(shí)、耗資的。相比之下,通過(guò)結(jié)合數(shù)學(xué)方法進(jìn)行流量矩陣估計(jì)的方法變得更為可行。但是,盡管幾十年來(lái)許多學(xué)者對(duì)流量矩陣估計(jì)問(wèn)題付出了相當(dāng)多的努力,但是準(zhǔn)確的估計(jì)方法依然沒(méi)有能夠產(chǎn)生。本文首先綜述了目前國(guó)際國(guó)內(nèi)關(guān)于流量矩陣估計(jì)的現(xiàn)有研究,在比較和分析了目前存在的許多有代表性方法及其存在的缺陷之后,分別提出了從方法原理、數(shù)學(xué)模型、現(xiàn)代優(yōu)化理論三個(gè)不同研究角度產(chǎn)生的三種流量矩陣估計(jì)方法,進(jìn)而詳述了這三個(gè)方法的思路由來(lái)、模型建立、優(yōu)缺點(diǎn)分析。最后,采用Abilene網(wǎng)絡(luò)真實(shí)數(shù)據(jù)的實(shí)驗(yàn)分別證實(shí)了這三種方法的有效性,誤差都減少了一半以上。第一種所提出的方法稱(chēng)為Advanced-Tomogravity。這個(gè)方法建立在精確的流量重力特征模型和tomography方法之上。通過(guò)引入相關(guān)因子向量參數(shù)至目前存在的流量重力特征模型,提出了精確的流量重力特征模型,進(jìn)而可以針對(duì)具體的某個(gè)網(wǎng)絡(luò)來(lái)設(shè)置相應(yīng)的向量參數(shù)值,實(shí)現(xiàn)估計(jì)準(zhǔn)確性的提高。通過(guò)數(shù)學(xué)的分析與公式推導(dǎo),獲得了該相關(guān)因子向量參數(shù)明確的賦值表達(dá)式。這個(gè)表達(dá)式的推導(dǎo)過(guò)程用到了廣義逆和最小二乘解等相關(guān)的數(shù)學(xué)基礎(chǔ)理論。采用了美國(guó)Abilene網(wǎng)絡(luò)真實(shí)網(wǎng)絡(luò)數(shù)據(jù)的仿真實(shí)驗(yàn)驗(yàn)證了所提出方法的有效性。仿真實(shí)驗(yàn)結(jié)果證實(shí)該方法不僅能夠更好的追蹤流量大小波動(dòng)特性的能力,而且能夠更準(zhǔn)確的逼近流整體的平均值趨勢(shì)。第二種方法Tomofanout建立在所提出的結(jié)合邊緣鏈路負(fù)載信息的Fanout模型之上。新的Fanout模型具有原來(lái)Fanout模型的性質(zhì),并且采用了邊緣鏈路負(fù)載信息,因而具有更佳的準(zhǔn)確性。另外,通過(guò)該模型獲得的估計(jì)結(jié)果進(jìn)一步由期望最大化迭代進(jìn)行處理以符合流量矩陣估計(jì)的初等模型。第三種方法稱(chēng)為MNETME,通過(guò)該方法采用了路由矩陣的廣義逆與鏈路負(fù)載向量的乘積作為神經(jīng)網(wǎng)絡(luò)的輸入來(lái)進(jìn)行訓(xùn)練與預(yù)測(cè)。并且該方法結(jié)合了期望最大化迭代作為對(duì)神經(jīng)網(wǎng)絡(luò)輸出數(shù)據(jù)的進(jìn)一步處理。得益于這些,與同類(lèi)方法相比,該方法用于訓(xùn)練的數(shù)據(jù)量少,而結(jié)果卻更準(zhǔn)確。
[Abstract]:The network traffic matrix reflects the traffic size of each pair of source to destination network nodes in the network. Many network engineering and network management projects such as load balancing congestion control and network security are based on the flow matrix. Therefore, the flow matrix has great practical significance. However, in the actual network at present, it is very time-consuming and expensive to obtain accurate flow matrix by direct measurement because of the different support of the equipment produced by different network equipment manufacturers for the flow measurement function. By contrast, it is more feasible to estimate the flow matrix with mathematical method. However, although many scholars have made considerable efforts to estimate the flow matrix in recent decades, accurate estimation methods have not been produced. In this paper, the current research on the estimation of flow matrix is summarized. After comparing and analyzing many representative methods and their defects, the paper puts forward the method principle and mathematical model, respectively. Three kinds of flow matrix estimation methods from three different research angles of modern optimization theory are presented, and the origin of the three methods, the establishment of models and the analysis of their advantages and disadvantages are described in detail. Finally, the validity of the three methods is verified by using the real data of Abilene network, and the error is reduced by more than half. The first method proposed is called Advanced-Tomogravity. This method is based on the accurate flow gravity characteristic model and tomography method. By introducing the correlation factor vector parameter to the current flow gravity characteristic model, an accurate flow gravity characteristic model is proposed, and the corresponding vector parameter value can be set for a specific network. Improve the accuracy of estimation. Through mathematical analysis and formula derivation, the explicit assignment expression of the parameters of the correlation factor vector is obtained. The derivation of the expression uses the basic mathematical theories such as the generalized inverse and the least square solution. The effectiveness of the proposed method is verified by the simulation of the real network data of the Abilene network in the United States. The simulation results show that the proposed method can not only track the fluctuation characteristics of the flow, but also approach the average trend of the whole flow more accurately. The second method, Tomofanout, is based on the proposed Fanout model combining edge link load information. The new Fanout model has the property of the original Fanout model and adopts the edge link load information, so it has better accuracy. In addition, the estimated results obtained from the model are further processed by the expectation maximization iteration to fit the primary model estimated by the flow matrix. The third method is called MNETME.The method uses the product of the generalized inverse of the routing matrix and the link load vector as the input of the neural network to train and predict. The method combines the expected maximization iteration as the further processing of the output data of the neural network. Because of these, compared with the similar method, this method has less data for training, but the result is more accurate.
【學(xué)位授予單位】：華中師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TP393.06

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1 周海峰;網(wǎng)絡(luò)流量矩陣估計(jì)方法研究[D];華中師范大學(xué);2014年

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本文編號(hào)：1883967