本文選題：流量矩陣 + 層析成像�。� 參考：《華中師范大學(xué)》2015年碩士論文

【摘要】：互聯(lián)網(wǎng)技術(shù)是現(xiàn)如今發(fā)展速度最快、應(yīng)用最廣泛的技術(shù)之一,然而在近些年來(lái),隨著互聯(lián)網(wǎng)技術(shù)逐漸的成熟、互聯(lián)網(wǎng)應(yīng)用的進(jìn)一步普及,大量的新型網(wǎng)絡(luò)服務(wù)和應(yīng)用在互聯(lián)網(wǎng)中如雨后春筍般涌現(xiàn)出來(lái)。巨大的網(wǎng)絡(luò)規(guī)模、大量的鏈路傳輸數(shù)據(jù)、許多異構(gòu)網(wǎng)絡(luò)的接入,使得網(wǎng)絡(luò)研究人員對(duì)網(wǎng)絡(luò)直接進(jìn)行網(wǎng)絡(luò)測(cè)量來(lái)獲得流量矩陣已經(jīng)非常困難。流量矩陣是許多網(wǎng)絡(luò)技術(shù)的重要支撐,它反映的是每個(gè)網(wǎng)絡(luò)路徑中的流量需求,在許多工程領(lǐng)域中有著重要的應(yīng)用。我們必須尋求一種新的方式來(lái)解決面臨的問(wèn)題,能夠有效、快速獲取到網(wǎng)絡(luò)中的流量矩陣,為下一步的網(wǎng)絡(luò)研究打好基礎(chǔ)。在本文中詳細(xì)介紹了流量矩陣概念以及其獲取方法。對(duì)流量矩陣估計(jì)問(wèn)題的方法及相關(guān)模型進(jìn)行了總結(jié)和概括,重點(diǎn)介紹了層析成像技術(shù)和重力模型。流量矩陣用直接測(cè)量的方式是行不通的,只有通過(guò)估計(jì)的方式去獲取,本文的重點(diǎn)任務(wù)就是要解決克服流量矩陣估計(jì)問(wèn)題。在流量矩陣估計(jì)方程中,OD流的數(shù)目遠(yuǎn)大于IP網(wǎng)絡(luò)中的鏈路數(shù),所以導(dǎo)致這個(gè)方程為欠定的、病態(tài)的方程,求解起來(lái)非常困難。并且以往所有的模型是在理想的情況下進(jìn)行的,沒(méi)有考慮鏈路噪聲的存在。本文為此就提出了一個(gè)新的模型—隨機(jī)規(guī)劃模型(Stochastic Programming Model)。通過(guò)在流量矩陣估計(jì)方程的約束函數(shù)中引入隨機(jī)變量,使原來(lái)的等式方程變?yōu)楦怕是蠼夥匠?增大了方程的求解空間,從而增大尋求最優(yōu)解的可能性,并且最關(guān)鍵的是,用隨機(jī)變量代表網(wǎng)絡(luò)中的鏈路噪聲,能夠更好地模擬現(xiàn)實(shí)的網(wǎng)絡(luò)環(huán)境。通過(guò)全面的理論分析和基于真實(shí)網(wǎng)絡(luò)數(shù)據(jù),并與經(jīng)典的層析成像重力模型(Tomogravity Model)作對(duì)比的仿真實(shí)驗(yàn),結(jié)果我們可以看出,隨機(jī)規(guī)劃模型的估計(jì)效果更好,與網(wǎng)絡(luò)的實(shí)際值更加接近。
[Abstract]:Internet technology is one of the fastest growing and most widely used technologies nowadays. However, in recent years, with the maturity of Internet technology, Internet applications have become more and more popular. A large number of new network services and applications have sprung up in the Internet. Because of the huge network scale, the large amount of link transmission data, and the access of many heterogeneous networks, it is very difficult for network researchers to measure the network directly to obtain the traffic matrix. Traffic matrix is an important support of many network technologies. It reflects the traffic requirements in each network path and has important applications in many engineering fields. We must find a new way to solve the problem, which can effectively and quickly obtain the network traffic matrix, and lay a good foundation for the next network research. In this paper, the concept of flow matrix and its acquisition method are introduced in detail. The methods and models of flow matrix estimation are summarized, and the tomography technique and gravity model are introduced. It is not feasible to measure the flow matrix by direct measurement. The main task of this paper is to overcome the problem of estimating the flow matrix. The number of OD flows in the flow matrix estimation equation is much larger than the number of links in the IP network, so it is very difficult to solve this equation because it is an ill-defined and ill-defined equation. And all previous models are carried out under ideal conditions without considering the existence of link noise. In this paper, a new model, stochastic Programming model, is proposed. By introducing random variables into the constraint function of the flow matrix estimation equation, the original equation is transformed into a probabilistic solution equation, which increases the space for solving the equation and increases the possibility of seeking the optimal solution. Using random variables to represent the link noise in the network can better simulate the real network environment. Through comprehensive theoretical analysis and simulation experiments based on real network data, and compared with the classical tomogravity model, we can see that the stochastic programming model is more effective than the classical tomogravity model. Closer to the actual value of the network.
【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】：2015
【分類號(hào)】：TP393.06

【共引文獻(xiàn)】

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本文編號(hào)：1914069