本文選題：流量矩陣 + 層析成像　； 參考：《華中師范大學》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、應用最廣泛的技術(shù)之一,然而在近些年來,隨著互聯(lián)網(wǎng)技術(shù)逐漸的成熟、互聯(lián)網(wǎng)應用的進一步普及,大量的新型網(wǎng)絡服務和應用在互聯(lián)網(wǎng)中如雨后春筍般涌現(xiàn)出來。巨大的網(wǎng)絡規(guī)模、大量的鏈路傳輸數(shù)據(jù)、許多異構(gòu)網(wǎng)絡的接入,使得網(wǎng)絡研究人員對網(wǎng)絡直接進行網(wǎng)絡測量來獲得流量矩陣已經(jīng)非常困難。流量矩陣是許多網(wǎng)絡技術(shù)的重要支撐,它反映的是每個網(wǎng)絡路徑中的流量需求,在許多工程領(lǐng)域中有著重要的應用。我們必須尋求一種新的方式來解決面臨的問題,能夠有效、快速獲取到網(wǎng)絡中的流量矩陣,為下一步的網(wǎng)絡研究打好基礎。在本文中詳細介紹了流量矩陣概念以及其獲取方法。對流量矩陣估計問題的方法及相關(guān)模型進行了總結(jié)和概括,重點介紹了層析成像技術(shù)和重力模型。流量矩陣用直接測量的方式是行不通的,只有通過估計的方式去獲取,本文的重點任務就是要解決克服流量矩陣估計問題。在流量矩陣估計方程中,OD流的數(shù)目遠大于IP網(wǎng)絡中的鏈路數(shù),所以導致這個方程為欠定的、病態(tài)的方程,求解起來非常困難。并且以往所有的模型是在理想的情況下進行的,沒有考慮鏈路噪聲的存在。本文為此就提出了一個新的模型—隨機規(guī)劃模型(Stochastic Programming Model)。通過在流量矩陣估計方程的約束函數(shù)中引入隨機變量,使原來的等式方程變?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,用隨機變量代表網(wǎng)絡中的鏈路噪聲,能夠更好地模擬現(xiàn)實的網(wǎng)絡環(huán)境。通過全面的理論分析和基于真實網(wǎng)絡數(shù)據(jù),并與經(jīng)典的層析成像重力模型(Tomogravity Model)作對比的仿真實驗,結(jié)果我們可以看出,隨機規(guī)劃模型的估計效果更好,與網(wǎng)絡的實際值更加接近。
[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.
【學位授予單位】：華中師范大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TP393.06

【共引文獻】

相關(guān)期刊論文 前1條

1 崔迪;張華;;基于馬爾科夫鏈的溢油事故應急救援船舶調(diào)度問題研究[J];中國水運(下半月);2013年03期

相關(guān)博士學位論文 前3條

1 姜潮;基于區(qū)間的不確定性優(yōu)化理論與算法[D];湖南大學;2008年

2 王保華;綜合運輸體系下快捷貨物運輸網(wǎng)絡資源配置優(yōu)化研究[D];北京交通大學;2010年

3 王莉;突發(fā)事件條件下鐵路行車組織模糊隨機優(yōu)化方法[D];北京交通大學;2012年

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本文編號：1914069