本文選題：網(wǎng)絡(luò)斷層掃描　切入點：深度優(yōu)先搜索　出處：《蘭州交通大學(xué)》2014年碩士論文

【摘要】：現(xiàn)如今網(wǎng)絡(luò)規(guī)模和網(wǎng)絡(luò)復(fù)雜性日益增長，在互聯(lián)網(wǎng)的研究中，準(zhǔn)確和及時的識別路由器級互聯(lián)網(wǎng)的拓?fù)浣Y(jié)構(gòu)是目前研究的熱點和難點。運用網(wǎng)絡(luò)斷層掃描技術(shù)來推斷網(wǎng)絡(luò)拓?fù)浣Y(jié)構(gòu)，是一種極具吸引力的方法。 網(wǎng)絡(luò)斷層掃描(Network Tomography,NT)是一種采用全新思想的網(wǎng)絡(luò)測量技術(shù),它是基于醫(yī)學(xué)透視的概念提出來的。該技術(shù)是一種基于端到端的測量方法，通過反演推斷獲取網(wǎng)絡(luò)的內(nèi)部特性，并運用統(tǒng)計和推斷的原理進(jìn)行拓?fù)浣Y(jié)構(gòu)的推測。網(wǎng)絡(luò)斷層掃描技術(shù)不需要網(wǎng)絡(luò)內(nèi)部的節(jié)點的合作，只需要選擇一組接收節(jié)點即可完成對網(wǎng)絡(luò)內(nèi)部特性的推斷，解決了網(wǎng)絡(luò)內(nèi)部節(jié)點不協(xié)作的問題。然而，目前斷層掃描技術(shù)有一定的限制性，不能及時推斷出大規(guī)模的網(wǎng)絡(luò)拓?fù)浣Y(jié)構(gòu)，因為推斷大規(guī)模網(wǎng)絡(luò)拓?fù)浣Y(jié)構(gòu)需要的測量工程較大，難以實現(xiàn)。本文對基于時延特性的網(wǎng)絡(luò)拓?fù)渫茢嗉夹g(shù)進(jìn)行研究，描述了一種基于葉節(jié)點深度優(yōu)先搜索(DFS)序列的網(wǎng)絡(luò)拓?fù)渫茢嗨惴ǎ紫仁褂眠f歸二分法找出葉節(jié)點DFS序列，然后運用葉節(jié)點FS序列推斷網(wǎng)絡(luò)拓?fù)浣Y(jié)構(gòu)。該算法能夠高效的發(fā)現(xiàn)網(wǎng)絡(luò)的邏輯拓?fù)錁浣Y(jié)構(gòu)。通過大量的仿真實驗表明，該方法所需要的成對探測包的數(shù)量低于傳統(tǒng)聚類算法的15%，可以更準(zhǔn)確、更快速的推斷出網(wǎng)絡(luò)的拓?fù)浣Y(jié)構(gòu)，進(jìn)而更大程度的提高了網(wǎng)絡(luò)拓?fù)渫茢嗟男�。論文具體工作如下： 前三章介紹了課題的研究背景、研究現(xiàn)狀以及研究內(nèi)容，并介紹了本課題研究中用到的網(wǎng)絡(luò)測量技術(shù)和網(wǎng)絡(luò)斷層掃描技術(shù)的相關(guān)內(nèi)容，并且對現(xiàn)有的較成熟的網(wǎng)絡(luò)拓?fù)渫茢嗨惴ㄟM(jìn)行了概括和總結(jié)。 第四章為本文核心內(nèi)容，主要介紹了基于葉節(jié)點DFS序列的網(wǎng)絡(luò)拓?fù)渫茢嗨惴�，，并且詳�?xì)描述了使用該算法推斷網(wǎng)絡(luò)拓?fù)浣Y(jié)構(gòu)的整個過程。 第五章仿真實驗，使用NS2仿真平臺，對第四章提出的基于葉節(jié)點DFS序列的網(wǎng)絡(luò)拓?fù)渫茢嗨惴ㄟM(jìn)行了有效驗證，證明了該算法的高效性和可行性。
[Abstract]:Nowadays, network scale and network complexity are increasing day by day. In the research of Internet, accurate and timely identification of router level Internet topology is a hot and difficult point.It is an attractive method to infer the network topology by using the network tomographic technique.Network Tomography (NT) is a new network measurement technology based on the concept of medical perspective.This technique is an end-to-end measurement method, which obtains the internal characteristics of the network by inversion inference, and uses the principles of statistics and inference to infer the topology of the network.The network tomography technology does not need the cooperation of the nodes in the network, but only needs to select a group of receiving nodes to complete the inference of the internal characteristics of the network, which solves the problem of the nodes in the network not cooperating.However, at present, the technique of tomographic scanning is too restrictive to infer the large-scale network topology in time, because it is difficult to realize the large scale network topology because the measurement engineering is needed to infer the large-scale network topology structure.In this paper, the network topology inference technology based on delay characteristic is studied, and a network topology inference algorithm based on the leaf node depth first search sequence is described. Firstly, the DFS sequence of the leaf node is found by recursive dichotomy.Then the topological structure of the network is inferred by using the leaf node FS sequence.The algorithm can efficiently discover the logical topology tree of the network.A large number of simulation experiments show that the number of pairwise detection packets in this method is lower than that of the traditional clustering algorithm, and the topology of the network can be inferred more accurately and quickly.Furthermore, the efficiency of network topology inference is improved to a greater extent.The specific work of the thesis is as follows:The first three chapters introduce the research background, research status and research content, and introduce the network measurement technology and network tomography technology used in this research.The existing network topology inference algorithms are summarized and summarized.The fourth chapter is the core of this paper, mainly introduces the network topology inference algorithm based on the leaf node DFS sequence, and describes the whole process of using the algorithm to infer the network topology structure in detail.In the fifth chapter, using the NS2 simulation platform, the network topology inference algorithm based on the leaf node DFS sequence is validated, and the efficiency and feasibility of the algorithm are proved.
【學(xué)位授予單位】：蘭州交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP393.02

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