本文選題：流量矩陣 + 壓縮感知　； 參考：《北京交通大學(xué)》2014年碩士論文

【摘要】：互聯(lián)網(wǎng)技術(shù)作為21世紀(jì)發(fā)展最快的技術(shù)之一,已經(jīng)廣泛應(yīng)用到人們的生產(chǎn)、生活當(dāng)中,對(duì)社會(huì)的進(jìn)步、經(jīng)濟(jì)的發(fā)展做出了巨大的貢獻(xiàn)。然而,隨著互聯(lián)網(wǎng)技術(shù)的進(jìn)一步成熟,近年來也涌現(xiàn)出了大量的新型網(wǎng)絡(luò)應(yīng)用和服務(wù),它們?cè)诮o人們帶來便捷的同時(shí),也給網(wǎng)絡(luò)運(yùn)營(yíng)商的維護(hù)管理帶來了巨大的壓力。與此同時(shí),數(shù)量眾多的異構(gòu)網(wǎng)絡(luò)的接入,使得互聯(lián)網(wǎng)變得更加難以掌控。通過網(wǎng)絡(luò)測(cè)量能夠獲知網(wǎng)絡(luò)性能的一些重要參數(shù),從而可以更好的管理網(wǎng)絡(luò)。因此,如何有效的進(jìn)行網(wǎng)絡(luò)測(cè)量成為了一個(gè)重要的研究課題。 流量矩陣是網(wǎng)絡(luò)測(cè)量的一個(gè)重要參數(shù),它反映了一個(gè)網(wǎng)絡(luò)中所有源節(jié)點(diǎn)和目的節(jié)點(diǎn)對(duì)之間的流量需求,是網(wǎng)絡(luò)規(guī)劃和流量工程的一個(gè)重要輸入。目前常利用網(wǎng)絡(luò)層析成像技術(shù)估算流量矩陣,它是從鏈路級(jí)的測(cè)量數(shù)據(jù)中估算出路徑級(jí)的參數(shù)。網(wǎng)絡(luò)層析成像技術(shù)的本質(zhì)決定了流量矩陣估算問題是一個(gè)欠約束問題,這就需要增加一些先驗(yàn)信息作為約束條件以得到最優(yōu)解,由此衍生出了許多先驗(yàn)?zāi)Ｐ�。由于多�?shù)大型網(wǎng)絡(luò)的鏈路測(cè)量值有限,以往的先驗(yàn)?zāi)Ｐ筒荒芤罁?jù)少量的鏈路信息有效的估算出流量矩陣,因此不能應(yīng)用于大型網(wǎng)絡(luò)。 壓縮感知理論表明任何充分可壓縮信號(hào)能夠利用少量非適應(yīng)性的隨機(jī)線性投影樣本進(jìn)行重構(gòu)。該理論適用于流量矩陣的估算,于是提出了一種基于壓縮感知技術(shù)的概率模型。概率模型能夠從少量的鏈路信息中重構(gòu)出流量矩陣,因此適用于大型網(wǎng)絡(luò)的流量矩陣估算。 本文通過仿真實(shí)驗(yàn)驗(yàn)證了概率模型的有效性,并使用真實(shí)網(wǎng)絡(luò)的流量數(shù)據(jù),對(duì)比概率模型和經(jīng)典的重力模型,證明了概率模型的估算效果更好。將概率模型應(yīng)用于真實(shí)網(wǎng)絡(luò)的流量矩陣估算時(shí),難點(diǎn)在于如何確定符合真實(shí)網(wǎng)絡(luò)的概率參數(shù)。為了解決這一問題,本文提出了重力-概率模型。利用重力模型估算得到的流量矩陣計(jì)算概率參數(shù),然后將該參數(shù)用于概率模型。使用真實(shí)網(wǎng)絡(luò)數(shù)據(jù)的仿真實(shí)驗(yàn)表明,重力-概率模型的估算效果優(yōu)于普通的概率模型和重力模型。
[Abstract]:As one of the fastest developing technologies in the 21st century, Internet technology has been widely used in people's production and life, and has made a great contribution to social progress and economic development. However, with the further maturity of Internet technology, a large number of new network applications and services have emerged in recent years, which not only bring convenience to people, but also bring great pressure to the maintenance and management of network operators. At the same time, access to a large number of heterogeneous networks makes the Internet more difficult to control. Some important parameters of network performance can be obtained by network measurement, which can better manage the network. Therefore, how to effectively carry out network measurement has become an important research topic. Traffic matrix is an important parameter of network measurement, it reflects the traffic requirement between all source nodes and destination nodes in a network, and it is an important input of network planning and traffic engineering. At present, network tomography is often used to estimate the flow matrix, which estimates the parameters of the path level from the measurement data at the link level. The nature of network tomography technology determines that the flow matrix estimation problem is an underconstrained problem, which requires the addition of some prior information as a constraint condition to obtain the optimal solution, and many prior models are derived. Due to the limited value of link measurement in most large networks, previous prior models can not effectively estimate the flow matrix based on a small amount of link information, so it can not be applied to large networks. Compression sensing theory shows that any fully compressible signal can be reconstructed using a small number of random linear projection samples. This theory is applicable to the estimation of traffic matrix, so a probability model based on compressed sensing technology is proposed. The probability model can reconstruct the traffic matrix from a small amount of link information, so it can be used to estimate the traffic matrix of large networks. In this paper, the validity of the probabilistic model is verified by the simulation experiment, and the probability model is compared with the classical gravity model by using the flow data of the real network, and it is proved that the probabilistic model is more effective. When the probabilistic model is applied to the estimation of the real network flow matrix, the difficulty lies in how to determine the probability parameters in accordance with the real network. In order to solve this problem, a gravity-probability model is proposed in this paper. The probability parameter is calculated by using the flow matrix estimated by gravity model and then applied to the probability model. The simulation results using real network data show that the gravimetric probability model is better than the ordinary probability model and gravity model.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP393.06

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本文編號(hào)：1970478