發(fā)布時(shí)間：2024-07-03 01:45

　　隨著互聯(lián)網(wǎng)滲透到人們生活的方方面面(物聯(lián)網(wǎng)),計(jì)算機(jī)網(wǎng)絡(luò)變得日漸龐大、復(fù)雜。在這種情況下,用不影響監(jiān)測(cè)網(wǎng)絡(luò)性能的方式獲得指標(biāo)和度量值,并進(jìn)行及時(shí)有效的網(wǎng)絡(luò)監(jiān)測(cè)和分析就變得至關(guān)重要。然而,測(cè)量網(wǎng)絡(luò)中所有節(jié)點(diǎn)的網(wǎng)絡(luò)流量是不切實(shí)際的,一種有前景的替代方案是僅在網(wǎng)絡(luò)邊緣進(jìn)行測(cè)量,并從這些測(cè)量值中推斷網(wǎng)絡(luò)的內(nèi)部行為。為了解決內(nèi)部鏈路參數(shù)測(cè)量(例如時(shí)延和丟包率)的問題,本文采用網(wǎng)絡(luò)層析(NT)技術(shù),收集基于端到端測(cè)量的路徑性能數(shù)據(jù),然后使用統(tǒng)計(jì)計(jì)算的方法推斷邏輯鏈路性能的概率分布。這種從端到端估計(jì)鏈路性能的技術(shù)既不需要內(nèi)部網(wǎng)絡(luò)的協(xié)作,也不依賴通信協(xié)議。此外,本文在網(wǎng)絡(luò)層析成像技術(shù)上融合了一種新的統(tǒng)計(jì)方法,使得建模網(wǎng)絡(luò)更容易估計(jì)內(nèi)部鏈路性能參數(shù)的性能。這種方法就是機(jī)器學(xué)習(xí)(ML),尤其是線性回歸模型。該技術(shù)能夠在給定輸入值(例如路徑時(shí)延)后預(yù)測(cè)真實(shí)值(例如鏈路時(shí)延),比如說讓模型從給定的樣本數(shù)據(jù)(學(xué)習(xí)數(shù)據(jù)大約占總數(shù)據(jù)的80%)中學(xué)習(xí),然后使用20%的數(shù)據(jù)(測(cè)試數(shù)據(jù))驗(yàn)證模型。將估計(jì)得到的時(shí)延值與使用網(wǎng)絡(luò)模擬器NS2對(duì)一個(gè)有線網(wǎng)絡(luò)仿真生成的實(shí)際時(shí)延值進(jìn)行比較,以兩者的計(jì)算誤差值(均方誤差MSE)評(píng)估模...

【文章頁數(shù)】：61 頁

【學(xué)位級(jí)別】：碩士

【文章目錄】：
摘要
ABSTRACT
LIST OF ABBREVIATIONS
Chapter 1 Introduction
    1.1 Introduction
        1.1.1 Network Tomography in the Internet
    1.2 Problem Statement
    1.3 Motivation
    1.4 Contribution
    1.5 Thesis Layout
Chapter 2 Literature Review
    2.1 Introduction
        2.1.1 Computer Network
        2.1.2 Tomography
        2.1.3 Network Tomograph
    2.2 Delay Network Tomography
        2.2.1 Estimation of Propagation Link Delay
        2.2.2 Estimation of Link Delay Density
    2.3 Traffic Network Tomography
        2.3.1 Vardi's model
        2.3.2 Vanderbei and lannone's model
        2.3.3 Maximum likelihood parameter estimation
        2.3.4 The EM algorithm
        2.3.5 Other aspects for the Network Tomography
Chapter 3 Statistica Inference
    3.1 Introduction
    3.2 Categories of Machine Learning
        3.2.1 Supervised Learning
        3.2.2 Unsupervised Learning
        3.2.3 Reinforcement Learning
    3.3 Linear Regression
        3.3.1 Simple Regression
        3.3.2 Multiple Regression
    3.4 The Loss Function
Chapter 4 Methodology
    4.1 Introduction
    4.2 Simulation methodology
        4.2.1 Capabilities and limitations of ns2
        4.2.2 Network simulation using ns2
        4.2.3 Network Animator NAM
    4.3 The Designed Model
        4.3.1 Machine Learning implementation
    4.4 End-To-end Delay
        4.4.1 The measurement procedure
Chapter 5 Evaluation and Results
    5.1 Introduction
    5.2 Result and Discussion
Chapter 6 Conclusion and Future Work
    6.1 Conclusion
    6.2 Future Work
Bibliography
ACKNOWLEDGEMENT
PUBLICATIONS AND MASTER ACTIVITIES



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