隨著互聯網滲透到人們生活的方方面面(物聯網),計算機網絡變得日漸龐大、復雜。在這種情況下,用不影響監測網絡性能的方式獲得指標和度量值,并進行及時有效的網絡監測和分析就變得至關重要。然而,測量網絡中所有節點的網絡流量是不切實際的,一種有前景的替代方案是僅在網絡邊緣進行測量,并從這些測量值中推斷網絡的內部行為。為了解決內部鏈路參數測量(例如時延和丟包率)的問題,本文采用網絡層析(NT)技術,收集基于端到端測量的路徑性能數據,然后使用統計計算的方法推斷邏輯鏈路性能的概率分布。這種從端到端估計鏈路性能的技術既不需要內部網絡的協作,也不依賴通信協議。此外,本文在網絡層析成像技術上融合了一種新的統計方法,使得建模網絡更容易估計內部鏈路性能參數的性能。這種方法就是機器學習(ML),尤其是線性回歸模型。該技術能夠在給定輸入值(例如路徑時延)后預測真實值(例如鏈路時延),比如說讓模型從給定的樣本數據(學習數據大約占總數據的80%)中學習,然后使用20%的數據(測試數據)驗證模型。將估計得到的時延值與使用網絡模擬器NS2對一個有線網絡仿真生成的實際時延值進行比較,以兩者的計算誤差值(均方誤差MSE)評估模...

【文章頁數】：61 頁

【學位級別】：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



本文編號：4000335