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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/133893


    Title: 壅塞最小化：透過網路流分類與簡化健忘路由
    Congestion Minimization by Flow Classification and Simplified Oblivious Routing
    Authors: 張筆翔
    Chang, Pi-Hsiang
    Contributors: 郭桐惟
    Kuo, Tung-Wei
    張筆翔
    Chang, Pi-Hsiang
    Keywords: 壅塞程度
    軟體定義網路
    線性規劃
    大象流
    健忘路由
    Network congestion
    Software-defined networks
    Linear programming
    Elephant flow
    Oblivious routing
    Date: 2020
    Issue Date: 2021-02-01 14:10:22 (UTC+8)
    Abstract: 為維持良好的網路服務品質及增加線路損壞後回復的可能性，網管人員需要盡可能地降低網路壅塞程度。另一方面，由於使用者的需求變化迅速，我們需要透過軟體定義網路即時地設定網路路由以即時降低壅塞程度。一般而言，最小化網路壅塞程度的網路路由設定可以透過線性規劃求得。然而，隨著拓樸規模增加，線性規劃的計算時間也會快速增加。為了減少計算時間，本論文區分大象流及老鼠流，並對兩者做不同的處理方式。更明確地說，我們對大象流使用線性規劃取得最小化壅塞程度的路徑規劃，並透過簡化版的健忘路由取得老鼠流的網路路由路徑。我們使用Fat tree、VL2、Bcube及Rocketfuel所提供的電信網路拓樸來進行實驗，其中在Fat tree網路中，我們的方法不僅可以有效地減少計算時間（計算時間為原有計算時間的10%），同時保持近乎最佳解的網路壅塞程度（網路壅塞程度為最佳解的1.004倍）。
    In order to maintain good service quality and increase the possibility of recovery after link failures, network administrators need to reduce network congestion as much as possible. On the other hand, due to the rapid changes in user needs, we need to set up network routing through software-defined networks to reduce congestion in real time. In general, network routing settings that minimize network congestion can be obtained through linear programming. However, as the scale of the topology increases, the calculation time of linear programming also increases rapidly. In order to reduce the calculation time, this thesis distinguishes elephant flows and mouse flows, and treats the two in different ways. More specifically, we apply linear programming on elephant flows to minimize network congestion, and obtain network routing paths of mouse flows through a simplified version of oblivious routing.
    We do experiments under Fat trees, VL2s, Bcubes and the telecommunication network topologies provided by Rocketfuel. Results show that under Fat trees, our method can not only effectively reduce the calculation time (the resulting calculation time is only 10% of the original calculation time), while maintaining near-optimal network congestion (the resulting network congestion is only 1.004 times the optimum).
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    Description: 碩士
    國立政治大學
    資訊科學系
    106753038
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0106753038
    Data Type: thesis
    DOI: 10.6814/NCCU202100161
    Appears in Collections:[Department of Computer Science ] Theses

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