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系統識別號 U0026-0301202005251200
論文名稱(中文) 物聯網於軟體定義網路中基於週期子流之主動載入流表條目機制
論文名稱(英文) Periodic Subflow-based Proactive Flow InstallationMechanism in SDN-based IoT
校院名稱 成功大學
系所名稱(中) 資訊工程學系
系所名稱(英) Institute of Computer Science and Information Engineering
學年度 108
學期 1
出版年 108
研究生(中文) 卓景昭
研究生(英文) Jing-Zhao Zhuo
學號 P76063011
學位類別 碩士
語文別 英文
論文頁數 48頁
口試委員 指導教授-蔡孟勳
口試委員-蘇淑茵
口試委員-劉任修
口試委員-蔡佩璇
口試委員-陳盈如
中文關鍵字 軟體定義網路  物聯網  流表條目管理 
英文關鍵字 SDN  IoT  Flow Table Management 
學科別分類
中文摘要 隨着近些年5G的發展,5G的新使用場景給產業界帶來了新的發展方向。隨着物聯網的發展,隨之而來的是網路中物聯網裝置的爆增。根據McKinsey Global Institute發表於2015的報告,截至到2025年,將會有1兆的物聯網裝置接入網路。面對大量且多樣的物聯網裝置,使用軟體定義網路是勢在必行的。但由於交換機的流表空間是有限的,並不能容納所有的流表條目。因此,我們需要一種有效的流表條目管理方式來減少封包處理延遲和信令開銷。在一般的網路中,延遲插入可以減少流表條目的數量,但會增加封包處理的延遲,導致網路效能下降。PFIM和EPFIM專爲物聯網裝置設計,他們通過偵測流量的週期性並進行流表條目的預先插入來降低封包處理延遲。但是他們無法偵測同一流量中的多個週期性,且在週期判斷的準確性和偵測時的信號開銷仍有改進的空間。

本文提出了一種基於週期性子流的主動流表條目插入機制。我們設計了一種新的數據收集方法,以提高流表條目預先插入的準確性。加入了全新的資料結構,實現了對同一流量中多種週期性流量的偵測。提出了新的演算法,實現了精準的週期計算。我們的方法相比與以往的方法,在流表命中率上有高達80%的提升,在平均每一流表命中率所需插入次數上,有高達99%的提升。
英文摘要 With the developing of 5G mobile network, 5G provides a new scenario and new direction of development for the industry, making the Internet of Things (IoT) into the golden age of development. With the developing of IoT, the number of IoT devices in the network keep increasing. According to the report of McKinsey Global Institute, which released in 2015, there will be 1 trillion IoT devices connect to the internet. SDN is necessary since ISPs require to provide various services for various IoT devices. However, the size of the flow table is limited, and it can not accommodate all flow entries for all traffic passing through. Thus, an efficient flow entry management scheme is required to reduce the processing delay and signal overhead. In general network, delayed installation is proposed, and it may reduce the number of flow entries. However, it will increase the processing delay, downgrading the network performance. PFIM and EPFIM are designed for IoT. They reduce the processing delay by detecting the periodicity of traffic and pre-installing flow entries. However, they are not able to detect multiple periodicities. Besides, there is still room for improving the accuracy of periodic detection and reducing signal overhead.

In this thesis, we propose a periodic subflow-based proactive flow installation mechanism. We design a new method of data collection to improve the accuracy of pre-installation. A new data structure is designed to enable the detection of multiple periodicities in a single flow. A new algorithm is proposed to achieve accurate periodic calculation. Compared with the previous mechanism, our mechanism improves the hit ratio of flow table hit by up to 80%, and improves the required installation per table hit up to 99%.
論文目次 中文摘要 .............i
Abstract ..............ii
Acknowledgements ............iv
Contents ..............v
List of Tables .............vii
List of Figures ............viii
1 Introduction ............1
2 Related Works .............6
2.1 Delay Installation and Expedite Eviction .......7
2.1.1 Defect on Expedited Eviction .......9
2.1.2 Defect on Delayed Installation .......9
2.1.3 Defect on Data Collection Mechanism .....9
2.2 PFIM ............11
2.2.1 Defect of Data Collection Mechanism .....11
2.2.2 Slow Reaction While periodicity Changes .....12
2.3 EPFIM ............13
2.3.1 Defect on Debug Function .......13
3 Proposed Scheme ...........15
3.1 Why Focus on Proactive-Installation .......15
3.2 Periodic Subflow-Based Proactive Flow entry Installation Mechanism
(PSPFIM) ............18
3.2.1 Improvement on Data Collection .......18
3.2.2 Byte-Count Division ........19
3.2.3 Calculating GCD of Flow Intervals ......20
3.2.4 Algorithm for Finding Periodicity in PSPFIM ....24
3.2.5 Condition for Pre-install Termination .....25
4 Simulation Setup ...........26
4.1 Table Hit Ratio(Rhit) ..........26
4.2 Required Installation per Table Hit(Rrith) ......27
4.3 Simulation Environment ..........27
4.4 Assumption ...........28
5 Performance Evaluation ...........29
5.1 Experiment 1: Improvement on Data Collection Mechanism ..29
5.1.1 Table Hit Ratio .........29
5.1.2 Required Installation per Table Hit ......33
5.2 Experiment 2: Different Period on Performance .....34
5.2.1 Table Hit Ratio .........34
5.2.2 Required Installation per Table Hit ......37
5.3 Experiment 3: Difference in Performance Between GCD Existence .38
5.3.1 Table Hit Ratio .........40
5.3.2 Required Installation per Table Hit ......40
5.4 Experiment 4: Improvement on number of OpenFlow Message ...41
6 Conclusions ............43
References .............44
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