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研究生：	王竣鋒 Chun-Feng Wang
論文名稱：	Heterogeneous Flow Table Distribution in Software-defined Networks
指導教授：	張貴雲 Guey-Yun Chang
口試委員:
學位類別：	碩士 Master
系所名稱：	資訊電機學院 - 資訊工程學系 Department of Computer Science & Information Engineering
論文出版年：	2015
畢業學年度：	103
語文別：	英文
論文頁數：	89
中文關鍵詞：	軟體定義網路、三態內容尋址儲存器、OpenFlow 、規模可伸縮性
外文關鍵詞：	Software-defined Networks, TCAM, OpenFlow, Scalability
相關次數：	點閱：7 下載：0
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近幾年來，軟體定義網路(SDN)已經變成了一個重要而且受歡迎的技術，這個技術提供了真實網路中開發新協議與政策的更好的彈性。而在軟體定義網路中的控制器轉換網路政策成網路規則，這些規則大多都儲存於交換器中的封包路徑表格中，而封包路徑表格通常都是用三態內容尋址儲存器(TCAM)來實作。而三態內容尋址儲存器有著一些限制，這種儲存器價格較為昂貴、耗電量高、以及高廢熱的產生使得在交換器中的三態內容尋址儲存器並不會太大，因此，若交換器需要儲存上百甚至上千條的規則的話，可能會不夠用。而其中一個解決的方案是若交換器有這樣規則的需求時，才向控制器尋問並快取這些規則，但這樣會使得封包延遲與佔用龐大的緩衝區。在此篇論文中，我們提出了一個規則分散與放置的演算法來提高效能，這個演算法會分散規則到各個交換器上，我們的演算法不僅適合用於容量小三態內容尋址儲存器的交換器上，更可保證語意上的正確性(即網路全域的行為不會受到改變)，我們在現有的系統上實作了我們的演算法並且展示一些結果來說明我們的演算法可以增進整個網路效能。

Recently, Software-defined Network (SDN) has become an important and popular technology which provides for the flexibility of developing new protocols and the policies of real networks. The controller in SDN translates network policies into rules which are installed in the flow tables (Flow tables are usually stored in ternary content addressable memory (TCAM)) of switches in the networks. Hence, TCAM usually has some critical disadvantages (e.g., high costs, power consumption and high heat generation). Flow tables cannot scale beyond a few hundred entries. Therefore, switches may need to cache rules reactively (i.e., installing rules on demand). However, when cache misses happen, switches will send the packet-in message to the controller and reactively cache the rules, which causes packet delay and large buffers. In this thesis, we propose a rule partition and allocation algorithm that distributes rules across network switches to improve the performance. Our algorithm not only is applicable to small TCAM switch scenario, but also guarantees semantically-invariant (i.e., the global action of the network is unchanged). We implement our algorithm into the real world SDN scenario and the experiment result shows that our algorithm have obviously reduced TCAM usage.

中文摘要 i
Abstract ii
Contents iii
List of Figures vi
List of Tables ix
1 INTRODUCTION 1
2 RELATED WORK 6
2.1 Table compaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2 Rule partition and allocation . . . . . . . . . . . . . . . . . . . . . . . . 8
3 THE PROPOSED ALGORITHM 10
3.1 Preliminary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.2 Heterogeneous Flow Table Distribution . . . . . . . . . . . . . . . . . . 11
3.2.1 Rule Partition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.2.2 Rule Allocation . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.3 Refinement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
4 SIMULATION 20
4.1 Rule partition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
4.1.1 Rule replications . . . . . . . . . . . . . . . . . . . . . . . . . . 21
4.2 Rule allocation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
4.2.1 Impact of shortest flow path length . . . . . . . . . . . . . . . . 22
4.2.2 Impact of the available amount of TCAM in a single switch . . . 23
4.2.3 Impact of the number of the flow . . . . . . . . . . . . . . . . . 23
4.2.4 Impact of the number of switch . . . . . . . . . . . . . . . . . . 23
5 IMPLEMENTATION 30
6 IMPLEMENTATION RESULT 34
6.1 Implementation in bus topology . . . . . . . . . . . . . . . . . . . . . . 34
6.2 Implementation in star topology . . . . . . . . . . . . . . . . . . . . . . 34
6.3 Implementation in simple fat-tree topology . . . . . . . . . . . . . . . . 35
7 CONCLUSION 40
A MAIN FUNCTION 41
A.1 rule_app.py . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
B RULE INITIATION 51
B.1 rule_generate.py . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
C PATH 61
C.1 Path.py . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
C.2 PathHasGenerate.py . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
D Enhance CBD 64
D.1 CBD.py . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
E COLORING 89
E.1 TheLast.py . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
E.2 mSwitch.py . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
E.3 Clique.py . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
E.4 V ertex.py . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
E.5 WelshPowell.py . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
F RULE NORMALIZATION 100
F.1 RuleNormalize.py . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
                                

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