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研究生: 張剴為
Chang, Kae-Wei
論文名稱: 五維封包分類使用二階層三元內容位址記憶體
Five-Dimensional Packet Classification Using 2-Level TCAM
指導教授: 張燕光
Chang, Yeim-Kuan
學位類別: 碩士
Master
系所名稱: 電機資訊學院 - 資訊工程學系
Department of Computer Science and Information Engineering
論文出版年: 2008
畢業學年度: 96
語文別: 英文
論文頁數: 139
中文關鍵詞: 封包分類兩階段TCAM架構任意範圍的通訊阜
外文關鍵詞: Port Number or Range, 2-Level TCAM architecture, Arbitrary range, Packet classification
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  • 網際網路快速的發展,有越來越多的高品質的網路服務或是網路應用程式需要被實做在現今的路由器(router)上,例如 QoS ( Quality of Service )、網路的資訊安全、多媒體通訊等等…。所以封包分類的技術因而被提出來滿足這些需求,為了要分配封包到特定的封包串流(flow),路由器(router)必須根據封包的表頭(header)資訊並且搜尋一系列封包分類的規則將封包分類至特定的封包串流(flow)。
    傳統上,三元內容位址記憶體(Ternary Content Addressable Memory) 常被用來解決IP lookup的問題,因為它擁有快速跟平行搜尋的特性,此外因為它可以儲存三種邏輯狀態 (“0”, “1”, “don’t care”) 所以它也非常適合用來儲存Prefix格式的資料。但是在五維的封包分類中,它包含了兩維任意範圍的通訊阜號碼(Port number),所以對於TCAM而言,它並不適合儲存此種格式的資料。此種情形下我們或許需要使用大量的TCAM位址空間去儲存一條封包分類的規則。
    在此篇論文中我們提出了一個兩接層的TCAM架構及相關的配套方法去解決五維封包分類的問題,我們所提出的方法的基本概念是,我們複製五維的位址空間並且設計一個策略將所有的規則分類,此外我們也設計了一系列裁切 ”分類規則” 的方法好讓我們可以達成五維封包分類的目標。在我們所提出的方法中,我們不但可以保有TCAM快速及平行搜尋的好處並且不會使用很大量的TCAM位址空間去儲存封包分類的規則表。

    The Internet is growing very rapidly. There are more and more high-quality Internet services and network applications are demanded in modern-day router, such as quality of service (QOS), security, and multimedia communication…etc. The packet classification is a proposed function for these demands. In order to classify a packet to a particular flow, the router must perform a search over a set of rules using multiple fields of the packet’s header as the search key.
    Traditionally, ternary content addressable memory (TCAM) has been adopted to solve the IP lookup problem due to their ability to perform fast and parallel searching. TCAM is very suitably for storing prefix. Because it can store three logic states (1, 0, don’t care) in a TCAM cell. But in five dimensional packet classification, it contains two fields arbitrary rang. In this situation, we may need many TCAM entries to store a classification rule.
    In this paper, we propose 2-Level TCAM architecture and relative complete method and strategy for five-dimensional packet classification. The basic idea of our scheme is that we duplicate the address space and we develop a strategy to group all packet classification rules. Besides, we also devise a method to trim the packet classification rules to help us to achieve our goal. Finally, in our proposed scheme, we can keep the advantage of TCAM and we won’t result in large number of packet classification rules.

    CHAPTER 1 INTRODUCTION 1 CHAPTER 2 RELATED WORK 4 2.1 TCAM INTRODUCTION 4 2.1.1 TCAM architecture 5 2.1.2 TCAM searching operation 7 2.2 BINARY TRIE 8 2.3 PACKET CLASSIFICATION OVERVIEW: 10 2.3.1 Grid-of-Trie 13 2.3.2 Extended Grid-of-Trie 19 2.4 MOTIVATION OF THIS PAPER 20 2.4.1 2-Level TCAM Architecture for One-Dimensional Range 20 2.4.2 Range conversion schemes 22 2.4.2.1 Disjoint range-to-prefix conversion scheme 23 2.4.2.2 Contiguous range (Elementary Intervals) conversion scheme 25 2.5 DIRECT CONVERT SCHEME 26 CHAPTER 3 PROPOSED SCHEME 28 3.1 EXTENDED RULE & RULE STRUCTURE 30 3.2 TWO-LEVEL TCAM ARCHITECTURE FOR FIVE-DIMENSIONAL PACKET CLASSIFICATION 35 3.3 STRATEGY OF RULE GROUPING 38 3.3.1 The Definitions of Any Two Packet Classification Rule’s Relation in A Five - Dimensional Address Space 38 3.3.1.1 Prefixes Dimension 39 3.3.1.2 Range Dimensions 43 3.3.1.3 Protocol Number Dimension 48 3.3.1.4 Summary of Packet Classification Rule’s Relation 49 3.3.2 Independent Set (Layer) 51 3.3.3 The Method of Rule Cutting 60 3.3.3.1 Two-Dimensional Prefix Cutting 64 3.3.3.2 Two-Dimensional Range Cutting 70 3.3.3.3 Summary of two-dimensional prefix cutting & two-dimensional range 75 3.3.4 Subgroup 76 3.3.5 Enhanced Mode of Rule Grouping 84 3.4 THE PRIORITY OF EXTENDED RULE IN TCAM 86 3.5 THE METHOD OF RANGE SPLIT 93 3.5.1 Choosing Range Dimension 93 3.5.2 Determine Reference Rule and Reference Position 100 3.5.3 The Policy of the Split 102 3.5.4 Conclusion of the Range Split 110 CHAPTER 4 EXPERIMENTAL RESULT 116 CHAPTER 5 CONCLUSION 137 CHAPTER 6 REFERENCE 138

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