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  1. NTU Theses and Dissertations Repository
  2. 電機資訊學院
  3. 電機工程學系
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/30512
完整後設資料紀錄
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dc.contributor.advisor王勝德
dc.contributor.authorSheng-Hsun Choen
dc.contributor.author卓昇勳zh_TW
dc.date.accessioned2021-06-13T02:05:57Z-
dc.date.available2009-07-16
dc.date.copyright2007-07-16
dc.date.issued2007
dc.date.submitted2007-06-30
dc.identifier.citation[1] T. V. Lakshman and D. Stidiais, “High speed policy-based packet fowarding using efficient multidimensional range matching,” in Proc. ACM SIGCOMM, Sep. 1998, pp. 191-202.
[2] F. Baboescu and G. Varghese “Scalable Packet Classification,” IEEE/ACM Trans. Networking, vol. 13, Issue 1, pp. 2–14, Feb. 2005.
[3] T. Srinivasan, N. Dhanasekar, M. Nivedita, R. Dhivyakrishnan, and A. A. Azeezunnisa, “Scalable and Parallel Aggregated Bit Vector Packet Classification using Prefix Computation Model,” in Proc. PARELEC, 2006, pp. 139-144.
[4] J. Li, H. Liu, and K. Sollins, Scalable packet classification using bit vector aggregating and folding, MIT, Dept., Comput. Sci., Apr. 2003, Tech. Rep. MIT-LCS-TM-637.
[5] P. C. Wang, H. Y. Chang, C. T. Chan, and S. C. Hu, “Scalable Packet Classification Using Condensate Bit Vector,” IEICE Trans. Communications, Vol. E88-B, No.4, pp. 1440-1447, 2005.
[6] C. R. Hsu, C. Chen, and C. Y. Lin, “Fast Packet Classification Using Bit Compression,” in Proc. IEEE GLOBECOM Conf., 2005, vol. 2, pp. .
[7] J. van Lunteren, “Searching very large routing tables in fast sram,” in Proc. IEEE ICCCN Conf., Oct. 2001, pp. 4-11.
[8] J. van Lunteren, “Searching very large routing tables in wide embedded memory,” in Proc. IEEE GLOBECOM Conf., Nov. 2001, vol. 3, pp.1615-1619.
[9] J. van Lunteren and T. Engbersen, “Fast and scalable packet classification,” IEEE Journal on Selected Areas in Communications, vol. 21, pp. 560-570, May. 2003.
[10] P. Gupta and N. McKeown, “Packet classification using hierarchical intelligent cuttings,” IEEE Micro, vol. 20, no. 1, pp. 34-41, Feb. 2000.
[11] P. Gupta and N. McKeown, “Packet classification on multiple fields,” in Proc. ACM SIGCOMM, Aug 1999, vol. 9, no. 4, pp. 147-160.
[12] PClassEval. [Online]. Available: http://www.arl.wustl.edu/~hs1/PClassEval.html
[13] D. E. Taylor and J. S. Turner, “ClassBench: A Packet Classification Benchmark,” in Proc. INFOCOMM, Mar. 2005, vol. 3, pp. 2068-2079.
[14] D. E. Taylor, “Survey and taxonomy of packet classification techniques,” ACM computing Surveys, vol. 37, no. 3, pp. 238-275, Sep. 2005.
[15] P. C. Wang, C. L. Lee, C. T. Chan, and H. Y. Chang, “Hardware-based Packet Classification Made Fast and Efficient,” in Proc. 11th International Conference on Parallel and Distributed Systems (ICPADS'05), Jul. 2005, pp. 47-51.
[16] S. Singh, F. Baboescu, G. Varghese, and J. Wang, “Packet classification using multidimensional cutting,” in Proc. of ACM SIGCOMM, Aug. 2003, pp. 213-224.
[17] V. Srinivasan, G.. Varghese, S. Suri, and M. Waldvogel, “Fast and scalable layer four switching,” in Proc. of ACM SIGCOMM, Sep. 1998, pp. 191-202.
[18] F. Baboescu, S. Singh, and G. Varghese, “Packet classification for core routers: Is there alternative to CAMs?” in Proc. IEEE INFCOMM, Mar. 2003, pp. 53-63.
[19] Memory-Memory (2000). [Online]. Available: http://www.memorymemory.com
[20] Xilinx, ML405 Evaluation Platform User Guide, UG201 v1.2, Mar. 2007.
[21] E. Spitznagel, D. Taylor, and J. Turner, “Packet classification using extended TCAMs,” in Proc. IEEE ICNP, Nov. 2003, pp. 120-131.
[22] D. Pao, C. Liu, A. Wu, L. Yeung, and K. S. Chan, “Efficient Hardware Architecture for Fast IP Address Lookup”, in Proc. IEE Computers and Digital Techniques, Jan. 2003, Vol. 150, Issue 1, pp. 43-52.
[23] H. Song and J. Lockwood, “Efficient packet classification for network intrusion detection using FPGA,” IEEE International Symposium on FPGAs, Feb. 2005, pp. 238-245.
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/30512-
dc.description.abstract在防火牆 (Firewall) 與入侵偵測 (Intrusion Detection) 等網路安全機制中,封包分類 (Packet Classification) 是一個是很重要的部份,其作用在於使用封包檔頭 (packet header) 中傳輸層 (Transport Layer) 與網路層 (Network Layer) 這兩層的欄位,來決定封包是否符合規則資料庫 (rule database) 中的某條規則。目前這個領域已經許多的演算法被提出來,但我們發現,雖然這些演算法在某個條件下或是使用某種類型的規則資料庫時,所使用的記憶體空間非常小,但若是換了另一套規則資料庫,其記憶體使用量可能無法令人接受。由於硬體平台的記憶體容量通常很小,並且一定是事前就必須規劃好的,若要將這些記憶體需求無法固定的封包分類演算法實作為硬體,則可能會發生記憶體不足的情形。為了解決這個問題,這篇論文提出了一個封包分類的架構,稱為 PBV (Probable Bit Vector),結合了 Bit Vector、Aggregate、Folding、規則重新排序 (rule rearrangement)、我們所提出的資料結構,以及硬體線路等。使用個架構,我們可以保證在任何情況下,記憶體的需求都不會超過一個很小的上限,而且實驗證明其平均的速度效能還是在可接受的範圍內。zh_TW
dc.description.abstractPacket classification is an important part of many Internet security applications, such as firewalls and intrusion detection. A packet classifier uses packet header information to decide if a packet matches any rule in a rule database. There exist many algorithms in this research area. However, many of them have the drawback of requiring a large amount of memory storage in general and consume small amount of memory only in some particular conditions, like using some kind of rule databases or with several restrictions. When the contents of the rule database changes, the memory requirement may become unaffordable, even the rule number remains the same. If those packet classifiers are going to be implemented on hardware, they may not be accepted due to the memory requirement and the limited amount of memory on hardware. To overcome this problem, we proposed a packet classification architecture called Probable Bit Vector (PBV), which combines the concepts of aggregated and folded bit vectors, the rule rearrangement, the Split IP Index Table data structure, and FPGA hardware circuits. With this architecture, we can guarantee that in any case the maximum amount of memory requirement will not exceed a relatively small number, and experiments with synthetically generated rule databases have showed that the average performance is still acceptable.en
dc.description.provenanceMade available in DSpace on 2021-06-13T02:05:57Z (GMT). No. of bitstreams: 1
ntu-96-R94921092-1.pdf: 863433 bytes, checksum: c47a8a2ce1fc1e48c71cc35fa99297e7 (MD5)
Previous issue date: 2007
en
dc.description.tableofcontents論文口試委員審定書 i
致謝 ii
中文摘要 iii
Abstract iv
第一章 序論 1
1.1 處理速度 1
1.1.1 預先處理速度 1
1.1.2 搜尋速度 2
1.1.3 更新速度 3
1.2 空間需求 4
第二章 相關研究 5
第三章 方法 8
3.1 問題描述 8
3.2 基礎演算法 9
3.2.1 位元向量 (Bit Vecotr,BV) 10
3.2.2 聚集 (Aggregation) 12
3.2.3 摺積 (Folding) 13
3.3 Probable Bit Vector 14
3.4 資料結構 16
3.4.1 分割 IP 索引表格 17
3.4.2 使用通訊協定欄位來重排規則 19
3.4.3 不考慮 BV (Don’t Care BV) 22
3.4 封包分類流程 23
3.5 記憶體需求 25
第四章 實做過程 28
4.1 演算法軟體實做與模擬 28
4.2 硬體描述語言模擬 30
4.3 FPGA 實做 31
第五章 實驗結果 33
5.1 實驗資料與單位 33
5.2 PBV參數與記憶體讀取次數 34
5.3.1 聚集單位與記憶體讀取次數 34
5.3.2 摺積常數與記憶體讀取次數 36
5.3.3 摺積群組單位與記憶體讀取次數 37
5.3.4 使用 port 欄位與記憶體讀取次數 38
5.3.5 使用 IP 索引表格數與記憶體讀取次數 40
5.3.6 不同 IP 分割、記憶體類型與記憶體讀取次數 41
5.3演算法比較 42
5.4 硬體實作結果 43
5.5 討論 45
第六章 結論 46
參考文獻 47
dc.language.isozh-TW
dc.subjectFPGAzh_TW
dc.subject封包分類zh_TW
dc.subject位元向量zh_TW
dc.subject聚集zh_TW
dc.subject折積zh_TW
dc.subjectFPGAen
dc.subjectPacket Classificationen
dc.subjectBit Vectoren
dc.subjectAggregateen
dc.subjectFoldingen
dc.title低記憶體容量需求的封包分類架構zh_TW
dc.titleA Packet Classification Architecture with Low Storage Requirementsen
dc.typeThesis
dc.date.schoolyear95-2
dc.description.degree碩士
dc.contributor.oralexamcommittee洪士灝,黃鐘揚,楊佳玲,薛智文
dc.subject.keyword封包分類,位元向量,聚集,折積,FPGA,zh_TW
dc.subject.keywordPacket Classification,Bit Vector,Aggregate,Folding,FPGA,en
dc.relation.page49
dc.rights.note有償授權
dc.date.accepted2007-07-03
dc.contributor.author-college電機資訊學院zh_TW
dc.contributor.author-dept電機工程學研究所zh_TW
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