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High-Speed Packet Classification Using Binary Search on Length

High-Speed Packet Classification Using Binary Search on Length. Authors: Hyesook Lim and Ju Hyoung Mun Presenter: Yi-Sheng, Lin ( 林意勝 ) Date: Jan. 14, 2008 Publisher/Conf. : ANCS’07 , 2007. Dept. of Computer Science and Information Engineering

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High-Speed Packet Classification Using Binary Search on Length

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  1. High-Speed Packet Classification Using Binary Search on Length Authors: Hyesook Lim and Ju Hyoung Mun Presenter: Yi-Sheng, Lin (林意勝) Date: Jan. 14, 2008 Publisher/Conf. : ANCS’07, 2007 Dept. of Computer Science and Information Engineering National Cheng Kung University, Taiwan R.O.C.

  2. Outline • Introduction • Area-based quad-trie • Binary Search on Prefix Length • Proposed Work • Optimization Technique • Simulation Results

  3. Introduction • We propose an algorithm which applies the binary search on prefix length into the area-based quad-trie for packet classification. • Two new optimization techniques are also proposed.

  4. Area-based quad-trie

  5. Binary Search on Prefix Length

  6. Proposed Work • We propose to separate the area-based quad-trie according to the level of the trie • Storing rules and internal nodes of each level into the corresponding hash table • Performing binary search on those hash tables(Quad-trie table). • Rule table : storing rules with the remaining fields • Each entry of the hash table has a rule table pointer which indicates the highest priority rule among the rules mapped into the corresponding node.

  7. Proposed Work

  8. Proposed Work--search (110111,110010,2783,2783,4)

  9. Proposed Work--search • When a node is accessed using the hash key, there could be three cases : • Encounter an internal node : guarantees no rule in shorter lengths. • Encounter an empty entry (no node) : guarantees no node in longer lengths. • Meet a node with rules : Updating best matching rule and searching can leave the current trie.

  10. Optimization Technique 1

  11. Optimization Technique 2

  12. Simulation Results • The number of rules (N) • the number of BSL tries (Nt) • The worst-case number of memory accesses (Twst) • The average number of memory accesses(Tavg) • the required memory size in storing BSL tries (Mtrie) • The required memory size in storing a rule table (Mrule) • The average memory consumption required in storing a rule (M/rule)

  13. Simulation Results

  14. Simulation Results

  15. Simulation Results

  16. Simulation Results

  17. Conclusion • From the simulation result using class-bench databases, we found out that the number of levels of rule nesting in classification tables is 6 at the maximum, and hence the number of tries constructed by the proposed algorithm is limited by 6. • The proposed algorithm showed steady performance not much depending on table characteristics.

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