high-speed packet classification using binary search on length authors: hyesook lim and ju hyoung...
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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.
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Outline
1. Introduction
2. Area-based quad-trie
3. Binary Search on Prefix Length
4. Proposed Work
5. Optimization Technique
6. Simulation Results
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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.
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Area-based quad-trie
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Binary Search on Prefix Length
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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 whic
h indicates the highest priority rule among the rules mapped into the corresponding node.
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Proposed Work
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Proposed Work--search(110111,110010,2783,2783,4)
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Proposed Work--search
When a node is accessed using the hash key, there could be three cases :
1. Encounter an internal node : guarantees no rule in shorter lengths.
2. Encounter an empty entry (no node) : guarantees no node in longer lengths.
3. Meet a node with rules : Updating best matching rule
and searching can leave the current trie.
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Optimization Technique 1
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Optimization Technique 2
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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 r
ule (M/rule)
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Simulation Results
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Simulation Results
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Simulation Results
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Simulation Results
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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.