HighSpeed 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 National Cheng Kung University, Taiwan R. O. C.

Outline 1. 2. 3. 4. 5. 6. Introduction Area-based quad-trie Binary Search on Prefix Length Proposed Work Optimization Technique Simulation Results

Introduction n n 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.

Area-based quad-trie

Binary Search on Prefix Length

Proposed Work n n n 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.

Proposed Work

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

Proposed Work--search n 1. 2. 3. 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.

Optimization Technique 1

Optimization Technique 2

Simulation Results n n n n 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)

Simulation Results

Conclusion n n 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.