DocumentCode :
3311835
Title :
A Timesaving Recursive Flow Packet Classification Algorithm
Author :
Pan, Yuke ; Chen, Bing ; Xu, Tao
Author_Institution :
Coll. of Inf. Sci. & Technol., Nanjing Univ. of Aeronaut. & Astronaut., Nanjing
Volume :
2
fYear :
2009
fDate :
25-26 April 2009
Firstpage :
442
Lastpage :
445
Abstract :
Packet classification, which classifies the incoming packets according to their head information, is an important technique for the next-generation routers, firewalls and so on. Recursive flow classification algorithm is one of the fastest software packet classification algorithms, but its initialization time is too long, so a timesaving recursive flow classification algorithm is proposed. In phase 0, equivalence classes are defined by the set of filters matched in processing the packet. A more effective method for finding every entrypsilas equivalence class is given. In phase 1, in order to combine the results for different chunks together, cross-producting tables are used to store precomputed results and a faster method of merging two chunks is given. Its initialization time is greatly reduced because of heuristic learning while its classification phase retaining the same time complexity with RFC. The experimental results show that the total time has an average decrease of 40%.
Keywords :
learning (artificial intelligence); software engineering; table lookup; cross-producting tables; heuristic learning; next-generation routers; software packet classification algorithms; timesaving recursive flow packet classification algorithm; Classification algorithms; Computer networks; Educational institutions; Information filtering; Information filters; Information security; Matched filters; Next generation networking; Software algorithms; Wireless communication; Algorithm; RFC; packet classification;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Networks Security, Wireless Communications and Trusted Computing, 2009. NSWCTC '09. International Conference on
Conference_Location :
Wuhan, Hubei
Print_ISBN :
978-1-4244-4223-2
Type :
conf
DOI :
10.1109/NSWCTC.2009.245
Filename :
4908500
Link To Document :
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