DocumentCode :
165884
Title :
EQC16: An optimized packet classification algorithm for large rule-sets
Author :
Trivedi, Uday ; Jangir, Mohan Lal
Author_Institution :
Samsung R&D Inst. India, Bangalore, India
fYear :
2014
fDate :
24-27 Sept. 2014
Firstpage :
112
Lastpage :
119
Abstract :
Packet classification is a well-researched field. However, none of the existing algorithms works well for very large rule-sets up to 128K rules. Further with the advent of IPv6, number of rule field bytes is going to increase from around 16 to 48. With higher number of field bytes, both memory usage and classification speed is affected badly. EQC16 attempts to solve this particular problem. It borrows the design from ABV (Aggregated Bit-Vector) algorithm and adds some effective optimizations. EQC16 uses 16 bit lookup to reduce memory accesses, min-max rule information to narrow down search scope, and combines two 8 bit fields for fast search. It has very high classification speed, reasonable memory requirement and small preprocessing time for large rule-sets and it supports real-time incremental updates. EQC16 algorithm was evaluated and compared with existing decomposition based algorithms BV (Bit-Vector), ABV and RFC (Recursive Flow Classification). The results indicate that EQC16 outperforms both BV and ABV in terms of classification speed and RFC in terms of preprocessing time and incremental update feature.
Keywords :
minimax techniques; packet switching; vectors; ABV; EQC16; RFC; aggregated bit-vector; min-max rule information; packet classification; recursive flow classification; rule-sets; Algorithm design and analysis; Classification algorithms; Indexes; Memory management; Optimization; Support vector machine classification; Vectors; 16 bit lookup; Incremental update; Large rule-sets; packet classification on multi-dimensional fields;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advances in Computing, Communications and Informatics (ICACCI, 2014 International Conference on
Conference_Location :
New Delhi
Print_ISBN :
978-1-4799-3078-4
Type :
conf
DOI :
10.1109/ICACCI.2014.6968202
Filename :
6968202
Link To Document :
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