DocumentCode :
340992
Title :
Learning vector quantization in flow classification of IP switched networks
Author :
Ilvesmäki, Mika ; Luoma, Marko ; Kantola, Raimo
Author_Institution :
Lab. of Telecommun. Technol., Helsinki Univ. of Technol., Espoo, Finland
Volume :
5
fYear :
1998
fDate :
1998
Firstpage :
3017
Abstract :
We discuss the flow classification in IP switched networks. Previous work done with flow classification methods has concentrated on optimizing the IP switch performance. We examine the performance of several previously introduced flow classification methods and then we introduce the use of learning vector quantization (LVQ) in flow classification. The LVQ classifier has the ability to offer the user an intuitive traffic profile. The LVQ classifier is found to successfully classify traffic flows with feasible performance requirements while also providing the user with an unambiguous traffic profile
Keywords :
Internet; learning systems; packet switching; telecommunication network routing; telecommunication traffic; transport protocols; vector quantisation; ATM network; IP switch performance; IP switched networks; Internet; LVQ classifier; QoS; frame relay; learning vector quantization; multilayer routing; quality of service; traffic flow classification; traffic profile; Frame relay; IP networks; Intelligent networks; Internet; Quality of service; Routing; Switches; TCPIP; Telecommunication traffic; Vector quantization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Global Telecommunications Conference, 1998. GLOBECOM 1998. The Bridge to Global Integration. IEEE
Conference_Location :
Sydney,NSW
Print_ISBN :
0-7803-4984-9
Type :
conf
DOI :
10.1109/GLOCOM.1998.776626
Filename :
776626
Link To Document :
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