Research note: Flow classification schemes in traffic-based multilayer IP switching-comparison between conventional and neural approach

Authors:
Mika IlvesmäKi;Marko Luoma;Raimo Kantola
Affiliations:
Helsinki University of Technology, Laboratory of Telecommunications Technology, PO Box 3000, FIN-02015 Hut, Finland;Helsinki University of Technology, Laboratory of Telecommunications Technology, PO Box 3000, FIN-02015 Hut, Finland;Helsinki University of Technology, Laboratory of Telecommunications Technology, PO Box 3000, FIN-02015 Hut, Finland
Venue:
Computer Communications
Year:
1998

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Abstract

In this work we compare different methods for router workload deduction and flow classification schemes. Gigabit-routing solutions and different schemes of integrated switching and routing (Internet protocol (IP) switching) have emerged. Several flow classification schemes to be used in traffic-controled IP switching are presented and evaluated. As a new alternative, the `Learning Vector Quantization' classifier is examined and found to be able to successfully classify Internet traffic flows.