Show simple item record

dc.contributor.authorCarela Español, Valentín
dc.contributor.authorBarlet Ros, Pere
dc.contributor.authorMulla Valls, Oriol
dc.contributor.authorSolé Pareta, Josep
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors
dc.date.accessioned2016-04-06T08:40:40Z
dc.date.available2016-08-01T00:31:00Z
dc.date.issued2015-07-01
dc.identifier.citationCarela, V., Barlet, P., Mulla , O., Solé-Pareta, J. An autonomic traffic classification system for network operation and management. "Journal of network and systems management", 01 Juliol 2015, vol. 23, núm. 3, p. 401-419.
dc.identifier.issn1064-7570
dc.identifier.urihttp://hdl.handle.net/2117/85268
dc.description.abstractTraffic classification is an important aspect in network operation and management, but challenging from a research perspective. During the last decade, several works have proposed different methods for traffic classification. Although most proposed methods achieve high accuracy, they present several practical limitations that hinder their actual deployment in production networks. For example, existing methods often require a costly training phase or expensive hardware, while their results have relatively low completeness. In this paper, we address these practical limitations by proposing an autonomic traffic classification system for large networks. Our system combines multiple classification techniques to leverage their advantages and minimize the limitations they present when used alone. Our system can operate with Sampled NetFlow data making it easier to deploy in production networks to assist network operation and management tasks. The main novelty of our system is that it can automatically retrain itself in order to sustain a high classification accuracy along time. We evaluate our solution using a 14-day trace from a large production network and show that our system can sustain an accuracy <96 %, even in presence of sampling, during long periods of time. The proposed system has been deployed in production in the Catalan Research and Education network and it is currently being used by network managers of more than 90 institutions connected to this network.
dc.format.extent19 p.
dc.language.isoeng
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors
dc.subject.lcshComputer networks -- Management
dc.subject.lcshTelecommunication -- Traffic -- Management
dc.subject.otherNetwork monitoring
dc.subject.otherMachine learning
dc.subject.otherDeep packet inspection
dc.subject.otherApplication identification
dc.subject.otherSelf-adaptative system
dc.titleAn autonomic traffic classification system for network operation and management
dc.typeArticle
dc.subject.lemacOrdinadors, Xarxes d' -- Gestió
dc.subject.lemacTelecomunicació -- Tràfic -- Gestió
dc.contributor.groupUniversitat Politècnica de Catalunya. CBA - Sistemes de Comunicacions i Arquitectures de Banda Ampla
dc.identifier.doi10.1007/s10922-013-9293-1
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://link.springer.com/article/10.1007%2Fs10922-013-9293-1
dc.rights.accessOpen Access
drac.iddocument17530555
dc.description.versionPostprint (author's final draft)
upcommons.citation.authorCarela, V.; Barlet, P.; Mulla, O.; Solé-Pareta, J.
upcommons.citation.publishedtrue
upcommons.citation.publicationNameJournal of network and systems management
upcommons.citation.volume23
upcommons.citation.number3
upcommons.citation.startingPage401
upcommons.citation.endingPage419


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder