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SAX-PAC (scalable and expressive packet classification)

Kogan, Kirill ; Nikolenko, Sergey I. ; Rottenstreich, Ori ; Culhane, William ; Eugster, Patrick (2014)
SAX-PAC (scalable and expressive packet classification).
ACM SIGCOMM Computer Communication Review. Chicago, Illinois, USA
doi: 10.1145/2619239.2626294
Konferenzveröffentlichung, Bibliographie

Kurzbeschreibung (Abstract)

Efficient packet classification is a core concern for network services. Traditional multi-field classification approaches, in both software and ternary content-addressable memory (TCAMs), entail tradeoffs between (memory) space and (lookup) time. TCAMs cannot efficiently represent range rules, a common class of classification rules confining values of packet fields to given ranges. The exponential space growth of TCAM entries relative to the number of fields is exacerbated when multiple fields contain ranges. In this work, we present a novel approach which identifies properties of many classifiers which can be implemented in linear space and with worst-case guaranteed logarithmic time and allows the addition of more fields including range constraints without impacting space and time complexities. On real-life classifiers from Cisco Systems and additional classifiers from ClassBench [7] (with real parameters), 90-95% of rules are thus handled, and the other 5- 10% of rules can be stored in TCAM to be processed in parallel.

Typ des Eintrags: Konferenzveröffentlichung
Erschienen: 2014
Autor(en): Kogan, Kirill ; Nikolenko, Sergey I. ; Rottenstreich, Ori ; Culhane, William ; Eugster, Patrick
Art des Eintrags: Bibliographie
Titel: SAX-PAC (scalable and expressive packet classification)
Sprache: Englisch
Publikationsjahr: August 2014
Verlag: ACM
(Heft-)Nummer: 4
Buchtitel: Proceedings of the 2014 ACM conference on SIGCOMM
Reihe: SIGCOMM '14
Band einer Reihe: 44
Veranstaltungstitel: ACM SIGCOMM Computer Communication Review
Veranstaltungsort: Chicago, Illinois, USA
DOI: 10.1145/2619239.2626294
Kurzbeschreibung (Abstract):

Efficient packet classification is a core concern for network services. Traditional multi-field classification approaches, in both software and ternary content-addressable memory (TCAMs), entail tradeoffs between (memory) space and (lookup) time. TCAMs cannot efficiently represent range rules, a common class of classification rules confining values of packet fields to given ranges. The exponential space growth of TCAM entries relative to the number of fields is exacerbated when multiple fields contain ranges. In this work, we present a novel approach which identifies properties of many classifiers which can be implemented in linear space and with worst-case guaranteed logarithmic time and allows the addition of more fields including range constraints without impacting space and time complexities. On real-life classifiers from Cisco Systems and additional classifiers from ClassBench [7] (with real parameters), 90-95% of rules are thus handled, and the other 5- 10% of rules can be stored in TCAM to be processed in parallel.

Freie Schlagworte: TCAM, packet classification
ID-Nummer: TUD-CS-2014-1097
Fachbereich(e)/-gebiet(e): DFG-Sonderforschungsbereiche (inkl. Transregio)
DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche
Profilbereiche
Profilbereiche > Cybersicherheit (CYSEC)
Zentrale Einrichtungen
DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche > SFB 1053: MAKI – Multi-Mechanismen-Adaption für das künftige Internet
DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche > SFB 1053: MAKI – Multi-Mechanismen-Adaption für das künftige Internet > B: Adaptionsmechanismen
DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche > SFB 1053: MAKI – Multi-Mechanismen-Adaption für das künftige Internet > B: Adaptionsmechanismen > Teilprojekt B2: Koordination und Ausführung
Hinterlegungsdatum: 29 Jun 2016 11:33
Letzte Änderung: 23 Jul 2020 13:25
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