Exploiting Order Independence for Scalable and Expressive Packet Classification

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 com...

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Detalles Bibliográficos
Autores: Kogan, Kirill, Nikolenko, Sergey, Rottenstreich, Ori, Culhane, William, Eugster, Patrick
Tipo de recurso: artículo
Fecha de publicación:2016
País:España
Institución:IMDEA Networks Institute
Repositorio:IMDEA Networks Institute Digital Repository
Idioma:inglés
OAI Identifier:oai:dspace.networks.imdea.org:20.500.12761/1493
Acceso en línea:http://hdl.handle.net/20.500.12761/1493
https://dx.doi.org/10.1109/TNET.2015.2407831
Access Level:acceso abierto
Palabra clave:Packet classification
ternary content-addressable memory (TCAM)
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repository_id_str
spelling Exploiting Order Independence for Scalable and Expressive Packet ClassificationKogan, KirillNikolenko, SergeyRottenstreich, OriCulhane, WilliamEugster, PatrickPacket classificationternary content-addressable memory (TCAM)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 \emph{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~\cite{CLASSBENCH} (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.pubCo-sponsored by the IEEE Communications Society, the IEEE Computer Society, and the ACM with its Special Interest Group on Data Communications (SIGCOMM)20162016-04-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articlehttp://hdl.handle.net/20.500.12761/1493https://dx.doi.org/10.1109/TNET.2015.2407831reponame:IMDEA Networks Institute Digital Repositoryinstname:IMDEA Networks InstituteInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:dspace.networks.imdea.org:20.500.12761/14932026-06-06T12:35:51Z
dc.title.none.fl_str_mv Exploiting Order Independence for Scalable and Expressive Packet Classification
title Exploiting Order Independence for Scalable and Expressive Packet Classification
spellingShingle Exploiting Order Independence for Scalable and Expressive Packet Classification
Kogan, Kirill
Packet classification
ternary content-addressable memory (TCAM)
title_short Exploiting Order Independence for Scalable and Expressive Packet Classification
title_full Exploiting Order Independence for Scalable and Expressive Packet Classification
title_fullStr Exploiting Order Independence for Scalable and Expressive Packet Classification
title_full_unstemmed Exploiting Order Independence for Scalable and Expressive Packet Classification
title_sort Exploiting Order Independence for Scalable and Expressive Packet Classification
dc.creator.none.fl_str_mv Kogan, Kirill
Nikolenko, Sergey
Rottenstreich, Ori
Culhane, William
Eugster, Patrick
author Kogan, Kirill
author_facet Kogan, Kirill
Nikolenko, Sergey
Rottenstreich, Ori
Culhane, William
Eugster, Patrick
author_role author
author2 Nikolenko, Sergey
Rottenstreich, Ori
Culhane, William
Eugster, Patrick
author2_role author
author
author
author
dc.subject.none.fl_str_mv Packet classification
ternary content-addressable memory (TCAM)
topic Packet classification
ternary content-addressable memory (TCAM)
description 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 \emph{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~\cite{CLASSBENCH} (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.
publishDate 2016
dc.date.none.fl_str_mv 2016
2016-04-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12761/1493
https://dx.doi.org/10.1109/TNET.2015.2407831
url http://hdl.handle.net/20.500.12761/1493
https://dx.doi.org/10.1109/TNET.2015.2407831
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Co-sponsored by the IEEE Communications Society, the IEEE Computer Society, and the ACM with its Special Interest Group on Data Communications (SIGCOMM)
publisher.none.fl_str_mv Co-sponsored by the IEEE Communications Society, the IEEE Computer Society, and the ACM with its Special Interest Group on Data Communications (SIGCOMM)
dc.source.none.fl_str_mv reponame:IMDEA Networks Institute Digital Repository
instname:IMDEA Networks Institute
instname_str IMDEA Networks Institute
reponame_str IMDEA Networks Institute Digital Repository
collection IMDEA Networks Institute Digital Repository
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.301603

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