Network-wide heavy hitter detection with commodity switches

Rob Harrison, Qizhe Cai, Arpit Gupta, Jennifer L. Rexford

Research output: Chapter in Book/Report/Conference proceedingConference contribution

23 Scopus citations

Abstract

Many network monitoring tasks identify subsets of traffic that stand out, e.g., top-k flows for a particular statistic. A Protocol Independent Switch Architecture (PISA) switch can identify these "heavy hitter" flows directly in the data plane, by aggregating traffic statistics across packets and comparing against a threshold. However, network operators often want to identify interesting traffic on a network-wide basis. To bridge the gap between line-rate monitoring and networkwide visibility, we present a distributed heavy-hitter detection scheme for networks modeled as one-big switch.We use adaptive thresholds to perform efficient threshold monitoring directly in the data plane. We implement our system using the P4 language, and evaluate it using real-world packet traces. We demonstrate that our solution can accurately detect network-wide heavy hitters with up to 70% savings in communication overhead compared to an existing approach with a provable upper bound.

Original languageEnglish (US)
Title of host publicationProceedings of the Symposium on SDN Research, SOSR 2018
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9781450356640
DOIs
StatePublished - Mar 28 2018
Event2018 Symposium on SDN Research, SOSR 2018 - Los Angeles, United States
Duration: Mar 28 2018Mar 29 2018

Publication series

NameProceedings of the Symposium on SDN Research, SOSR 2018

Other

Other2018 Symposium on SDN Research, SOSR 2018
CountryUnited States
CityLos Angeles
Period3/28/183/29/18

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Networks and Communications

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