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

30 Scopus citations


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
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


Other2018 Symposium on SDN Research, SOSR 2018
CountryUnited States
CityLos Angeles

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Networks and Communications

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