Heavy-hitter detection entirely in the data plane

Vibhaalakshmi Sivaraman, Srinivas Narayana, Ori Rottenstreich, S. Muthukrishnan, Jennifer L. Rexford

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

88 Scopus citations

Abstract

Identifying the "heavy hitter" flows or flows with large traffic volumes in the data plane is important for several applications e.g., flow-size aware routing, DoS detection, and traffic engineering. However, measurement in the data plane is constrained by the need for linerate processing (at 10-100Gb/s) and limited memory in switching hardware. We propose HashPipe, a heavy hitter detection algorithm using emerging programmable data planes. HashPipe implements a pipeline of hash tables which retain counters for heavy flows while evicting lighter flows over time. We prototype HashPipe in P4 and evaluate it with packet traces from an ISP backbone link and a data center. On the ISP trace (which contains over 400,000 flows), we find that HashPipe identifies 95% of the 300 heaviest flows with less than 80KB of memory.

Original languageEnglish (US)
Title of host publicationSOSR 2017 - Proceedings of the 2017 Symposium on SDN Research
PublisherAssociation for Computing Machinery, Inc
Pages164-176
Number of pages13
ISBN (Electronic)9781450349475
DOIs
StatePublished - Apr 3 2017
Event2017 Symposium on SDN Research, SOSR 2017 - Santa Clara, United States
Duration: Apr 3 2017Apr 4 2017

Publication series

NameSOSR 2017 - Proceedings of the 2017 Symposium on SDN Research

Other

Other2017 Symposium on SDN Research, SOSR 2017
CountryUnited States
CitySanta Clara
Period4/3/174/4/17

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Software

Keywords

  • Network algorithms
  • Network monitoring
  • Programmable networks
  • Software-defined networks

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  • Cite this

    Sivaraman, V., Narayana, S., Rottenstreich, O., Muthukrishnan, S., & Rexford, J. L. (2017). Heavy-hitter detection entirely in the data plane. In SOSR 2017 - Proceedings of the 2017 Symposium on SDN Research (pp. 164-176). (SOSR 2017 - Proceedings of the 2017 Symposium on SDN Research). Association for Computing Machinery, Inc. https://doi.org/10.1145/3050220.3063772