Heavy hitters in streams and sliding windows

Ran Ben-Basat, Gil Einziger, Roy Friedman, Yaron Kassner

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

94 Scopus citations

Abstract

Identifying heavy hitter flows is a fundamental problem in various network domains. The well established method of using sketches to approximate flow statistics suffers from space inefficiencies. In addition, flow arrival rates are dynamic, thus keeping track of the most recent heavy hitters poses a challenge. Sliding window approximations address this problem, reducing space at the cost of increasing point query time. This paper presents two novel algorithms for identifying heavy hitters in streams and sliding windows. Both algorithms use statically allocated memory and support constant time point queries. For sliding windows, this is an asymptotic improvement over previous work. We also demonstrate reduced memory requirements of up to 85% in streams and 66% in sliding windows over synthetic and real Internet packet traces.

Original languageEnglish
Title of host publicationIEEE INFOCOM 2016 - 35th Annual IEEE International Conference on Computer Communications
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467399531
DOIs
StatePublished - 27 Jul 2016
Externally publishedYes
Event35th Annual IEEE International Conference on Computer Communications, IEEE INFOCOM 2016 - San Francisco, United States
Duration: 10 Apr 201614 Apr 2016

Publication series

NameProceedings - IEEE INFOCOM
Volume2016-July
ISSN (Print)0743-166X

Conference

Conference35th Annual IEEE International Conference on Computer Communications, IEEE INFOCOM 2016
Country/TerritoryUnited States
CitySan Francisco
Period10/04/1614/04/16

ASJC Scopus subject areas

  • Computer Science (all)
  • Electrical and Electronic Engineering

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