Poster abstract: A sliding counting bloom filter

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

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

Abstract

Bloom filters and their variants support membership or multiplicity queries with a low probabilistic error. For many networking applications, recent data is more significant than older data, motivating the need for sliding window solutions. In this work, we introduce Sliding Window Approximate Membership Protocol (SWAMP), a simple algorithm for membership and multiplicity queries over sliding windows. SWAMP is the first approximate set membership sliding window algorithm that is memory succinct, i.e., up to a factor of (1 + 0(1)) from the information theoretic lower bound, for constant error probabilities. It also operates in constant time and supports multiplicity queries with no additional overheads. Finally, we evaluate the memory consumption of SWAMP on a wide range of parameters and show a 25-40% reduction compared to the state of the art sliding Bloom filters (that cannot count). In summary, SWAMP improves the memory consumption of its competitors and can also count.

Original languageAmerican English
Title of host publication2017 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2017
Pages1012-1013
Number of pages2
ISBN (Electronic)9781538627846
DOIs
StatePublished - 20 Nov 2017
Event2017 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2017 - Atlanta, United States
Duration: 1 May 20174 May 2017

Publication series

Name2017 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2017

Conference

Conference2017 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2017
Country/TerritoryUnited States
CityAtlanta
Period1/05/174/05/17

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Control and Optimization
  • Artificial Intelligence
  • Computer Networks and Communications

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