Counting with tinytable: Every bit counts!

Gil Einziger, Roy Friedman

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

7 Scopus citations

Abstract

Counting Bloom filters (CBF) and their variants are data structures that support membership or multiplicity queries with a low probabilistic error. Yet, they incur a significant memory space overhead when compared to lower bounds as well as to (plain) Bloom filters, which can only represent set membership without removals. This work presents TinyTable, an efficient hash table based algorithm that supports membership queries, removals and multiplicity queries (statistics). TinyTable improves space efficiency by as much as 28% compared to CBF variants and as much as 60% for monitoring flow statistics. When the required false positive rate is smaller than 1%, TinyTable is even slightly more space efficient than (plain) Bloom filters. Our performance study shows that TinyTable has acceptable runtime overheads.

Original languageEnglish
Title of host publicationProceedings of the 17th International Conference on Distributed Computing and Networking, ICDCN 2016
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450340328
DOIs
StatePublished - 4 Jan 2016
Externally publishedYes
Event17th International Conference on Distributed Computing and Networking, ICDCN 2016 - Singapore, Singapore
Duration: 4 Jan 20167 Jan 2016

Publication series

NameACM International Conference Proceeding Series
Volume04-07-January-2016

Conference

Conference17th International Conference on Distributed Computing and Networking, ICDCN 2016
Country/TerritorySingapore
CitySingapore
Period4/01/167/01/16

Keywords

  • Approximate counting
  • Counting Bloom filter
  • Hash tables

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