Pay for a Sliding Bloom Filter and Get Counting, Distinct Elements, and Entropy for Free

Eran Assaf, Ran Ben Basat, Gil Einziger, Roy Friedman

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

20 Scopus citations

Abstract

For many networking applications, recent data is more significant than older data, motivating the need for sliding window solutions. Various capabilities, such as DDoS detection and load balancing, require insights about multiple metrics including Bloom filters, per-flow counting, count distinct and entropy estimation. In this work, we present a unified construction that solves all the above problems in the sliding window model. Our single solution offers a better space to accuracy tradeoff than the state-of-the-art for each of these individual problems We show this both analytically and by running multiple real Internet backbone and datacenter packet traces.

Original languageEnglish
Title of host publicationINFOCOM 2018 - IEEE Conference on Computer Communications
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2204-2212
Number of pages9
ISBN (Electronic)9781538641286
DOIs
StatePublished - 8 Oct 2018
Externally publishedYes
Event2018 IEEE Conference on Computer Communications, INFOCOM 2018 - Honolulu, United States
Duration: 15 Apr 201819 Apr 2018

Publication series

NameProceedings - IEEE INFOCOM
Volume2018-April
ISSN (Print)0743-166X

Conference

Conference2018 IEEE Conference on Computer Communications, INFOCOM 2018
Country/TerritoryUnited States
CityHonolulu
Period15/04/1819/04/18

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