Randomized admission policy for efficient top-k and frequency estimation

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

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

46 Scopus citations

Abstract

Network management protocols often require timely and meaningful insight about per flow network traffic. This paper introduces Randomized Admission Policy (RAP) - a novel algorithm for the frequency and top-k estimation problems, which are fundamental in network monitoring. We demonstrate space reductions compared to the alternatives by a factor of up to 32 on real packet traces and up to 128 on heavy-tailed workloads. For top-k identification, RAP exhibits memory savings by a factor of between 4 and 64 depending on the workloads' skewness. These empirical results are backed by formal analysis, indicating the asymptotic space improvement of our probabilistic admission approach. Additionally, we present d-Way RAP, a hardware friendly variant of RAP that empirically maintains its space and accuracy benefits.

Original languageEnglish
Title of host publicationINFOCOM 2017 - IEEE Conference on Computer Communications
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509053360
DOIs
StatePublished - 2 Oct 2017
Externally publishedYes
Event2017 IEEE Conference on Computer Communications, INFOCOM 2017 - Atlanta, United States
Duration: 1 May 20174 May 2017

Publication series

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

Conference

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

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