On sampling in higher-dimensional peer-to-peer systems

Goran Konjevod, Andrea Richa, Donglin Xia

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

We present fully distributed algorithms for random sampling of nodes in peer-to-peer systems, extending and generalizing the work of King and Saia [Proceedings of PODC 2004] from simple Chord-like distributed hash tables to systems based on higher-dimensional hierarchical constructions, like Content Addressable Networks (CAN). We also show preliminary results on the generalization of the problem to biased sampling. In addition, we provide an extension of CAN that requires only O(1) space per node and achieves O(log n) lookup latency and message complexities.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages641-652
Number of pages12
Volume3887 LNCS
DOIs
Publication statusPublished - 2006
EventLATIN 2006: Theoretical Informatics - 7th Latin American Symposium - Valdivia, Chile
Duration: Mar 20 2006Mar 24 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3887 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

OtherLATIN 2006: Theoretical Informatics - 7th Latin American Symposium
CountryChile
CityValdivia
Period3/20/063/24/06

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ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

Konjevod, G., Richa, A., & Xia, D. (2006). On sampling in higher-dimensional peer-to-peer systems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3887 LNCS, pp. 641-652). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3887 LNCS). https://doi.org/10.1007/11682462_59