Randomized distributed decision

Pierre Fraigniaud; Mika Göös; Amos Korman; Merav Parter; David Peleg

doi:10.1007/s00446-014-0211-x

Randomized distributed decision

Pierre Fraigniaud, Mika Göös, Amos Korman, Merav Parter, David Peleg

Research output: Contribution to journal › Article › peer-review

Abstract

The paper tackles the power of randomization in the context of local distributed computing by analyzing the ability to “boost” the success probability of deciding a distributed language using a Monte-Carlo algorithm. We prove that, in many cases, the ability to increase the success probability for deciding distributed languages is rather limited. This contrasts with the sequential computing setting where boosting can systematically be achieved by repeating the randomized execution.

Original language	English
Pages (from-to)	419-434
Number of pages	16
Journal	Distributed Computing
Volume	27
Issue number	6
DOIs	https://doi.org/10.1007/s00446-014-0211-x
State	Published - 23 Nov 2014
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2014, Springer-Verlag Berlin Heidelberg.

Keywords

Complexity classes
Distributed local algorithms
Randomized algorithms

ASJC Scopus subject areas

Theoretical Computer Science
Hardware and Architecture
Computer Networks and Communications
Computational Theory and Mathematics

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10.1007/s00446-014-0211-x

Cite this

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Randomized distributed decision

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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