Repository logo

Infoscience

  • English
  • French
Log In
Logo EPFL, École polytechnique fédérale de Lausanne

Infoscience

  • English
  • French
Log In
  1. Home
  2. Academic and Research Output
  3. Journal articles
  4. The Generalized Loneliness Detector and Weak System Models for k-Set Agreement
 
research article

The Generalized Loneliness Detector and Weak System Models for k-Set Agreement

Biely, Martin  
•
Robinson, Peter
•
Schmid, Ulrich
2014
Ieee Transactions On Parallel And Distributed Systems

This paper presents two weak partially synchronous system models Manti(n-k) and Msink(n-k), which are just strong enough for solving k-set agreement: We introduce the generalized (n-k)-loneliness failure detector L(k), which we first prove to be sufficient for solving k-set agreement, and show that L(k) but not L(k-1) can be implemented in both models. Manti(n-k) and Msink(n-k) are hence the first message passing models that lie between models where V (and therefore consensus) can be implemented and the purely asynchronous model. We also address k-set agreement in anonymous systems, that is, in systems where (unique) process identifiers are not available. Since our novel k-set agreement algorithm using L(k) also works in anonymous systems, it turns out that the loneliness failure detector L = L(n-1) introduced by Delporte et al. is also the weakest failure detector for set agreement in anonymous systems. Finally, we analyze the relationship between L(k) and other failure detectors suitable for solving k-set agreement.

  • Details
  • Metrics
Type
research article
DOI
10.1109/Tpds.2013.77
Web of Science ID

WOS:000334672900024

Author(s)
Biely, Martin  
Robinson, Peter
Schmid, Ulrich
Date Issued

2014

Publisher

Ieee Computer Soc

Published in
Ieee Transactions On Parallel And Distributed Systems
Volume

25

Issue

4

Start page

1078

End page

1088

Subjects

Distributed systems

•

models of computation

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LSR-IC  
Available on Infoscience
May 26, 2014
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/103661
Logo EPFL, École polytechnique fédérale de Lausanne
  • Contact
  • infoscience@epfl.ch

  • Follow us on Facebook
  • Follow us on Instagram
  • Follow us on LinkedIn
  • Follow us on X
  • Follow us on Youtube
AccessibilityLegal noticePrivacy policyCookie settingsEnd User AgreementGet helpFeedback

Infoscience is a service managed and provided by the Library and IT Services of EPFL. © EPFL, tous droits réservés