Not all fair probabilistic schedulers are equivalent

Ioannis Chatzigiannakis, Shlomi Dolev, Sándor P. Fekete, Othon Michail, Paul G. Spirakis

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

18 Scopus citations


We propose a novel, generic definition of probabilistic schedulers for population protocols. We then identify the consistent probabilistic schedulers, and prove that any consistent scheduler that assigns a non-zero probability to any transition i → j, where i and j are configurations satisfying i ≠ j, is fair with probability 1. This is a new theoretical framework that aims to simplify proving specific probabilistic schedulers fair. In this paper we propose two new schedulers, the State Scheduler and the Transition Function Scheduler. Both possess the significant capability of being protocol-aware, i.e. they can assign transition probabilities based on information concerning the underlying protocol. By using our framework we prove that the proposed schedulers, and also the Random Scheduler that was defined by Angluin et al. [2], are all fair with probability 1. Finally, we define and study equivalence between schedulers w.r.t. performance and correctness and prove that there exist fair probabilistic schedulers that are not equivalent w.r.t. to performance and others that are not equivalent w.r.t. correctness.

Original languageEnglish
Title of host publicationPrinciples of Distributed Systems - 13th International Conference, OPODIS 2009, Proceedings
Number of pages15
StatePublished - 31 Dec 2009
Event13th International Conference on Principles of Distributed Systems, OPODIS 2009 - Nimes, France
Duration: 15 Dec 200918 Dec 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5923 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference13th International Conference on Principles of Distributed Systems, OPODIS 2009


  • Communicating automata
  • Fair scheduler
  • Fairness
  • Population protocol
  • Probabilistic scheduler
  • Sensor network

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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