On the Fairness of Probabilistic Schedulers for Population Protocols

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

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

Abstract

We propose a novel, generic definition of probabilistic schedulers for population protocols. We design 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. We prove that the proposed schedulers, and also the Random Scheduler that was defined by Angluin et al. [1], are all fair with probability 1. We also 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. We implement our schedulers using a new tool for simulating population protocols and evaluate their performance from the viewpoint of experimental analysis and verification. We study three representative protocols to verify stability, and compare the experimental time to convergence with the known complexity bounds. We run our experiments from very small to extremely large populations (of up to 108 agents). W
Original languageEnglish
Title of host publicationAlgorithmic Methods for Distributed Cooperative Systems, 06.09. - 11.09.2009
EditorsSándor P. Fekete, Stefan Fischer, Martin A. Riedmiller, Subhash Suri
PublisherSchloss Dagstuhl - Leibniz-Zentrum für Informatik, Germany
Volume09371
StatePublished - 2009

Publication series

NameDagstuhl Seminar Proceedings
PublisherSchloss Dagstuhl - Leibniz-Zentrum für Informatik, Germany

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