Article Dans Une Revue Stochastic Analysis and Applications Année : 2021

Probabilistic non-asymptotic analysis of distributed algorithms

Résumé

We present a new probabilistic analysis of distributed algorithms. Our approach relies on the theory of quasi-stationary distributions (QSD) recently developped by the first and third authors [4, 5, 6]. We give properties on the deadlock time and the distribution of the model before deadlock, both for discrete and diffusion models. Our results are non-asymptotic since they apply to any finite values of the involved parameters (time, numbers of resources, number of processors, etc.) and reflect the real behavior of these algorithms, with potential applications to deadlock prevention, which are very important for real world applications in computer science.

Fichier principal
Vignette du fichier
2020_12_CSV.pdf (356.88 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
Licence

Dates et versions

hal-01710663 , version 1 (16-02-2018)
hal-01710663 , version 2 (04-02-2021)

Licence

Identifiants

Citer

Nicolas Champagnat, René Schott, Denis Villemonais. Probabilistic non-asymptotic analysis of distributed algorithms. Stochastic Analysis and Applications, 2021, 36 (6), pp.981-998. ⟨10.1080/07362994.2020.1861952⟩. ⟨hal-01710663v2⟩
633 Consultations
613 Téléchargements

Altmetric

Partager

  • More