Toward more localized local algorithms: removing assumptions concerning global knowledge - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Article Dans Une Revue Distributed Computing Année : 2013

Toward more localized local algorithms: removing assumptions concerning global knowledge

Résumé

Numerous sophisticated local algorithm were suggested in the literature for various fundamental problems. Notable examples are the MIS and (∆+1)-coloring algorithms by Barenboim and Elkin [6], by Kuhn [22], and by Panconesi and Srinivasan [34], as well as the O(∆ 2)-coloring algorithm by Linial [28]. Unfortunately, most known local algorithms (including, in particular, the aforementioned algorithms) are non-uniform, that is, local algorithms generally use good estimations of one or more global parameters of the network, e.g., the maximum degree ∆ or the number of nodes n. This paper provides a method for transforming a non-uniform local algorithm into a uniform one. Furthermore , the resulting algorithm enjoys the same asymp-totic running time as the original non-uniform algorithm. Our method applies to a wide family of both deterministic and randomized algorithms. Specifically, it applies to almost all state of the art non-uniform algorithms for MIS and Maximal Matching, as well as to many results concerning the coloring problem. (In particular, it applies to all aforementioned algorithms.) To obtain our transformations we introduce a new distributed tool called pruning algorithms, which we believe may be of independent interest.
Fichier principal
Vignette du fichier
color-template-dist.pdf (349.71 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01241086 , version 1 (09-12-2015)

Identifiants

Citer

Amos Korman, Jean-Sébastien Sereni, Laurent Viennot. Toward more localized local algorithms: removing assumptions concerning global knowledge. Distributed Computing, 2013, 26 (5-6), ⟨10.1007/s00446-012-0174-8⟩. ⟨hal-01241086⟩
448 Consultations
230 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More