Toward more localized local algorithms: removing assumptions concerning global knowledge - Archive ouverte HAL Access content directly
Journal Articles Distributed Computing Year : 2013

Toward more localized local algorithms: removing assumptions concerning global knowledge

(1, 2) , (3) , (1, 2, 4)
1
2
3
4

Abstract

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
Origin : Files produced by the author(s)
Loading...

Dates and versions

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

Identifiers

Cite

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⟩
435 View
221 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More