Toward more localized local algorithms: removing assumptions concerning global knowledge

TL;DR

This paper introduces a method to convert non-uniform local algorithms into uniform ones without increasing their asymptotic running time, broadening their applicability in distributed network problems.

Contribution

It presents a general transformation technique for non-uniform algorithms into uniform algorithms, utilizing a new distributed tool called pruning algorithms.

Findings

Transforms a wide range of non-uniform algorithms into uniform ones.

Maintains the original asymptotic running time after transformation.

Applicable to algorithms for MIS, Maximal Matching, and coloring problems.

Abstract

Numerous sophisticated local algorithm were suggested in the literature for various fundamental problems. Notable examples are the MIS and $(\Delta+1)$-coloring algorithms by Barenboim and Elkin [6], by Kuhn [22], and by Panconesi and Srinivasan [34], as well as the $O(\Delta 2)$-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 $\Delta$ 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.