TY - GEN

T1 - Toward more localized local algorithms

T2 - 30th Annual ACM SIGACT-SIGOPS Symposium on Principles of Distributed Computing, PODC'11, Held as Part of the 5th Federated Computing Research Conference, FCRC

AU - Korman, Amos

AU - Sereni, Jean Sébastien

AU - Viennot, Laurent

PY - 2011

Y1 - 2011

N2 - Numerous sophisticated local algorithm were suggested in the literature for various fundamental problems. Notable examples are the MIS and (Δ+1)-coloring algorithms by Barenboim and Elkin [6], by Kuhn [22], and by Panconesi and Srinivasan [33], as well as the OΔ2-coloring algorithm by Linial [27]. Unfortunately, most known local algorithms (including, in particular, the aforementioned algorithms) are non-uniform, that is, they assume that all nodes know good estimations of one or more global parameters of the network, e.g., the maximum degree Δ or the number of nodes n. This paper provides a rather general method for transforming a non-uniform local algorithm into a uniform one. Furthermore, the resulting algorithm enjoys the same asymptotic 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 of the state of the art non-uniform algorithms regarding 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.

AB - Numerous sophisticated local algorithm were suggested in the literature for various fundamental problems. Notable examples are the MIS and (Δ+1)-coloring algorithms by Barenboim and Elkin [6], by Kuhn [22], and by Panconesi and Srinivasan [33], as well as the OΔ2-coloring algorithm by Linial [27]. Unfortunately, most known local algorithms (including, in particular, the aforementioned algorithms) are non-uniform, that is, they assume that all nodes know good estimations of one or more global parameters of the network, e.g., the maximum degree Δ or the number of nodes n. This paper provides a rather general method for transforming a non-uniform local algorithm into a uniform one. Furthermore, the resulting algorithm enjoys the same asymptotic 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 of the state of the art non-uniform algorithms regarding 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.

KW - coloring

KW - distributed algorithm

KW - global knowledge

KW - maximal matching

KW - mis

KW - parameters

UR - http://www.scopus.com/inward/record.url?scp=79959907289&partnerID=8YFLogxK

U2 - 10.1145/1993806.1993814

DO - 10.1145/1993806.1993814

M3 - Conference contribution

AN - SCOPUS:79959907289

SN - 9781450307192

T3 - Proceedings of the Annual ACM Symposium on Principles of Distributed Computing

SP - 49

EP - 58

BT - PODC'11 - Proceedings of the 2011 ACM Symposium Principles of Distributed Computing

Y2 - 6 June 2011 through 8 June 2011

ER -