Distributed Strong Diameter Network Decomposition

preprint2016arXivOpen access

Abstract

For a pair of positive parameters D,χD,χ, a partition P{\cal P} of the vertex set VV of an nn-vertex graph G=(V,E)G = (V,E) into disjoint clusters of diameter at most DD each is called a (D,χ)(D,χ) network decomposition, if the supergraph G(P){\cal G}({\cal P}), obtained by contracting each of the clusters of P{\cal P}, can be properly χχ-colored. The decomposition P{\cal P} is said to be strong (resp., weak) if each of the clusters has strong (resp., weak) diameter at most DD, i.e., if for every cluster C∈PC \in {\cal P} and every two vertices u,v∈Cu,v \in C, the distance between them in the induced graph G(C)G(C) of CC (resp., in GG) is at most DD. Network decomposition is a powerful construct, very useful in distributed computing and beyond. It was shown by Awerbuch \etal \cite{AGLP89} and Panconesi and Srinivasan \cite{PS92}, that strong (2O(log⁡n),2O(log⁡n))(2^{O(\sqrt{\log n})},2^{O(\sqrt{\log n})}) network decompositions can be computed in 2O(log⁡n)2^{O(\sqrt{\log n})} distributed time. Linial and Saks \cite{LS93} devised an ingenious randomized algorithm that constructs {\em weak} (O(log⁡n),O(log⁡n))(O(\log n),O(\log n)) network decompositions in O(log⁡2n)O(\log^2 n) time. It was however open till now if {\em strong} network decompositions with both parameters 2o(log⁡n)2^{o(\sqrt{\log n})} can be constructed in distributed 2o(log⁡n)2^{o(\sqrt{\log n})} time. In this paper we answer this long-standing open question in the affirmative, and show that strong (O(log⁡n),O(log⁡n))(O(\log n),O(\log n)) network decompositions can be computed in O(log⁡2n)O(\log^2 n) time. We also present a tradeoff between parameters of our network decomposition. Our work is inspired by and relies on the "shifted shortest path approach", due to Blelloch \etal \cite{BGKMPT11}, and Miller \etal \cite{MPX13}. These authors developed this approach for PRAM algorithms for padded partitions. We adapt their approach to network decompositions in the distributed model of computation.

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