Distributed Strong Diameter Network Decomposition

Abstract

For a pair of positive parameters D,, a partition P of the vertex set V of an n-vertex graph G = (V,E) into disjoint clusters of diameter at most D each is called a (D,) network decomposition, if the supergraph G( P), obtained by contracting each of the clusters of P, can be properly -colored. The decomposition P is said to be strong (resp., weak) if each of the clusters has strong (resp., weak) diameter at most D, i.e., if for every cluster C ∈ P and every two vertices u,v ∈ C, the distance between them in the induced graph G(C) of C (resp., in G) is at most D. Network decomposition is a powerful construct, very useful in distributed computing and beyond. It was shown by Awerbuch ηl AGLP89 and Panconesi and Srinivasan PS92, that strong (2O( n),2O( n)) network decompositions can be computed in 2O( n) distributed time. Linial and Saks LS93 devised an ingenious randomized algorithm that constructs weak (O( n),O( n)) network decompositions in O(2 n) time. It was however open till now if strong network decompositions with both parameters 2o( n) can be constructed in distributed 2o( n) time. In this paper we answer this long-standing open question in the affirmative, and show that strong (O( n),O( n)) network decompositions can be computed in O(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 ηl BGKMPT11, and Miller ηl 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.

0

Turn this paper into a lesson

ArcXiv compiles a structured reading guide from this paper's metadata: plain-English importance, contributions, prerequisite concepts, which sections to read first, flashcards, and a quiz. Grounded in the abstract, never invented.

Discussion (0)

Sign in to join the discussion.

Loading comments…