3 research outputs found

    On Fast and Robust Information Spreading in the Vertex-Congest Model

    Full text link
    This paper initiates the study of the impact of failures on the fundamental problem of \emph{information spreading} in the Vertex-Congest model, in which in every round, each of the nn nodes sends the same O(logn)O(\log{n})-bit message to all of its neighbors. Our contribution to coping with failures is twofold. First, we prove that the randomized algorithm which chooses uniformly at random the next message to forward is slow, requiring Ω(n/k)\Omega(n/\sqrt{k}) rounds on some graphs, which we denote by Gn,kG_{n,k}, where kk is the vertex-connectivity. Second, we design a randomized algorithm that makes dynamic message choices, with probabilities that change over the execution. We prove that for Gn,kG_{n,k} it requires only a near-optimal number of O(nlog3n/k)O(n\log^3{n}/k) rounds, despite a rate of q=O(k/nlog3n)q=O(k/n\log^3{n}) failures per round. Our technique of choosing probabilities that change according to the execution is of independent interest.Comment: Appears in SIROCCO 2015 conferenc
    corecore