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Antonio Fernández Anta

  1. Failure Detectors in Homonymous Distributed Systems (with an Application to Consensus).

    Tue, 10/11/2011 - 15:01 — SomNambul
    Authors: Antonio Fernández Anta, Sergio Arévalo, Damien Imbs, Ernesto Jiménez, Michel Raynal
    Subjects: and Cluster Computing, Distributed, Parallel
    Abstract

    This paper addresses the consensus problem in homonymous distributed systems
    where processes are prone to crash failures and have no initial knowledge of
    the system membership ("homonymous" means that several processes may have the
    same identifier). New classes of failure detectors suited to these systems are
    first defined. Among them, the classes H\Omega\ and H\Sigma\ are introduced
    that are the homonymous counterparts of the classes \Omega\ and \Sigma,
    respectively.

  2. Conauto-2.0: Fast Isomorphism Testing and Automorphism Group Computation.

    Fri, 08/05/2011 - 15:01 — SomNambul
    Authors: Antonio Fernández Anta, José Luis López-Presa, Luis Núñez Chiroque
    Subjects: Data Structures and Algorithms
    Abstract

    In this paper we present an algorithm, called conauto-2.0, that can
    efficiently compute a set of generators of the automorphism group of a graph,
    and test whether two graphs are isomorphic, finding an isomorphism if they are.
    This algorithm uses the basic individualization/refinement technique, and is an
    improved version of the algorithm conauto, which has been shown to be very fast
    for random graphs and several families of hard graphs.

  3. Distance-based Node Sampling using Drifting Random Walks.

    Thu, 07/07/2011 - 15:01 — SomNambul
    Authors: Antonio Fernández Anta, Andrés Sevilla, Alberto Mozo
    Subjects: and Cluster Computing, Distributed, Parallel
    Abstract

    Sampling a large network with a given distribution has been identified as a
    useful operation to build network overlays. For example, sampling nodes with
    uniform probability is the cornerstone of epidemic information spreading, and
    constructing small world network topologies can be done by sampling with a
    probability that depends on the distance to a given node. In this paper we
    describe distributed algorithms for sampling networks, so that a node is
    selected with a probability that is a function of the distance of the node to a
    special node, called the \emph{source}.

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