857 research outputs found
Distributed anonymous discrete function computation
We propose a model for deterministic distributed function computation by a
network of identical and anonymous nodes. In this model, each node has bounded
computation and storage capabilities that do not grow with the network size.
Furthermore, each node only knows its neighbors, not the entire graph. Our goal
is to characterize the class of functions that can be computed within this
model. In our main result, we provide a necessary condition for computability
which we show to be nearly sufficient, in the sense that every function that
satisfies this condition can at least be approximated. The problem of computing
suitably rounded averages in a distributed manner plays a central role in our
development; we provide an algorithm that solves it in time that grows
quadratically with the size of the network
Leader election in synchronous networks
Worst, best and average number of messages and running time of leader election algorithms of different distributed systems are analyzed. Among others the known characterizations of the expected number of messages for LCR algorithm and of the worst number of messages of Hirschberg-Sinclair algorithm are improve
Distributed Computing in the Asynchronous LOCAL model
The LOCAL model is among the main models for studying locality in the
framework of distributed network computing. This model is however subject to
pertinent criticisms, including the facts that all nodes wake up
simultaneously, perform in lock steps, and are failure-free. We show that
relaxing these hypotheses to some extent does not hurt local computing. In
particular, we show that, for any construction task associated to a locally
checkable labeling (LCL), if is solvable in rounds in the LOCAL model,
then remains solvable in rounds in the asynchronous LOCAL model.
This improves the result by Casta\~neda et al. [SSS 2016], which was restricted
to 3-coloring the rings. More generally, the main contribution of this paper is
to show that, perhaps surprisingly, asynchrony and failures in the computations
do not restrict the power of the LOCAL model, as long as the communications
remain synchronous and failure-free
Gracefully Degrading Gathering in Dynamic Rings
Gracefully degrading algorithms [Biely \etal, TCS 2018] are designed to
circumvent impossibility results in dynamic systems by adapting themselves to
the dynamics. Indeed, such an algorithm solves a given problem under some
dynamics and, moreover, guarantees that a weaker (but related) problem is
solved under a higher dynamics under which the original problem is impossible
to solve. The underlying intuition is to solve the problem whenever possible
but to provide some kind of quality of service if the dynamics become
(unpredictably) higher.In this paper, we apply for the first time this approach
to robot networks. We focus on the fundamental problem of gathering a squad of
autonomous robots on an unknown location of a dynamic ring. In this goal, we
introduce a set of weaker variants of this problem. Motivated by a set of
impossibility results related to the dynamics of the ring, we propose a
gracefully degrading gathering algorithm
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