15,630 research outputs found
Shortest, Fastest, and Foremost Broadcast in Dynamic Networks
Highly dynamic networks rarely offer end-to-end connectivity at a given time.
Yet, connectivity in these networks can be established over time and space,
based on temporal analogues of multi-hop paths (also called {\em journeys}).
Attempting to optimize the selection of the journeys in these networks
naturally leads to the study of three cases: shortest (minimum hop), fastest
(minimum duration), and foremost (earliest arrival) journeys. Efficient
centralized algorithms exists to compute all cases, when the full knowledge of
the network evolution is given.
In this paper, we study the {\em distributed} counterparts of these problems,
i.e. shortest, fastest, and foremost broadcast with termination detection
(TDB), with minimal knowledge on the topology.
We show that the feasibility of each of these problems requires distinct
features on the evolution, through identifying three classes of dynamic graphs
wherein the problems become gradually feasible: graphs in which the
re-appearance of edges is {\em recurrent} (class R), {\em bounded-recurrent}
(B), or {\em periodic} (P), together with specific knowledge that are
respectively (the number of nodes), (a bound on the recurrence
time), and (the period). In these classes it is not required that all pairs
of nodes get in contact -- only that the overall {\em footprint} of the graph
is connected over time.
Our results, together with the strict inclusion between , , and ,
implies a feasibility order among the three variants of the problem, i.e.
TDB[foremost] requires weaker assumptions on the topology dynamics than
TDB[shortest], which itself requires less than TDB[fastest]. Reversely, these
differences in feasibility imply that the computational powers of ,
, and also form a strict hierarchy
The Computational Power of Beeps
In this paper, we study the quantity of computational resources (state
machine states and/or probabilistic transition precision) needed to solve
specific problems in a single hop network where nodes communicate using only
beeps. We begin by focusing on randomized leader election. We prove a lower
bound on the states required to solve this problem with a given error bound,
probability precision, and (when relevant) network size lower bound. We then
show the bound tight with a matching upper bound. Noting that our optimal upper
bound is slow, we describe two faster algorithms that trade some state
optimality to gain efficiency. We then turn our attention to more general
classes of problems by proving that once you have enough states to solve leader
election with a given error bound, you have (within constant factors) enough
states to simulate correctly, with this same error bound, a logspace TM with a
constant number of unary input tapes: allowing you to solve a large and
expressive set of problems. These results identify a key simplicity threshold
beyond which useful distributed computation is possible in the beeping model.Comment: Extended abstract to appear in the Proceedings of the International
Symposium on Distributed Computing (DISC 2015
Parallelizing RRT on large-scale distributed-memory architectures
This paper addresses the problem of parallelizing the Rapidly-exploring Random Tree (RRT) algorithm on large-scale distributed-memory architectures, using the Message Passing Interface. We compare three parallel versions of RRT based on classical parallelization schemes. We evaluate them on different motion planning problems and analyze the various factors influencing their performance
A System for Distributed Mechanisms: Design, Implementation and Applications
We describe here a structured system for distributed mechanism design
appropriate for both Intranet and Internet applications. In our approach the
players dynamically form a network in which they know neither their neighbours
nor the size of the network and interact to jointly take decisions. The only
assumption concerning the underlying communication layer is that for each pair
of processes there is a path of neighbours connecting them. This allows us to
deal with arbitrary network topologies.
We also discuss the implementation of this system which consists of a
sequence of layers. The lower layers deal with the operations that implement
the basic primitives of distributed computing, namely low level communication
and distributed termination, while the upper layers use these primitives to
implement high level communication among players, including broadcasting and
multicasting, and distributed decision making.
This yields a highly flexible distributed system whose specific applications
are realized as instances of its top layer. This design is implemented in Java.
The system supports at various levels fault-tolerance and includes a
provision for distributed policing the purpose of which is to exclude
`dishonest' players. Also, it can be used for repeated creation of dynamically
formed networks of players interested in a joint decision making implemented by
means of a tax-based mechanism. We illustrate its flexibility by discussing a
number of implemented examples.Comment: 36 pages; revised and expanded versio
Fast Distributed Computation of Distances in Networks
This paper presents a distributed algorithm to simultaneously compute the
diameter, radius and node eccentricity in all nodes of a synchronous network.
Such topological information may be useful as input to configure other
algorithms. Previous approaches have been modular, progressing in sequential
phases using building blocks such as BFS tree construction, thus incurring
longer executions than strictly required. We present an algorithm that, by
timely propagation of available estimations, achieves a faster convergence to
the correct values. We show local criteria for detecting convergence in each
node. The algorithm avoids the creation of BFS trees and simply manipulates
sets of node ids and hop counts. For the worst scenario of variable start
times, each node i with eccentricity ecc(i) can compute: the node eccentricity
in diam(G)+ecc(i)+2 rounds; the diameter in 2*diam(G)+ecc(i)+2 rounds; and the
radius in diam(G)+ecc(i)+2*radius(G) rounds.Comment: 12 page
Parallelizing RRT on distributed-memory architectures
This paper addresses the problem of improving the performance of the Rapidly-exploring Random Tree (RRT) algorithm by parallelizing it. For scalability reasons we do so on a distributed-memory architecture, using the message-passing paradigm. We present three parallel versions of RRT along with the technicalities involved in their implementation. We also evaluate the algorithms and study how they behave on different motion planning problems
Computing in Additive Networks with Bounded-Information Codes
This paper studies the theory of the additive wireless network model, in
which the received signal is abstracted as an addition of the transmitted
signals. Our central observation is that the crucial challenge for computing in
this model is not high contention, as assumed previously, but rather
guaranteeing a bounded amount of \emph{information} in each neighborhood per
round, a property that we show is achievable using a new random coding
technique.
Technically, we provide efficient algorithms for fundamental distributed
tasks in additive networks, such as solving various symmetry breaking problems,
approximating network parameters, and solving an \emph{asymmetry revealing}
problem such as computing a maximal input.
The key method used is a novel random coding technique that allows a node to
successfully decode the received information, as long as it does not contain
too many distinct values. We then design our algorithms to produce a limited
amount of information in each neighborhood in order to leverage our enriched
toolbox for computing in additive networks
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