331 research outputs found
Fast and compact self-stabilizing verification, computation, and fault detection of an MST
This paper demonstrates the usefulness of distributed local verification of
proofs, as a tool for the design of self-stabilizing algorithms.In particular,
it introduces a somewhat generalized notion of distributed local proofs, and
utilizes it for improving the time complexity significantly, while maintaining
space optimality. As a result, we show that optimizing the memory size carries
at most a small cost in terms of time, in the context of Minimum Spanning Tree
(MST). That is, we present algorithms that are both time and space efficient
for both constructing an MST and for verifying it.This involves several parts
that may be considered contributions in themselves.First, we generalize the
notion of local proofs, trading off the time complexity for memory efficiency.
This adds a dimension to the study of distributed local proofs, which has been
gaining attention recently. Specifically, we design a (self-stabilizing) proof
labeling scheme which is memory optimal (i.e., bits per node), and
whose time complexity is in synchronous networks, or time in asynchronous ones, where is the maximum degree of
nodes. This answers an open problem posed by Awerbuch and Varghese (FOCS 1991).
We also show that time is necessary, even in synchronous
networks. Another property is that if faults occurred, then, within the
requireddetection time above, they are detected by some node in the locality of each of the faults.Second, we show how to enhance a known
transformer that makes input/output algorithms self-stabilizing. It now takes
as input an efficient construction algorithm and an efficient self-stabilizing
proof labeling scheme, and produces an efficient self-stabilizing algorithm.
When used for MST, the transformer produces a memory optimal self-stabilizing
algorithm, whose time complexity, namely, , is significantly better even
than that of previous algorithms. (The time complexity of previous MST
algorithms that used memory bits per node was , and
the time for optimal space algorithms was .) Inherited from our proof
labelling scheme, our self-stabilising MST construction algorithm also has the
following two properties: (1) if faults occur after the construction ended,
then they are detected by some nodes within time in synchronous
networks, or within time in asynchronous ones, and (2) if
faults occurred, then, within the required detection time above, they are
detected within the locality of each of the faults. We also show
how to improve the above two properties, at the expense of some increase in the
memory
Efficient, Superstabilizing Decentralised Optimisation for Dynamic Task Allocation Environments
Decentralised optimisation is a key issue for multi-agent systems, and while many solution techniques have been developed, few provide support for dynamic environments, which change over time, such as disaster management. Given this, in this paper, we present Bounded Fast Max Sum (BFMS): a novel, dynamic, superstabilizing algorithm which provides a bounded approximate solution to certain classes of distributed constraint optimisation problems. We achieve this by eliminating dependencies in the constraint functions, according to how much impact they have on the overall solution value. In more detail, we propose iGHS, which computes a maximum spanning tree on subsections of the constraint graph, in order to reduce communication and computation overheads. Given this, we empirically evaluate BFMS, which shows that BFMS reduces communication and computation done by Bounded Max Sum by up to 99%, while obtaining 60-88% of the optimal utility
From Sequential Layers to Distributed Processes, Deriving a minimum weight spanning tree algorithm
Analysis and design of distributed algorithms and protocols are difficult issues. An important cause for those difficulties is the fact that the logical structure of the solution is often invisible in the actual implementation. We introduce a framework that allows for a formal treatment of the design process, from an abstract initial design to an implementation tailored to specific architectures. A combination of algebraic and axiomatic techniques is used to verify correctness of the derivation steps. This is shown by deriving an implementation of a distributed minimum weight spanning tree algorithm in the style of [GHS]
A time- and message-optimal distributed algorithm for minimum spanning trees
This paper presents a randomized Las Vegas distributed algorithm that
constructs a minimum spanning tree (MST) in weighted networks with optimal (up
to polylogarithmic factors) time and message complexity. This algorithm runs in
time and exchanges messages (both with
high probability), where is the number of nodes of the network, is the
diameter, and is the number of edges. This is the first distributed MST
algorithm that matches \emph{simultaneously} the time lower bound of
[Elkin, SIAM J. Comput. 2006] and the message
lower bound of [Kutten et al., J.ACM 2015] (which both apply to
randomized algorithms).
The prior time and message lower bounds are derived using two completely
different graph constructions; the existing lower bound construction that shows
one lower bound {\em does not} work for the other. To complement our algorithm,
we present a new lower bound graph construction for which any distributed MST
algorithm requires \emph{both} rounds and
messages
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