18,727 research outputs found
On the Computational Power of Radio Channels
Radio networks can be a challenging platform for which to develop distributed algorithms, because the network nodes must contend for a shared channel. In some cases, though, the shared medium is an advantage rather than a disadvantage: for example, many radio network algorithms cleverly use the shared channel to approximate the degree of a node, or estimate the contention. In this paper we ask how far the inherent power of a shared radio channel goes, and whether it can efficiently compute "classicaly hard" functions such as Majority, Approximate Sum, and Parity.
Using techniques from circuit complexity, we show that in many cases, the answer is "no". We show that simple radio channels, such as the beeping model or the channel with collision-detection, can be approximated by a low-degree polynomial, which makes them subject to known lower bounds on functions such as Parity and Majority; we obtain round lower bounds of the form Omega(n^{delta}) on these functions, for delta in (0,1). Next, we use the technique of random restrictions, used to prove AC^0 lower bounds, to prove a tight lower bound of Omega(1/epsilon^2) on computing a (1 +/- epsilon)-approximation to the sum of the nodes\u27 inputs. Our techniques are general, and apply to many types of radio channels studied in the literature
Randomized protocols for asynchronous consensus
The famous Fischer, Lynch, and Paterson impossibility proof shows that it is
impossible to solve the consensus problem in a natural model of an asynchronous
distributed system if even a single process can fail. Since its publication,
two decades of work on fault-tolerant asynchronous consensus algorithms have
evaded this impossibility result by using extended models that provide (a)
randomization, (b) additional timing assumptions, (c) failure detectors, or (d)
stronger synchronization mechanisms than are available in the basic model.
Concentrating on the first of these approaches, we illustrate the history and
structure of randomized asynchronous consensus protocols by giving detailed
descriptions of several such protocols.Comment: 29 pages; survey paper written for PODC 20th anniversary issue of
Distributed Computin
Randomization Adaptive Self-Stabilization
We present a scheme to convert self-stabilizing algorithms that use
randomization during and following convergence to self-stabilizing algorithms
that use randomization only during convergence. We thus reduce the number of
random bits from an infinite number to a bounded number. The scheme is
applicable to the cases in which there exits a local predicate for each node,
such that global consistency is implied by the union of the local predicates.
We demonstrate our scheme over the token circulation algorithm of Herman and
the recent constant time Byzantine self-stabilizing clock synchronization
algorithm by Ben-Or, Dolev and Hoch. The application of our scheme results in
the first constant time Byzantine self-stabilizing clock synchronization
algorithm that uses a bounded number of random bits
Randomized Two-Process Wait-Free Test-and-Set
We present the first explicit, and currently simplest, randomized algorithm
for 2-process wait-free test-and-set. It is implemented with two 4-valued
single writer single reader atomic variables. A test-and-set takes at most 11
expected elementary steps, while a reset takes exactly 1 elementary step. Based
on a finite-state analysis, the proofs of correctness and expected length are
compressed into one table.Comment: 9 pages, 4 figures, LaTeX source; Submitte
On the Complexity of List Ranking in the Parallel External Memory Model
We study the problem of list ranking in the parallel external memory (PEM)
model. We observe an interesting dual nature for the hardness of the problem
due to limited information exchange among the processors about the structure of
the list, on the one hand, and its close relationship to the problem of
permuting data, which is known to be hard for the external memory models, on
the other hand.
By carefully defining the power of the computational model, we prove a
permuting lower bound in the PEM model. Furthermore, we present a stronger
\Omega(log^2 N) lower bound for a special variant of the problem and for a
specific range of the model parameters, which takes us a step closer toward
proving a non-trivial lower bound for the list ranking problem in the
bulk-synchronous parallel (BSP) and MapReduce models. Finally, we also present
an algorithm that is tight for a larger range of parameters of the model than
in prior work
Using Collective Intelligence to Route Internet Traffic
A COllective INtelligence (COIN) is a set of interacting reinforcement
learning (RL) algorithms designed in an automated fashion so that their
collective behavior optimizes a global utility function. We summarize the
theory of COINs, then present experiments using that theory to design COINs to
control internet traffic routing. These experiments indicate that COINs
outperform all previously investigated RL-based, shortest path routing
algorithms.Comment: 7 page
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