980 research outputs found
Self-stabilizing Numerical Iterative Computation
Many challenging tasks in sensor networks, including sensor calibration,
ranking of nodes, monitoring, event region detection, collaborative filtering,
collaborative signal processing, {\em etc.}, can be formulated as a problem of
solving a linear system of equations. Several recent works propose different
distributed algorithms for solving these problems, usually by using linear
iterative numerical methods.
In this work, we extend the settings of the above approaches, by adding
another dimension to the problem. Specifically, we are interested in {\em
self-stabilizing} algorithms, that continuously run and converge to a solution
from any initial state. This aspect of the problem is highly important due to
the dynamic nature of the network and the frequent changes in the measured
environment.
In this paper, we link together algorithms from two different domains. On the
one hand, we use the rich linear algebra literature of linear iterative methods
for solving systems of linear equations, which are naturally distributed with
rapid convergence properties. On the other hand, we are interested in
self-stabilizing algorithms, where the input to the computation is constantly
changing, and we would like the algorithms to converge from any initial state.
We propose a simple novel method called \syncAlg as a self-stabilizing variant
of the linear iterative methods. We prove that under mild conditions the
self-stabilizing algorithm converges to a desired result. We further extend
these results to handle the asynchronous case.
As a case study, we discuss the sensor calibration problem and provide
simulation results to support the applicability of our approach
An Optimal Self-Stabilizing Firing Squad
Consider a fully connected network where up to processes may crash, and
all processes start in an arbitrary memory state. The self-stabilizing firing
squad problem consists of eventually guaranteeing simultaneous response to an
external input. This is modeled by requiring that the non-crashed processes
"fire" simultaneously if some correct process received an external "GO" input,
and that they only fire as a response to some process receiving such an input.
This paper presents FireAlg, the first self-stabilizing firing squad algorithm.
The FireAlg algorithm is optimal in two respects: (a) Once the algorithm is
in a safe state, it fires in response to a GO input as fast as any other
algorithm does, and (b) Starting from an arbitrary state, it converges to a
safe state as fast as any other algorithm does.Comment: Shorter version to appear in SSS0
Self-stabilization Overhead: an Experimental Case Study on Coded Atomic Storage
Shared memory emulation can be used as a fault-tolerant and highly available
distributed storage solution or as a low-level synchronization primitive.
Attiya, Bar-Noy, and Dolev were the first to propose a single-writer,
multi-reader linearizable register emulation where the register is replicated
to all servers. Recently, Cadambe et al. proposed the Coded Atomic Storage
(CAS) algorithm, which uses erasure coding for achieving data redundancy with
much lower communication cost than previous algorithmic solutions.
Although CAS can tolerate server crashes, it was not designed to recover from
unexpected, transient faults, without the need of external (human)
intervention. In this respect, Dolev, Petig, and Schiller have recently
developed a self-stabilizing version of CAS, which we call CASSS. As one would
expect, self-stabilization does not come as a free lunch; it introduces,
mainly, communication overhead for detecting inconsistencies and stale
information. So, one would wonder whether the overhead introduced by
self-stabilization would nullify the gain of erasure coding.
To answer this question, we have implemented and experimentally evaluated the
CASSS algorithm on PlanetLab; a planetary scale distributed infrastructure. The
evaluation shows that our implementation of CASSS scales very well in terms of
the number of servers, the number of concurrent clients, as well as the size of
the replicated object. More importantly, it shows (a) to have only a constant
overhead compared to the traditional CAS algorithm (which we also implement)
and (b) the recovery period (after the last occurrence of a transient fault) is
as fast as a few client (read/write) operations. Our results suggest that CASSS
does not significantly impact efficiency while dealing with automatic recovery
from transient faults and bounded size of needed resources
Tight Bounds for MIS in Multichannel Radio Networks
Daum et al. [PODC'13] presented an algorithm that computes a maximal
independent set (MIS) within
rounds in an -node multichannel radio network with communication
channels. The paper uses a multichannel variant of the standard graph-based
radio network model without collision detection and it assumes that the network
graph is a polynomially bounded independence graph (BIG), a natural
combinatorial generalization of well-known geographic families. The upper bound
of that paper is known to be optimal up to a polyloglog factor.
In this paper, we adapt algorithm and analysis to improve the result in two
ways. Mainly, we get rid of the polyloglog factor in the runtime and we thus
obtain an asymptotically optimal multichannel radio network MIS algorithm. In
addition, our new analysis allows to generalize the class of graphs from those
with polynomially bounded local independence to graphs where the local
independence is bounded by an arbitrary function of the neighborhood radius.Comment: 37 pages, to be published in DISC 201
Avatar: A Time- and Space-Efficient Self-Stabilizing Overlay Network
Overlay networks present an interesting challenge for fault-tolerant
computing. Many overlay networks operate in dynamic environments (e.g. the
Internet), where faults are frequent and widespread, and the number of
processes in a system may be quite large. Recently, self-stabilizing overlay
networks have been presented as a method for managing this complexity.
\emph{Self-stabilizing overlay networks} promise that, starting from any
weakly-connected configuration, a correct overlay network will eventually be
built. To date, this guarantee has come at a cost: nodes may either have high
degree during the algorithm's execution, or the algorithm may take a long time
to reach a legal configuration. In this paper, we present the first
self-stabilizing overlay network algorithm that does not incur this penalty.
Specifically, we (i) present a new locally-checkable overlay network based upon
a binary search tree, and (ii) provide a randomized algorithm for
self-stabilization that terminates in an expected polylogarithmic number of
rounds \emph{and} increases a node's degree by only a polylogarithmic factor in
expectation
Fast and Compact Distributed Verification and Self-Stabilization of a DFS Tree
We present algorithms for distributed verification and silent-stabilization
of a DFS(Depth First Search) spanning tree of a connected network. Computing
and maintaining such a DFS tree is an important task, e.g., for constructing
efficient routing schemes. Our algorithm improves upon previous work in various
ways. Comparable previous work has space and time complexities of bits per node and respectively, where is the highest
degree of a node, is the number of nodes and is the diameter of the
network. In contrast, our algorithm has a space complexity of bits
per node, which is optimal for silent-stabilizing spanning trees and runs in
time. In addition, our solution is modular since it utilizes the
distributed verification algorithm as an independent subtask of the overall
solution. It is possible to use the verification algorithm as a stand alone
task or as a subtask in another algorithm. To demonstrate the simplicity of
constructing efficient DFS algorithms using the modular approach, We also
present a (non-sielnt) self-stabilizing DFS token circulation algorithm for
general networks based on our silent-stabilizing DFS tree. The complexities of
this token circulation algorithm are comparable to the known ones
Silent MST approximation for tiny memory
In network distributed computing, minimum spanning tree (MST) is one of the
key problems, and silent self-stabilization one of the most demanding
fault-tolerance properties. For this problem and this model, a polynomial-time
algorithm with memory is known for the state model. This is
memory optimal for weights in the classic range (where
is the size of the network). In this paper, we go below this
memory, using approximation and parametrized complexity.
More specifically, our contributions are two-fold. We introduce a second
parameter~, which is the space needed to encode a weight, and we design a
silent polynomial-time self-stabilizing algorithm, with space . In turn, this allows us to get an approximation algorithm for the problem,
with a trade-off between the approximation ratio of the solution and the space
used. For polynomial weights, this trade-off goes smoothly from memory for an -approximation, to memory for exact solutions,
with for example memory for a 2-approximation
On the Tomography of Networks and Multicast Trees
In this paper we model the tomography of scale free networks by studying the
structure of layers around an arbitrary network node. We find, both
analytically and empirically, that the distance distribution of all nodes from
a specific network node consists of two regimes. The first is characterized by
rapid growth, and the second decays exponentially. We also show that the nodes
degree distribution at each layer is a power law with an exponential cut-off.
We obtain similar results for the layers surrounding the root of multicast
trees cut from such networks, as well as the Internet. All of our results were
obtained both analytically and on empirical Interenet data
Modeling the Influence of Antifreeze Proteins on Three-Dimensional Ice Crystal Melt Shapes using a Geometric Approach
The melting of pure axisymmetric ice crystals has been described previously
by us within the framework of so-called geometric crystal growth.
Nonequilibrium ice crystal shapes evolving in the presence of hyperactive
antifreeze proteins (hypAFPs) are experimentally observed to assume ellipsoidal
geometries ("lemon" or "rice" shapes). To analyze such shapes we harness the
underlying symmetry of hexagonal ice Ih and extend two-dimensional geometric
models to three-dimensions to reproduce the experimental dissolution process.
The geometrical model developed will be useful as a quantitative test of the
mechanisms of interaction between hypAFPs and ice.Comment: 15 pages, 5 figures; Proc. R. Soc. A, Published online before print
June 27, 201
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