59,133 research outputs found
A More Faithful Formal Definition of the Desired Property for Distributed Snapshot Algorithms to Model Check the Property
The first distributed snapshot algorithm was invented by Chandy and Lamport: Chandy-Lamport distributed snapshot algorithm (CLDSA). Distributed snapshot algorithms are crucial components to make distributed systems fault tolerant. Such algorithms are extremely important because many modern key software systems are in the form of distributed systems and should be fault tolerant. There are at least two desired properties such algorithms should satisfy: 1) the distributed snapshot reachability property (called the DSR property) and 2) the ability to run concurrently with, but not alter, an underlying distributed system (UDS). This paper identifies subtle errors in a paper on formalization of the DSR property and shows how to correct them. We give a more faithful formal definition of the DSR property; the definition involves two state machines - one state machine M_UDS that formalizes a UDS and the other M_CLDSA that formalizes the UDS on which CLDSA is superimposed (UDS-CLDSA) - and can be used to more precise model checking of the DSR property for CLDSA. We also prove a theorem on equivalence of our new definition and an existing one that only involves M_CLDSA to guarantee the validity of the existing model checking approach. Moreover, we prove the second property, namely that CLDSA does not alter the behaviors of UDS
Revisiting Snapshot Algorithms by Refinement-based Techniques
International audienceThe snapshot problem addresses a collection of important algorithmic issues related to the distributed computations, which are used for debugging or recovering the distributed programs. Among the existing solutions, Chandy and Lamport propose a simple distributed algorithm. In this paper, we explore the correct-by-construction process to formalize the snapshot algorithms in distributed system. The formalization process is based on a modeling language Event B, which supports a refinement-based incremental development using RODIN platform. These refinement-based techniques help to derive a correct distributed algorithm. Moreover, we demonstrate how this class of other distributed algorithms can be revisited. A consequence is to provide a fully mechanized proof of the distributed algorithms
Data Collection and Capacity Analysis in Large-scale Wireless Sensor Networks
In this dissertation, we study data collection and its achievable network capacity in Wireless Sensor Networks (WSNs). Firstly, we investigate the data collection issue in dual-radio multi-channel WSNs under the protocol interference model. We propose a multi-path scheduling algorithm for snapshot data collection, which has a tighter capacity bound than the existing best result, and a novel continuous data collection algorithm with comprehensive capacity analysis. Secondly, considering most existing works for the capacity issue are based on the ideal deterministic network model, we study the data collection problem for practical probabilistic WSNs. We design a cell-based path scheduling algorithm and a zone-based pipeline scheduling algorithm for snapshot and continuous data collection in probabilistic WSNs, respectively. By analysis, we show that the proposed algorithms have competitive capacity performance compared with existing works. Thirdly, most of the existing works studying the data collection capacity issue are for centralized synchronous WSNs. However, wireless networks are more likely to be distributed asynchronous systems. Therefore, we investigate the achievable data collection capacity of realistic distributed asynchronous WSNs and propose a data collection algorithm with fairness consideration. Theoretical analysis of the proposed algorithm shows that its achievable network capacity is order-optimal as centralized and synchronized algorithms do and independent of network size. Finally, for completeness, we study the data aggregation issue for realistic probabilistic WSNs. We propose order-optimal scheduling algorithms for snapshot and continuous data aggregation under the physical interference model
High-resolution distributed sampling of bandlimited fields with low-precision sensors
The problem of sampling a discrete-time sequence of spatially bandlimited
fields with a bounded dynamic range, in a distributed,
communication-constrained, processing environment is addressed. A central unit,
having access to the data gathered by a dense network of fixed-precision
sensors, operating under stringent inter-node communication constraints, is
required to reconstruct the field snapshots to maximum accuracy. Both
deterministic and stochastic field models are considered. For stochastic
fields, results are established in the almost-sure sense. The feasibility of
having a flexible tradeoff between the oversampling rate (sensor density) and
the analog-to-digital converter (ADC) precision, while achieving an exponential
accuracy in the number of bits per Nyquist-interval per snapshot is
demonstrated. This exposes an underlying ``conservation of bits'' principle:
the bit-budget per Nyquist-interval per snapshot (the rate) can be distributed
along the amplitude axis (sensor-precision) and space (sensor density) in an
almost arbitrary discrete-valued manner, while retaining the same (exponential)
distortion-rate characteristics. Achievable information scaling laws for field
reconstruction over a bounded region are also derived: With N one-bit sensors
per Nyquist-interval, Nyquist-intervals, and total network
bitrate (per-sensor bitrate ), the maximum pointwise distortion goes to zero as
or . This is shown to be possible
with only nearest-neighbor communication, distributed coding, and appropriate
interpolation algorithms. For a fixed, nonzero target distortion, the number of
fixed-precision sensors and the network rate needed is always finite.Comment: 17 pages, 6 figures; paper withdrawn from IEEE Transactions on Signal
Processing and re-submitted to the IEEE Transactions on Information Theor
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
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