Introduction to Distributed Self-Stabilizing Algorithms

Altisen, Karine; Devismes, Stéphane; Dubois, Swan; Petit, Franck

doi:10.2200/s00908ed1v01y201903dct015

Cited by 31 publications

(32 citation statements)

References 104 publications

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“…Algorithms under test. We have implemented the following self-stabilizing algorithms: a token circulation for rooted unidirectional rings assuming a distributed daemon (DTR [8]); a breadth first search spanning tree construction for rooted networks assuming a distributed daemon (BFS [2]); a depth first search spanning tree construction for rooted networks assuming a distributed daemon (DFS [6]); a coloring algorithm for anonymous networks assuming a locally central daemon (COL [13]); a synchronous unison for anonymous networks assuming a synchronous daemon (SYN [3]); and Algorithm 1 (ASY [7]). All these algorithms can be found in the SASA gitlab repository.…”

Section: Resultsmentioning

confidence: 99%

“…The first stage is made of the atomic and synchronous execution of all activated nodes (1-a), followed by the evaluation of which nodes are enabled in the next step (1-b). 4 At the second stage, a daemon nondeterministically chooses among the enabled nodes which ones should be activated at the next step (2). Overall, this can be viewed as a reactive program (Stage (1)) that runs in closed-loop with its environment (Stage (2)), where the outputs (resp.…”

Section: Connection To the Synchrone Reactive Toolboxmentioning

confidence: 99%

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sasa: a SimulAtor of Self-stabilizing Algorithms

Altisen

Devismes

Jahier

2022

The Computer Journal

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In this paper, we present sasa, an open-source SimulAtor of Self-stabilizing Algorithms. Self-stabilization defines the ability of a distributed algorithm to recover after transient failures. sasa is implemented as a faithful representation of the atomic-state model (also called the locally shared memory model with composite atomicity). This model is the most commonly used one in the self-stabilizing area to prove both the correct operation of self-stabilizing algorithms and complexity bounds on them. sasa encompasses all features necessary to debug, test and analyze self-stabilizing algorithms. All these facilities are programmable to enable users to accommodate to their particular needs. For example, asynchrony is modeled by programmable stochastic daemons playing the role of input sequence generators. Properties of algorithms can be checked using formal test oracles. The sasa distribution also provides several facilities to easily achieve (batch-mode) simulation campaigns. We show that the lightweight design of sasa allows to efficiently perform huge such campaigns. Following a modular approach, we have aimed at relying as much as possible the design of sasa on existing tools, including ocaml, dot and several tools developed in the Synchrone Group of the VERIMAG laboratory.

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Section: Resultsmentioning

confidence: 99%

Section: Connection To the Synchrone Reactive Toolboxmentioning

confidence: 99%

sasa: a SimulAtor of Self-stabilizing Algorithms

Altisen

Devismes

Jahier

2022

The Computer Journal

Self Cite

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“…The stabilization time of a randomized (self-stabilizing) algorithm on a given graph is a random variable and one typically aims towards bounding it in expectation and whp. 4 The schedule { } ≥0 is said to be synchronous if = for all ∈ Z ≥0 which means that ( ) = for = 0, 1, . .…”

Section: Computational Modelmentioning

confidence: 99%

“…When it comes to recovering from transient faults, the agreed upon concept for fault tolerance is self-stabilization. Introduced in the seminal paper of Dijkstra [14], an algorithm is self-stabilizing if it is guaranteed to converge to a correct output from any (possibly faulty) initial configuration [4,15].…”

Section: Introductionmentioning

confidence: 99%

A Thin Self-Stabilizing Asynchronous Unison Algorithm with Applications to Fault Tolerant Biological Networks

Emek

Keren

2021

Proceedings of the 2021 ACM Symposium on Principles of Distributed Computing

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Introduced by Emek and Wattenhofer (PODC 2013), the stone age (SA) model provides an abstraction for network algorithms distributed over randomized finite state machines. This model, designed to resemble the dynamics of biological processes in cellular networks, assumes a weak communication scheme that is built upon the nodes' ability to sense their vicinity in an asynchronous manner. Recent works demonstrate that the weak computation and communication capabilities of the SA model suffice for efficient solutions to some core tasks in distributed computing, but they do so under the (somewhat less realistic) assumption of fault free computations. In this paper, we initiate the study of self-stabilizing SA algorithms that are guaranteed to recover from any combination of transient faults. Specifically, we develop efficient self-stabilizing SA algorithms for the leader election and maximal independent set tasks in bounded diameter graphs subject to an asynchronous scheduler. These algorithms rely on a novel efficient self-stabilizing asynchronous unison (AU) algorithm that is "thin" in terms of its state space: the number of states used by the AU algorithm is linear in the graph's diameter bound, irrespective of the number of nodes.

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“…Different algorithms and control strategies are widely studied and adopted to improve the efficiency of the distributed control (Xiao et al, 2019) and its natural features of error tolerance (Raynal, 2018). Specific algorithms have been used, including clock synchronization theories (Dalwadi and Padole, 2017) and self-stabilization algorithm (Altisen et al, 2019), to ensure the information consistency and control stability, which are very precise and complicated to realize in some real scenarios as for controllable power loads. Therefore, the distributed control strategy is prominent in large-scale collaborations at the current stage.…”

Section: Introductionmentioning

confidence: 99%

A Weak-Consistency–Oriented Collaborative Strategy for Large-Scale Distributed Demand Response

et al. 2021

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Large-scale distributed demand response is a hotspot in the development of power systems, which is of much significance in accelerating the consumption of new energy power generation and the process of clean energy substitution. However, the rigorous distributed algorithms utilized in current research studies are mostly very complicated for the large-scale demand response, requiring high quality of information systems. Considering the electrical features of power systems, a weak-consistency–oriented collaborative strategy is proposed for the practical implementation of the large-scale distributed demand response in this study. First, the basic conditions and objectives of demand response are explored from the view of system operators, and the challenges of large-scale demand response are discussed and furthermore modelled with a simplification based on the power system characteristics, including uncertainties and fluctuations. Then, a weakly consistent distributed strategy for demand response is proposed based on the Paxos distributed algorithm, where the information transmission is redesigned based on the electrical features to achieve better error tolerance. Using case studies with different information transmission error rates and other conditions, the proposed strategy is demonstrated to be an effective solution for the large-scale distributed demand response implementation, with a robust response capability under even remarkable transmission errors. By integrating the proposed strategy, the requirement for the large-scale distributed systems, especially the information systems, is highly eased, leading to the acceleration of the practical demand response implementation.

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Introduction to Distributed Self-Stabilizing Algorithms

Cited by 31 publications

References 104 publications

sasa: a SimulAtor of Self-stabilizing Algorithms

sasa: a SimulAtor of Self-stabilizing Algorithms

A Thin Self-Stabilizing Asynchronous Unison Algorithm with Applications to Fault Tolerant Biological Networks

A Weak-Consistency–Oriented Collaborative Strategy for Large-Scale Distributed Demand Response

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