Derivation of Fault Tolerance Measures of Self-Stabilizing Algorithms by Simulation

Müllner, Nils; Dhama, Abhishek; Theel, Oliver

doi:10.1109/anss-41.2008.26

Cited by 11 publications

(4 citation statements)

References 11 publications

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“…It is not available anymore. Müllner et al [24] present a simulator of the register model, a computational model which is close to the atomic-state model. This simulator does not allow to evaluate stabilization time.…”

Section: Introductionmentioning

confidence: 99%

Exploring Worst Cases of Self-stabilizing Algorithms Using Simulations

Jahier,

Altisen,

Devismes

2023

Lecture Notes in Computer Science

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Self-stabilization qualifies the ability of a distributed system to recover after transient failures. SASA is a simulator of self-stabilizing algorithms written in the atomic-state model, the most commonly used model in the selfstabilizing area. A simulator is, in particular, useful to study the time complexity of algorithms. For example, one can experimentally check whether existing theoretical bounds are correct or tight. Simulations are also useful to get insights when no bound is known.In this paper, we use SASA to investigate the worst cases of various well-known self-stabilization algorithms. We apply classical optimization methods (such as local search, branch and bound, Tabu list) on the two sources of non-determinism: the choice of initial configuration and the scheduling of process activations (daemon). We propose a methodology based on heuristics and an open-source tool to find tighter worst-case lower bounds.

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Section: Introductionmentioning

confidence: 99%

Exploring Worst Cases of Self-stabilizing Algorithms Using Simulations

Jahier,

Altisen,

Devismes

2023

Lecture Notes in Computer Science

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“…Related work. Only few simulators dedicated to self-stabilization in locally shared memory models, such as the ASM, have been proposed [11,15,21]. Overall, they all have limited capabilities and features, and are not extensible since not programmable.…”

Section: Introductionmentioning

confidence: 99%

“…Müllner et al [21] present a simulator (written in Erlang) of the register model, a computational model which is close to the ASM. This simulator does not allow to evaluate stabilization time.…”

Section: Introductionmentioning

confidence: 99%

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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“…On the other hand, practical performance evaluation of self-stabilizing algorithms typically uses simulation [13], [19], [18], [1], but most approaches consider running the algorithm from an initial random state, which is known to introduce significant bias [1] and also raises the question of case coverage since only a very small portion of possible states are encountered in a typical simulation run.…”

Section: Introductionmentioning

confidence: 99%

Rigorous Performance Evaluation of Self-Stabilization Using Probabilistic Model Checking

Fallahi

Bonakdarpour

Tixeuil

2013

2013 IEEE 32nd International Symposium on Reliable Distributed Systems

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We propose a new metric for effectively and accurately evaluating the performance of self-stabilizing algorithms. Self-stabilization is a versatile category of fault-tolerance that guarantees system recovery to normal behavior within a finite number of steps, when the state of the system is perturbed by transient faults (or equally, the initial state of the system can be some arbitrary state). The performance of self-stabilizing algorithms is conventionally characterized in the literature by asymptotic computation complexity. We argue that such characterization of performance is too abstract and does not reflect accurately the realities of deploying a distributed algorithm in practice. Our new metric for characterizing the performance of self-stabilizing algorithms is the expected mean value of recovery time. Our metric has several crucial features. Firstly, it encodes accurate average case speed of recovery. Secondly, we show that our evaluation method can effectively incorporate several other parameters that are of importance in practice and have no place in asymptotic computation complexity. Examples include the type of distributed scheduler, likelihood of occurrence of faults, the impact of faults on speed of recovery, and network topology. We utilize a deep analysis technique, namely, probabilistic model checking to rigorously compute our proposed metric. All our claims are backed by detailed case studies and experiments.

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Derivation of Fault Tolerance Measures of Self-Stabilizing Algorithms by Simulation

Cited by 11 publications

References 11 publications

Exploring Worst Cases of Self-stabilizing Algorithms Using Simulations

Exploring Worst Cases of Self-stabilizing Algorithms Using Simulations

sasa: a SimulAtor of Self-stabilizing Algorithms

Rigorous Performance Evaluation of Self-Stabilization Using Probabilistic Model Checking

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