2020
DOI: 10.1145/3385634.3385638
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Model checking randomized distributed algorithms

Abstract: Randomization is a powerful paradigm to solve hard problems, especially in distributed computing. Proving the correctness, and assessing the performances, of randomized distributed algorithms, is a very challenging research objective, that the verification community has started to address. In this article, we review existing model checking approaches to the verification of randomized distributed algorithms and identify further research directions.

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Cited by 2 publications
(2 citation statements)
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“…The presented study shares similarities with surveys presented in [9]- [11] for dealing with SSE problem, however, this paper collects the wide range of SSE reduction methods, offers explanations for each method, their success factor, and identifies challenges. Additionally, a discussion of SSE problems in component-based systems is also covered.…”
Section: Introductionmentioning
confidence: 93%
“…The presented study shares similarities with surveys presented in [9]- [11] for dealing with SSE problem, however, this paper collects the wide range of SSE reduction methods, offers explanations for each method, their success factor, and identifies challenges. Additionally, a discussion of SSE problems in component-based systems is also covered.…”
Section: Introductionmentioning
confidence: 93%
“…Building a DC model on the Hadoop platform requires the use of a Storm cluster. The coordination and cooperation between Storm nodes is completed by ZooKeeper, which records all the states of each node, and if it stops, it is recorded in the local disk [15]. It is because of this feature that Storm has a high stability in practical applications and can well cope with various emergencies.…”
Section: Model Based On Three-layer B/s Modementioning
confidence: 99%