Configuration of inter-process communication with probabilistic model checking

Herrmann, Linda; Küttler, Martin; Stumpf, Tobias; Baier, Christel; Härtig, Hermann; Klüppelholz, Sascha

doi:10.1007/s10009-019-00536-0

Cited by 1 publication

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“…In [ 21 ], the authors analyze the fault-tolerance of inter-process communication in a space probe. They use parametric probabilistic model checking to compute rational functions for the availability of the space probe and find the optimal parameter values.…”

Section: Related Workmentioning

confidence: 99%

Synthesizing optimal bias in randomized self-stabilization

et al. 2021

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Randomization is a key concept in distributed computing to tackle impossibility results. This also holds for self-stabilization in anonymous networks where coin flips are often used to break symmetry. Although the use of randomization in self-stabilizing algorithms is rather common, it is unclear what the optimal coin bias is so as to minimize the expected convergence time. This paper proposes a technique to automatically synthesize this optimal coin bias. Our algorithm is based on a parameter synthesis approach from the field of probabilistic model checking. It over- and under-approximates a given parameter region and iteratively refines the regions with minimal convergence time up to the desired accuracy. We describe the technique in detail and present a simple parallelization that gives an almost linear speed-up. We show the applicability of our technique to determine the optimal bias for the well-known Herman’s self-stabilizing token ring algorithm. Our synthesis obtains that for small rings, a fair coin is optimal, whereas for larger rings a biased coin is optimal where the bias grows with the ring size. We also analyze a variant of Herman’s algorithm that coincides with the original algorithm but deviates for biased coins. Finally, we show how using speed reducers in Herman’s protocol improve the expected convergence time.

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Section: Related Workmentioning

confidence: 99%