Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.1925
This item is licensed with a CreativeCommons licence by/4.0
Publisher DOI: doi: 10.3390/a11050058
Title: Computing Fault-Containment Times of Self-Stabilizing Algorithms Using Lumped Markov Chains
Language: English
Authors: Turau, Volker 
Keywords: distributed algorithms;fault-tolerance;self-stabilization;Markov chain;lumping
Issue Date: 3-May-2018
Publisher: Multidisciplinary Digital Publishing Institute
Source: Algorithms 11 (2018), 5 : 58
Journal or Series Name: Algorithms 
Abstract (english): The analysis of self-stabilizing algorithms is often limited to the worst case stabilization time starting from an arbitrary state, i.e., a state resulting from a sequence of faults. Considering the fact that these algorithms are intended to provide fault tolerance in the long run, this is not the most relevant metric. A common situation is that a running system is an a legitimate state when hit by a single fault. This event has a much higher probability than multiple concurrent faults. Therefore, the worst case time to recover from a single fault is more relevant than the recovery time from a large number of faults. This paper presents techniques to derive upper bounds for the mean time to recover from a single fault for self-stabilizing algorithms based on Markov chains in combination with lumping. To illustrate the applicability of the techniques they are applied to a new self-stabilizing coloring algorithm.
URI: http://tubdok.tub.tuhh.de/handle/11420/1928
DOI: 10.15480/882.1925
ISSN: 1999-4893
Other Identifiers: doi: 10.3390/a11050058
Institute: Telematik E-17 
Type: (wissenschaftlicher) Artikel
Funded by: Deutsche Forschungsgemeinschaft
Project: DFG (TU 221/6-2) 
Appears in Collections:Publications (tub.dok)

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