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Silent MST Approximation for Tiny Memory

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Lélia Blin
Swan Dubois
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Abstract

In this paper we show that approximation can help reduce the space used for self-stabilization. In the classic state model, where the nodes of a network communicate by reading the states of their neighbors, an important measure of efficiency is the space: the number of bits used at each node to encode the state. In this model, a classic requirement is that the algorithm has to be silent, that is, after stabilization the states should not change anymore. We design a silent self-stabilizing algorithm for the problem of minimum spanning tree, that has a trade-off between the quality of the solution and the space needed to compute it.
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Dates and versions

hal-03140584 , version 1 (13-02-2021)

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Lélia Blin, Swan Dubois, Laurent Feuilloley. Silent MST Approximation for Tiny Memory. SSS 2020 : The 22th International Symposium on Stabilization, Safety, and Security of Distributed Systems, Nov 2020, Austin, TX / Virtual, United States. pp.118-132, ⟨10.1007/978-3-030-64348-5_10⟩. ⟨hal-03140584⟩
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