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Silent MST Approximation for Tiny Memory

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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Submitted on : Saturday, February 13, 2021 - 7:42:58 AM
Last modification on : Tuesday, January 11, 2022 - 11:16:06 AM
Long-term archiving on: : Friday, May 14, 2021 - 6:08:03 PM


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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⟩



Les métriques sont temporairement indisponibles