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arXiv · 1905.08565

Silent MST approximation for tiny memory

Lélia Blin·Swan Dubois·Laurent Feuilloley
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Abstract

In this paper we show that approximation can help reduce the space used for self-stabilization. In the classic \emph{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 \emph{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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Lélia Blin, Swan Dubois, Laurent Feuilloley. 2019-05-21. Silent MST approximation for tiny memory. https://arxiv.org/abs/1905.08565

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