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Self-Stabilizing Leader Election in Polynomial Steps

Abstract : We propose a silent self-stabilizing leader election algorithm for bidirectional connected identified networks of arbitrary topology. This algorithm is written in the locally shared memory model. It assumes the distributed unfair daemon, the most general scheduling hypothesis of the model. Our algorithm requires no global knowledge on the network (such as an upper bound on the diameter or the number of processes, for example). We show that its stabilization time is in $\Theta(n^3)$ steps in the worst case, where $n$ is the number of processes. Its memory requirement is asymptotically optimal, i.e., $\Theta(\log n)$ bits per processes. Its round complexity is of the same order of magnitude --- i.e., $\Theta(n)$ rounds --- as the best existing algorithms designed with similar settings. To the best of our knowledge, this is the first self-stabilizing leader election algorithm for arbitrary identified networks that is proven to achieve a stabilization time polynomial in steps. By contrast, we show that the previous best existing algorithms designed with similar settings stabilize in a non polynomial number of steps in the worst case.
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Submitted on : Monday, January 7, 2019 - 11:03:32 AM
Last modification on : Thursday, February 3, 2022 - 10:04:04 AM
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Karine Altisen, Alain Cournier, Stéphane Devismes, Anaïs Durand, Franck Petit. Self-Stabilizing Leader Election in Polynomial Steps. Information and Computation, Elsevier, 2017, 254 (3), pp.330-366. ⟨10.1016/j.ic.2016.09.002⟩. ⟨hal-01347471⟩



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