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Optimal Space Lower Bound for Deterministic Self-Stabilizing Leader Election Algorithms
- Source :
- Discrete Mathematics & Theoretical Computer Science, vol. 25:1, Distributed Computing and Networking (March 1, 2023) dmtcs:9335
- Publication Year :
- 2019
-
Abstract
- Given a boolean predicate $\Pi$ on labeled networks (e.g., proper coloring, leader election, etc.), a self-stabilizing algorithm for $\Pi$ is a distributed algorithm that can start from any initial configuration of the network (i.e., every node has an arbitrary value assigned to each of its variables), and eventually converge to a configuration satisfying $\Pi$. It is known that leader election does not have a deterministic self-stabilizing algorithm using a constant-size register at each node, i.e., for some networks, some of their nodes must have registers whose sizes grow with the size $n$ of the networks. On the other hand, it is also known that leader election can be solved by a deterministic self-stabilizing algorithm using registers of $O(\log \log n)$ bits per node in any $n$-node bounded-degree network. We show that this latter space complexity is optimal. Specifically, we prove that every deterministic self-stabilizing algorithm solving leader election must use $\Omega(\log \log n)$-bit per node registers in some $n$-node networks. In addition, we show that our lower bounds go beyond leader election, and apply to all problems that cannot be solved by anonymous algorithms.<br />Comment: Final journal version for DMTCS (Conference version at OPODIS 2021)
Details
- Database :
- arXiv
- Journal :
- Discrete Mathematics & Theoretical Computer Science, vol. 25:1, Distributed Computing and Networking (March 1, 2023) dmtcs:9335
- Publication Type :
- Report
- Accession number :
- edsarx.1905.08563
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.46298/dmtcs.9335