Brief Announcement: Optimal Time and Space Leader Election in Population Protocols

P. Berenbrink, George Giakkoupis, Peter Kling
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引用次数: 3

Abstract

Population protocols are a model of distributed computing, where n agents with limited computational power and memory perform randomly scheduled pairwise interactions. Recently, a significant amount of work has been devoted to the study of the time and space complexity of leader election in this model. It is known that Ω (log log n) states per agent are needed to elect a leader in fewer than [EQUATION] expected interactions (Alistarh et al.; SODA'17) and that Ω (n log n) expected interactions are required regardless of the number of states (Sudo and Masuzawa; 2020). On the positive side, Gasieniec and Stachowiak (SODA'18) gave the first protocol that uses an optimal Θ(log log n) number or states and elects a leader in O(n log2 n) expected interactions. This running time was subsequently improved to O(n log n log log n) (Gasieniec et al.; SPAA'19). We provide the first leader election population protocol that is both time and space optimal, electing a leader in O(n log n) expected interactions and using Θ(log log n) states per agent. A novel component is a simple protocol that efficiently selects a small set of agents of polylog n size, given O(n∈) initially selected agents. Unlike existing approaches, which monotonically shrink this initially selected set, we first grow it in a controlled way to a specific size before shrinking it again.
简要公告:人口协议中的最优时空领袖选举
人口协议是一种分布式计算模型,其中n个具有有限计算能力和内存的代理执行随机安排的成对交互。近年来,人们对该模型中领导人选举的时空复杂性进行了大量的研究。众所周知,每个代理需要Ω (log log n)个状态才能在少于[EQUATION]预期的交互中选出领导者(Alistarh et al.;SODA'17)和Ω (n log n)期望相互作用是必需的,而不管状态的数量(Sudo和Masuzawa;2020)。从积极的方面来看,Gasieniec和Stachowiak (SODA’18)给出了第一个使用最优Θ(log log n)个数或状态的协议,并在O(n log2 n)个预期交互中选择领导者。该运行时间随后被改进为O(n log n log log n) (Gasieniec等人;SPAA 19)。我们提供了时间和空间最优的第一个领导者选举人口协议,在O(n log n)次预期交互中选举领导者,每个代理使用Θ(log log n)个状态。一种新颖的组件是一种简单的协议,给定O(n∈)个初始选择的代理,它可以有效地选择一个大小为polylogn的小代理集。不像现有的方法,单调地缩小这个初始选择的集合,我们首先以一种可控的方式将它增长到一个特定的大小,然后再缩小它。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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