领导人选举算法说明。初步报告

Dominik Bojko, J. Cichon
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摘要

本文讨论的领导者选举算法取决于两个参数 $p、L$。前一个参数是身份抽取过程中使用的几何分布概率参数,后一个参数是每个节点为保存潜在领导者身份而可以开发的最大内存比特数。我们考虑的是$\{v_{1}, \ldots, v_{n}\}$ 的节点族。在这个算法中,每个节点 $v_{i}$ 从参数为 $p$ 的几何分布中独立生成一个随机数 $x_{i}$,并进一步计算出数字 $y_{i}=\min(x_{i},\ L)$。选择最大数字的节点成为候选领导者。如果正好有一个候选者,这个过程就能成功选出领导者。我们固定一个数 $N$ 和 $\varepsilon > 0$。我们的目标是确定这样的参数$p$和$L$,它们能保证所考虑的算法是准确的,即对于任意节点数$1\leq n\leq N$,它成功的概率至少是$1 -\varepsilon$。这种强烈的要求使我们的考虑有别于其他许多对领导者选举算法的分析,后者通常只关注其渐近正确性和属性。我们的算法可以在单跳或多跳环境中实现。在单跳的情况下,它需要 $\log_{2}L$ 的回合数才能以所需的成功概率选出领导者,而在多跳环境下,它需要 $\mathrm{O}(d\log_{2}L)$ 的回合数,其中 $d$ 表示网络直径的上限。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Note on Leader Election Algorithms. Preliminary Report
In this paper we discuss a leader election algorithm which depends on two parameters $p, L$. The first one is a probability parameter of geometric distribution used during a draw of identity and the latter one is a maximal number of bits of memory that each node can develop in order to save identity of a potential leader. We consider a family $\{v_{1}, \ldots, v_{n}\}$ of nodes. In this algorithm each node $v_{i}$ generates independently a random number $x_{i}$ from a geometric distribution with parameter $p$ and furtherly calculates the number $y_{i}=\min(x_{i},\ L)$. Nodes which choose the biggest number become candidates for a leader. This procedure successfully elects the leader if there is exactly one candidate. We fix a number $N$ and $\varepsilon > 0$. Our goal is to determine such parameters $p$ and $L$ which guarantee that the considered algorithm will be accurate i.e. it will be successful with a probability at least $1 -\varepsilon$, for an arbitrary number of nodes $1\leq n\leq N$. This strong requirement distinguishes our considerations from many other analysis of leader election algorithms, which often focus only on theirs asymptotic correctness and properties. Our algorithm can be implemented either in single hop or in multi hop environment. In the single hop case it needs $\log_{2}L$ rounds to select a leader with required probability of success and may be adapted to the multi hop environment, where it needs $\mathrm{O}(d\log_{2}L)$ rounds, where $d$ denotes an upper bound for a diameter of the network.
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