Leader election, is a fundamental coordination problem in distributed systems. It has been addressed in many ways for different systems. Among these approaches, resilient leader election algorithms are of particular interest due to the ongoing emergence of open, complex distributed systems such as smart cities and the Internet of Things. However, previous algorithms attaining the optimal scaling of O(diameter) stabilization time complexity either assume some prior knowledge of the network or else that very large messages can be sent. In this paper, we present a resilient leader election algorithm with O(diameter) stabilization time, small messages, and no prior knowledge of the network. This algorithm is based on aggregate computing, which provides a layered approach to algorithm development based on composition of resilient algorithmic “building blocks.” With our algorithm, a key design function $g\left(\cdot \right)$ defines important performance attributes: a fast-growing $g\left(\cdot \right)$ will delay discarding of obsolete data, while a slow-growing $g\left(\cdot \right)$ will slow down convergence to a single leader. We prove that the best asymptotic behavior for $g\left(x\right)$ is $(1+\sqrt{2})x+o\left(x\right)$, guaranteeing a near-optimal time complexity of $(2+2\sqrt{2})$ diameter + o(diameter) rounds for stabilization.

领导者选举，是分布式系统中一个基本的协调问题。已经针对不同的系统以多种方式解决了这个问题。在这些方法中，由于开放、复杂的分布式系统（如智能城市和物联网）的不断出现，弹性领导者选举算法特别受关注。然而，以前的算法获得 O(直径) 稳定时间复杂度的最佳缩放或者假设网络的一些先验知识，或者可以发送非常大的消息。在本文中，我们提出了一种具有 O(diameter) 稳定时间、小消息且没有网络先验知识的弹性领导者选举算法。该算法基于聚合计算，它提供了一种基于弹性算法“构建块”组合的算法开发分层方法。使用我们的算法，一个关键的设计函数$G\left(\cdot \right)$定义了重要的性能属性：快速增长的$G\left(\cdot \right)$将延迟丢弃过时的数据，而增长缓慢的$G\left(\cdot \right)$将减慢收敛到单个领导者的速度。我们证明了最佳渐近行为$G\left(X\right)$是$(1+\sqrt{2})X+○\left(X\right)$，保证接近最优的时间复杂度$(2+2\sqrt{2})$直径 + o（直径）轮用于稳定。