Introduced by Emek and Wattenhofer (PODC 2013), the \emph{stone age (SA)} model provides an abstraction for network algorithms distributed over randomized finite state machines. This model, designed to resemble the dynamics of biological processes in cellular networks, assumes a weak communication scheme that is built upon the nodes' ability to sense their vicinity in an asynchronous manner. Recent works demonstrate that the weak computation and communication capabilities of the SA model suffice for efficient solutions to some core tasks in distributed computing, but they do so under the (somewhat less realistic) assumption of fault free computations. In this paper, we initiate the study of \emph{self-stabilizing} SA algorithms that are guaranteed to recover from any combination of transient faults. Specifically, we develop efficient self-stabilizing SA algorithms for the \emph{leader election} and \emph{maximal independent set} tasks in bounded diameter graphs subject to an asynchronous scheduler. These algorithms rely on a novel efficient self-stabilizing \emph{asynchronous unison (AU)} algorithm that is "thin" in terms of its state space: the number of states used by the AU algorithm is linear in the graph's diameter bound, irrespective of the number of nodes.
翻译:由 Emek 和 Wattenhofer ( PoDC 2013 ) 引入的 emph{stone age (SA) 模型为在随机化的限定国家机器上分布的网络算法提供了抽象的抽象。 这个模型旨在类似于细胞网络生物过程的动态, 假设一个薄弱的通信计划, 建立在节点以非同步的方式感知其周围的能力之上。 最近的工作表明, SA 模型的薄弱计算和通信能力足以有效解决分布式计算中的某些核心任务, 但是在( 不太现实的) 错误自由计算假设下, 这些算法可以提供。 在本文中, 我们开始研究 SA 的算法, 保证从任何易变性缺陷的组合中恢复过来。 具体地说, 我们为\emph{ 领头选举} 和\ emph{ 最大独立设置 的任务, 能够有效地自我稳定 的SA 运算法 。 这些算法依赖于一种新型的高效自我稳定自我调节的计算法, 其直径直径的直径直径直径直径直径直径直径直径直径直径直径直径直径直径直径的算法( AAUs) 是非直径直径直径直径直的状态。 。 的算法是非AUIAU 。