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, "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.

中文翻译：

---

薄型自稳定异步Unison算法及其在容错生物网络中的应用

由Emek和Wattenhofer（PODC 2013）引入的\ emph {石器时代（SA）}模型为分布在随机有限状态机上的网络算法提供了一种抽象。该模型旨在模拟蜂窝网络中生物过程的动态，它采用弱通信方案，该方案建立在节点以异步方式感知其附近的能力的基础上。最近的工作表明，SA模型的弱计算和通信能力足以满足分布式计算中某些核心任务的有效解决方案，但在无故障计算的假设（有些不太现实）下，它们确实可以实现。在本文中，我们开始研究\ emph {self-stabilizing} SA算法，该算法可确保从瞬态故障的任何组合中恢复。具体来说，我们针对受异步调度程序约束的有界直径图中的\ emph {领导者选举}和\ emph {最大独立集}任务，开发了高效的自稳定SA算法。这些算法依靠一种新颖有效的自稳定\ emph {asynchronous unison（AU）}算法，在其状态空间方面“很细”：AU算法使用的状态数在图形的直径范围内是线性的，而与节点数。