动态网络中分布式算法拓扑假设的刻画
Characterizing Topological Assumptions of Distributed Algorithms in Dynamic Networks
摘要：
除了时间或消息数量的复杂性之外，分析分布式算法的一种常见方法是查看它们对底层网络的假设。我们从网络动力学的角度来研究这个问题。特别是，我们要问的是，如何严格证明网络演化的给定属性对于给定算法是必要的或充分的。本文的主要贡献是提出了在这个方向上的两个现有的工具相结合：本地计算的图形relabelings，和不断发展的图形。这样的组合可以表达网络动态的细粒度属性，然后在精确的、相互交织的级别上检查这些属性对执行的影响。我们通过对三个简单算法的分析来说明这个框架的使用，然后讨论这项工作的一般含义，其中包括（i）根据拓扑要求比较分布式算法的可能性，（ii）基于这些要求的动态网络的正式层次结构，以及（iii）我们的框架引起的机械化的潜力，我们相信这为动态网络中的自动分析和决策支持打开了一扇大门。
英文摘要：
Besides the complexity in time or in number of messages, a common approach for analyzing distributed algorithms is to look at the assumptions they make on the underlying network. We investigate this question from the perspective of network dynamics. In particular, we ask how a given property on the evolution of the network can be rigorously proven as necessary or sufficient for a given algorithm. The main contribution of this paper is to propose the combination of two existing tools in this direction: local computations by means of graph relabelings, and evolving graphs. Such a combination makes it possible to express fine-grained properties on the network dynamics, then examine what impact those properties have on the execution at a precise, intertwined, level. We illustrate the use of this framework through the analysis of three simple algorithms, then discuss general implications of this work, which include (i) the possibility to compare distributed algorithms on the basis of their topological requirements, (ii) a formal hierarchy of dynamic networks based on these requirements, and (iii) the potential for mechanization induced by our framework, which we believe opens a door towards automated analysis and decision support in dynamic networks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
