Distributed Computing in Dynamic Networks: Towards a Framework for Automated Analysis of Algorithms

Besides the complexity in time or in number of messages, a com mon 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 d ynamics. In particular, we ask how a given property on the evolution of the network can be rigorously pr oven as necessary or sufficient for a given algorithm. The main contribution of this paper is to pr ose the combination of two existing tools in this direction:local computationsby means ofgraph relabelings , andevolving graphs . Such a combination makes it possible to express fine-grained prop erties on the network dynamics, then examine what impact those properties have on the execution a t 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 possibilit y o compare distributed algorithms on the basis of their topological requirements, (ii) a formal hier archy of dynamic networks based on these requirements, and (iii) the potential for mechanization in duced by our framework, which we believe opens a door towards automated analysis and decision suppor t in dynamic networks.