Characterizing Topological Assumptions of Distributed Algorithms in Dynamic Networks

TL;DR

This paper introduces a framework combining local graph relabelings and evolving graphs to analyze how network dynamics influence distributed algorithms, enabling comparison and formal hierarchy of their topological assumptions.

Contribution

It proposes a novel combination of tools for analyzing the impact of network evolution on distributed algorithms, facilitating formal comparison and potential automation.

Findings

Framework allows expressing fine-grained network properties

Enables comparison of algorithms based on topological requirements

Lays groundwork for automated analysis of dynamic networks

Abstract

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.