Distributed Computation in Node-Capacitated Networks

TL;DR

This paper introduces the Node-Capacitated Clique model to analyze how limited communication capacity at nodes affects the complexity of distributed graph algorithms, providing new algorithms for key problems.

Contribution

It presents the Node-Capacitated Clique model and develops distributed algorithms for MST, BFS, MIS, matching, and coloring under node communication constraints.

Findings

MST can be solved in polylogarithmic time with limited node communication.

Algorithms' runtime depends linearly on graph arboricity for many graph families.

Efficient distributed algorithms are feasible even with O(log n) communication limits per node.

Abstract

In this paper, we study distributed graph algorithms in networks in which the nodes have a limited communication capacity. Many distributed systems are built on top of an underlying networking infrastructure, for example by using a virtual communication topology known as an overlay network. Although this underlying network might allow each node to directly communicate with a large number of other nodes, the amount of communication that a node can perform in a fixed amount of time is typically much more limited. We introduce the Node-Capacitated Clique model as an abstract communication model, which allows us to study the effect of nodes having limited communication capacity on the complexity of distributed graph computations. In this model, the $n$ nodes of a network are connected as a clique and communicate in synchronous rounds. In each round, every node can exchange messages of $O(\log n)$ bits with at most $O(\log n)$ other nodes. When solving a graph problem, the input graph $G$ is defined on the same set of $n$ nodes, where each node knows which other nodes are its neighbors in $G$. To initiate research on the Node-Capacitated Clique model, we present distributed algorithms for the Minimum Spanning Tree (MST), BFS Tree, Maximal Independent Set, Maximal Matching, and Vertex Coloring problems. We show that even with only $O(\log n)$ concurrent interactions per node, the MST problem can still be solved in polylogarithmic time. In all other cases, the runtime of our algorithms depends linearly on the arboricity of $G$, which is a constant for many important graph families such as planar graphs.