Quasirandom Rumor Spreading: An Experimental Analysis

TL;DR

This paper experimentally compares classical and quasirandom rumor spreading protocols, demonstrating that quasirandom methods are faster, more consistent, and maintain similar asymptotic guarantees across various network models.

Contribution

It provides the first experimental analysis showing the practical advantages of quasirandom rumor spreading over classical methods across different network scenarios.

Findings

Quasirandom rumor spreading is generally faster than classical.

The quasirandom process has more concentrated runtime around the mean.

Performance advantages persist even in lossy and asynchronous models.

Abstract

We empirically analyze two versions of the well-known "randomized rumor spreading" protocol to disseminate a piece of information in networks. In the classical model, in each round each informed node informs a random neighbor. In the recently proposed quasirandom variant, each node has a (cyclic) list of its neighbors. Once informed, it starts at a random position of the list, but from then on informs its neighbors in the order of the list. While for sparse random graphs a better performance of the quasirandom model could be proven, all other results show that, independent of the structure of the lists, the same asymptotic performance guarantees hold as for the classical model. In this work, we compare the two models experimentally. This not only shows that the quasirandom model generally is faster, but also that the runtime is more concentrated around the mean. This is surprising given that much fewer random bits are used in the quasirandom process. These advantages are also observed in a lossy communication model, where each transmission does not reach its target with a certain probability, and in an asynchronous model, where nodes send at random times drawn from an exponential distribution. We also show that typically the particular structure of the lists has little influence on the efficiency.