Refined Mean Field Analysis of the Gossip Shuffle Protocol -- extended version --

TL;DR

This paper evaluates a refined mean field approach for analyzing the gossip shuffle protocol, demonstrating improved accuracy in performance estimation for medium-sized networks and highlighting the importance of capturing coordination effects.

Contribution

It introduces a refined mean field analysis method tailored for the gossip shuffle protocol, enhancing accuracy over traditional approaches.

Findings

Refined mean field provides more accurate performance estimates.

Coordination effects are crucial for correct analysis.

Improved analysis for medium-sized network models.

Abstract

Gossip protocols form the basis of many smart collective adaptive systems. They are a class of fully decentralised, simple but robust protocols for the distribution of information throughout large scale networks with hundreds or thousands of nodes. Mean field analysis methods have made it possible to approximate and analyse performance aspects of such large scale protocols in an efficient way. Taking the gossip shuffle protocol as a benchmark, we evaluate a recently developed refined mean field approach. We illustrate the gain in accuracy this can provide for the analysis of medium size models analysing two key performance measures. We also show that refined mean field analysis requires special attention to correctly capture the coordination aspects of the gossip shuffle protocol.