Gossip-based Search in Multipeer Communication Networks

TL;DR

This paper analyzes a gossip-based search algorithm in multipeer networks, modeling its behavior mathematically and validating with simulations to understand its efficiency and the impact of various parameters.

Contribution

It introduces a mathematical framework for analyzing gossip-based search in multipeer networks, considering different node cooperation patterns and validating models with simulations.

Findings

The models accurately predict search performance.

Node cooperation significantly affects search efficiency.

Parameter tuning improves search success rate.

Abstract

We study a gossip-based algorithm for searching data objects in a multipeer communication network. All of the nodes in the network are able to communicate with each other. There exists an initiator node that starts a round of searches by randomly querying one or more of its neighbors for a desired object. The queried nodes can also be activated and look for the object. We examine several behavioural patterns of nodes with respect to their willingness to cooperate in the search. We derive mathematical models for the search process based on the balls and bins model, as well as known approximations for the rumour-spreading problem. All models are validated with simulations. We also evaluate the performance of the algorithm and examine the impact of search parameters.