A Learning Based Scheme for Fair Timeliness in Sparse Gossip Networks

TL;DR

This paper proposes a learning-based approach to optimize update rates in sparse gossip networks, aiming to improve fairness in information timeliness across nodes with irregular connectivity.

Contribution

It introduces a novel formulation of rate allocation as a continuum-armed bandit problem and applies Gaussian process Bayesian optimization for fair timeliness in sparse networks.

Findings

Effective rate allocation improves worst-case node timeliness.

Bayesian optimization balances exploration and exploitation.

Method adapts to irregular network structures.

Abstract

We consider a gossip network, consisting of $n$ nodes, which tracks the information at a source. The source updates its information with a Poisson arrival process and also sends updates to the nodes in the network. The nodes themselves can exchange information among themselves to become as timely as possible. However, the network structure is sparse and irregular, i.e., not every node is connected to every other node in the network, rather, the order of connectivity is low, and varies across different nodes. This asymmetry of the network implies that the nodes in the network do not perform equally in terms of timelines. Due to the gossiping nature of the network, some nodes are able to track the source very timely, whereas, some nodes fall behind versions quite often. In this work, we investigate how the rate-constrained source should distribute its update rate across the network to maintain fairness regarding timeliness, i.e., the overall worst case performance of the network can be minimized. Due to the continuous search space for optimum rate allocation, we formulate this problem as a continuum-armed bandit problem and employ Gaussian process based Bayesian optimization to meet a trade-off between exploration and exploitation sequentially.