Convergence Time Distributions for Max-Consensus over Unreliable Networks

TL;DR

This paper introduces LiFE-CD, an algorithm that exactly computes the full convergence time distribution for max-consensus in unreliable networks, enabling reliable deadline-aware protocol design.

Contribution

LiFE-CD provides deterministic, exact convergence time distributions for max-consensus over unreliable networks, surpassing asymptotic bounds and simulation-based methods.

Findings

LiFE-CD yields exact results for acyclic networks.

It provides tight upper bounds for cyclic networks.

Numerical results confirm analytical accuracy and efficiency.

Abstract

This paper proposes the LiFE-CD algorithm for convergence time analysis of the max-consensus algorithm in multi-agent systems under Bernoulli-distributed link failures. Unlike existing approaches, which either assume ideal communication or provide asymptotic upper bounds on the expected convergence time, LiFE-CD deterministically computes the full probability distribution of the convergence time from network topology and individual link failure probabilities, without simulation. The full probability distribution enables deadline-aware protocol design with specified reliability guarantees. Based on geometrically distributed link delays, the proposed algorithm iteratively reduces the given network topology considering both unicast and broadcast transmissions. LiFE-CD yields exact results for acyclic networks and, for cyclic networks, tight upper bounds on the convergence time via shortest-path spanning tree construction. Numerical results confirm analytical exactness for acyclic networks, validate tightness for cyclic networks, and demonstrate improvement over existing approaches. Our complexity analysis shows reduced computational cost compared to Monte Carlo simulations, while eliminating stochastic variability and enhancing reproducibility. All results extend directly to min-consensus by structural equivalence.