Stochastic Performance Modeling for Practical Byzantine Fault Tolerance Consensus in Blockchain

TL;DR

This paper develops a stochastic performance model for PBFT consensus in blockchain, using advanced queueing theory and Markov processes to evaluate stability and performance measures, verified through numerical examples.

Contribution

It introduces a novel queueing-based stochastic model for PBFT, enabling stability analysis and performance evaluation with a matrix-geometric solution.

Findings

Derived necessary and sufficient stability conditions.

Computed four key performance measures.

Validated results with numerical examples.

Abstract

The practical Byzantine fault tolerant (PBFT) consensus mechanism is one of the most basic consensus algorithms (or protocols) in blockchain technologies, thus its performance evaluation is an interesting and challenging topic due to a higher complexity of its consensus work in the peer-to-peer network. This paper describes a simple stochastic performance model of the PBFT consensus mechanism, which is refined as not only a queueing system with complicated service times but also a level-independent quasi-birth-and-death (QBD) process. From the level-independent QBD process, we apply the matrix-geometric solution to obtain a necessary and sufficient condition under which the PBFT consensus system is stable, and to be able to numerically compute the stationary probability vector of the QBD process. Thus we provide four useful performance measures of the PBFT consensus mechanism, and can numerically calculate the four performance measures. Finally, we use some numerical examples to verify the validity of our theoretical results, and show how the four performance measures are influenced by some key parameters of the PBFT consensus. By means of the theory of multi-dimensional Markov processes, we are optimistic that the methodology and results given in this paper are applicable in a wide range research of PBFT consensus mechanism and even other types of consensus mechanisms.