Optimizing View Change for Byzantine Fault Tolerance in Parallel Consensus

TL;DR

This paper introduces a View Change Optimization (VCO) model using mixed integer programming to enhance leader selection in parallel Byzantine Fault Tolerance protocols, significantly improving performance during view changes in blockchain systems.

Contribution

The paper presents a novel VCO model and an efficient iterative backup leader selection algorithm that optimize view change processes in parallel BFT protocols, outperforming existing methods.

Findings

VCO improves performance during view changes.

VCO outperforms existing methods in cloud experiments.

Effective for large network sizes.

Abstract

The parallel Byzantine Fault Tolerant (BFT) protocol is viewed as a promising solution to address the consensus scalability issue of the permissioned blockchain. One of the main challenges in parallel BFT is the view change process that happens when the leader node fails, which can lead to performance bottlenecks. Existing parallel BFT protocols typically rely on passive view change mechanisms with blind leader rotation. Such approaches frequently select unavailable or slow nodes as leaders, resulting in degraded performance. To address these challenges, we propose a View Change Optimization (VCO) model based on mixed integer programming that optimizes leader selection and follower reassignment across parallel committees by considering communication delays and failure scenarios. We applied a decomposition method with efficient subproblems and improved benders cuts to solve the VCO model. Leveraging the results of improved decomposition solution method, we propose an efficient iterative backup leader selection algorithm as views proceed. By performing experiments in Microsoft Azure cloud environments, we demonstrate that the VCO-driven parallel BFT outperforms existing configuration methods under both normal operation and faulty condition. The results show that the VCO model is effective as network size increases, making it a suitable solution for high-performance parallel BFT systems.