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.

翻译：并行拜占庭容错协议被视为解决许可区块链共识可扩展性问题的有前景方案。并行BFT中的一个主要挑战是当领导者节点失效时发生的视图变更过程，这可能导致性能瓶颈。现有的并行BFT协议通常依赖被动视图变更机制与盲目的领导者轮换。这种方法经常选择不可用或响应缓慢的节点作为领导者，导致性能下降。为应对这些挑战，我们提出一种基于混合整数规划的视图变更优化模型，该模型通过考虑通信延迟与故障场景，优化跨并行委员会的领导者选择与跟随者重分配。我们采用具有高效子问题的分解方法并改进Benders割平面来求解VCO模型。借助改进分解求解方法的结果，我们提出一种高效的迭代式备份领导者选择算法以推进视图变更。通过在Microsoft Azure云环境中进行实验，我们证明在正常运行与故障条件下，VCO驱动的并行BFT均优于现有配置方法。结果表明，VCO模型在网络规模扩大时依然有效，使其成为高性能并行BFT系统的适用解决方案。