Btissam Er-Rahmadi | Searcharxiv

arXiv2026

Committee Configuration Optimization for Parallel Byzantine Consensus in a Trusted Execution Environment

Parallel Byzantine Fault Tolerant (BFT) protocols based on committee-based sharding improve scalability but weaken safety since smaller node groups are responsible for consensus. Recent approaches integrate trusted execution environments (TEEs) into parallel BFT frameworks to enhance safety. While the scalability and safety issues are addressed by trusted parallel BFT, existing committee configuration methods often rely on randomized assignment, which can degrade performance. This paper proposes a committee configuration optimization (CCO) model based on mixed integer programming to improve transaction performance for trusted parallel BFT. The model considers communication delays and node failure rates to determine an optimal committee configuration that minimizes transaction latency under both normal operations and scenarios of trusted hardware failures. We integrate CCO into a trusted parallel BFT protocol and evaluate the performance on Microsoft virtual machines. Experimental results demonstrate 15% and 21% improved transaction throughput under normal operations and fallback process, respectively, highlighting the benefits of optimization-driven committee configuration in trusted parallel BFT systems.

cs.DC↗

arXiv2026

Optimizing View Change for Byzantine Fault Tolerance in Parallel Consensus

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.

cs.DC↗