Zhang, Xinghe (Beijing Jiaotong University), Ma, Lianchuan (Beijing Jiaotong University), Li, Kaicheng (Beijing Jiaotong University), TANG, Tao (Beijing Jiaotong University)

GRaft: Credit-Based Byzantine-Tolerant Consensus for Geo-Redundant Rail Transit Cloud Platforms

Scheduled for presentation during the Video Session "On-Demand Video Presentations" (VP-VP), Saturday, November 22, 2025, 08:00−18:00, On-Demand Platform

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on April 2, 2026

Abstract

Cloud computing, with its centralized management and elastic resource scalability, has become a key enabler for modern rail transit system. However, rail transit signal system demands high reliability and availability, typically requiring distributed architectures and geographic redundancy, conditions that clash with traditional centralized cloud models. To overcome this, we introduce an N-2×2-out-of-2 geo-redundant architecture built on a safe cloud computing platform, paired with GRaft, a consensus algorithm tailored for rail transit geo-redundancy. GRaft employs a credit-based node scoring mechanism that evaluates static configuration metrics, historical consensus participation, and communication latency to optimize leader election and prevent vote splitting. To further secure operation under Byzantine conditions, GRaft integrates an RSA-PSS digital signature scheme for real-time detection of malicious leader behavior and to guarantee consensus correctness. Experimental results show that our method maintains stable election and consensus latencies while significantly enhancing geo-redundant switchover performance, providing support for fault tolerance and continuous operation in cross-region distributed cloud environments.