基于领导者与无领导者BFT协议的自适应切换

中文摘要 AI 辅助

拜占庭容错（BFT）协议以在分布式系统中提供操作一致性和弹性而著称。然而，不断变化的网络条件（通常由网络固有的动态性或对抗性影响所驱动）使得在任何时候都依赖静态协议并非最优选择。现有的BFT协议自适应解决方案仅在基于领导者的协议之间切换，并通过单独的共识轮次协调每次切换，这使得它们在处理严重异步或自适应对手针对网络领导者的情况时效果不佳。我们提出了BFTide，一种协议自适应架构，使BFT系统能够在网络条件变化时智能且快速地切换到合适的协议。BFTide集成了一种新颖的协议切换层，将协议转换逻辑嵌入到正在进行的BFT操作中，从而在部分同步的基于领导者的协议和异步的无领导者协议之间实现安全且低开销的转换。它进一步结合了离线训练的强化学习策略，允许节点在运行时根据观察到的系统指标提出协议。实验结果表明，与静态BFT协议和最新的BFT协议自适应方案BFTBrain（NSDI'25）相比，BFTide在不利网络条件下降低了交易延迟，同时保持了相当的吞吐量。切换层在空闲时给中位延迟增加了10-21%的适度开销，并且每次切换不需要单独的共识轮次。

英文摘要

Byzantine fault-tolerant (BFT) protocols are known for providing operational consistency and resilience in distributed systems. However, evolving network conditions, often driven by the network's inherent dynamism or adversarial influence, make it suboptimal to rely on a static protocol at all times. Existing BFT protocol adaptation solutions switch only among leader-based protocols and coordinate each switch through a separate consensus round, leaving them ineffective at handling severe asynchrony or situations in which an adaptive adversary targets the network's leader. We propose BFTide, a protocol adaptation architecture that enables a BFT system to intelligently and swiftly switch to a suitable protocol as network conditions shift. BFTide integrates a novel protocol switching layer that embeds protocol transition logic into the ongoing BFT operation, enabling safe and low-overhead transitions between partially synchronous leader-based protocols and asynchronous leaderless protocols. It further incorporates an offline-trained reinforcement learning policy that allows nodes to propose protocols at runtime based on observed system metrics. Experimental results show that BFTide reduces transaction latency under adverse network conditions compared with static BFT protocols and the state-of-the-art BFT protocol adaptation scheme BFTBrain (NSDI'25), while maintaining comparable throughput. The switching layer adds a modest 10-21% overhead to median latency when idle and requires no separate consensus round per switch.

发表机构

• University of Kentucky（肯塔基大学）
• Washington University in St. Louis（华盛顿大学圣路易斯分校）
• Virginia Tech（弗吉尼亚理工大学）

机构由 AI 辅助整理，请以论文原文为准。