BFTide improves distributed system reliability by adaptive protocol switching
Adaptive Switching Between Leader-Based and Leaderless BFT Protocols
Distributed, Parallel, and Cluster Computing
Summary
Distributed computer systems need to keep working correctly even when some parts fail or act maliciously. Normally, these systems use specific methods that work best under certain network conditions but struggle when conditions change. The authors present BFTide, a new approach that can switch smoothly between different methods depending on how the network is behaving, without extra slow steps. They also use machine learning to help decide which method to use during operation. Their tests show BFTide can make systems faster and more reliable in challenging network situations.
What this means in practice
- •For cloud infrastructure teams: Enable distributed cloud systems to dynamically switch consensus methods for better performance during network slowdowns or attacks.
- •For blockchain developers: Build blockchain networks that adapt consensus protocols automatically to maintain speed and security under changing network conditions.
Authors
Sudip Bhujel, Yue Li, Ning Zhang, Y. Thomas Hou, Wenjing Lou, Yang Xiao
Abstract
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.