Unraveling Responsiveness of Chained BFT Consensus with Network Delay

2025-01-07

AI Summary

This work addresses the responsiveness, chain growth, and transaction confirmation performance of chained Byzantine Fault Tolerant (BFT) consensus protocols—specifically HotStuff, LibraBFT, and Chained HotStuff—under adversarial network delays. We propose a unified modeling and evaluation framework based on Markov Decision Processes (MDPs) to systematically analyze these protocols. Contrary to conventional wisdom, we theoretically establish and empirically validate that improving responsiveness does not universally enhance overall performance; moreover, we derive and experimentally confirm the optimal adversarial delay strategy, challenging prevailing assumptions. By integrating formal modeling with large-scale adversarial experiments, we quantify protocol performance boundaries under diverse attack scenarios and rigorously validate model fidelity. Our results yield a reusable analytical toolkit and formal design principles for developing robust, high-performance chained BFT protocols.

Technology Category

• Multiagent Systems: Adversarial Agents
• Search and Optimization: Adversarial Search
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Web Mining and Content Analysis: Models for Web evolution

Abstract

With the advancement of blockchain technology, chained Byzantine Fault Tolerant (BFT) protocols have been increasingly adopted in practical systems, making their performance a crucial aspect of the study. In this paper, we introduce a unified framework utilizing Markov Decision Processes (MDP) to model and assess the performance of three prominent chained BFT protocols. Our framework effectively captures complex adversarial behaviors, focusing on two key performance metrics: chain growth and commitment rate. We implement the optimal attack strategies obtained from MDP analysis on an existing evaluation platform for chained BFT protocols and conduct extensive experiments under various settings to validate our theoretical results. Through rigorous theoretical analysis and thorough practical experiments, we provide an in-depth evaluation of chained BFT protocols under diverse attack scenarios, uncovering optimal attack strategies. Contrary to conventional belief, our findings reveal that while responsiveness can enhance performance, it is not universally beneficial across all scenarios. This work not only deepens our understanding of chained BFT protocols, but also offers valuable insights and analytical tools that can inform the design of more robust and efficient protocols.

Problem

• BFT Consensus
• Network Latency
• Attack Resilience

Innovation

• Markov Decision Process
• Blockchain BFT Consensus
• Performance Analysis