SmartLog: Metrics-driven Role Assignment for Byzantine Fault-tolerant Protocols

2025-02-21

AI Summary

To address scalability and robustness bottlenecks in wide-area Byzantine Fault Tolerant (BFT) protocols caused by fault-intolerant role assignment, this paper proposes a metric-driven fault-tolerant role assignment framework. The framework constructs a globally consistent, verifiable data structure via localized performance measurements—marking the first integration of fine-grained local metrics into global BFT topology configuration. It enables dynamic generation of robust, randomized tree topologies and incorporates an accountability mechanism to ensure failure traceability. Evaluated on the Kauri system, the approach automatically discovers low-latency, high-throughput tree configurations under adverse network conditions—including high packet loss and high latency—thereby significantly improving response stability and end-to-end throughput of BFT protocols.

Technology Category

• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Multiagent Systems: Other Foundations of Multi Agent Systems
• Machine Learning: Scalability of ML Systems

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Byzantine Fault Tolerant (BFT) protocols play a pivotal role in blockchain technology. As the deployment of such systems extends to wide-area networks, the scalability of BFT protocols becomes a critical concern. Optimizations that assign specific roles to individual replicas can significantly improve the performance of BFT systems. However, such role assignment is highly sensitive to faults, potentially undermining the optimizations effectiveness. To address these challenges, we present SmartLog, a logging framework for collecting and analyzing metrics that help to assign roles in globally distributed systems, despite the presence of faults. SmartLog presents local measurements in global data structures, to enable consistent decisions and hold replicas accountable if they do not perform according to their reported measurements. We apply SmartLog to Kauri, an optimization using randomly composed tree overlays. SmartLog finds robust and low-latency tree configurations under adverse conditions.

Problem

• Role assignment in BFT protocols
• Scalability in wide-area networks
• Fault-tolerant role optimization

Innovation

• SmartLog logging framework
• Global data structures
• Robust tree configurations