发表机构
Chalmers University of Technology; IMDEA Software Institute（查尔姆斯理工大学; IMDEA软件研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文实现并基准测试了 Bracha、AFRT 和 Coded MBRB 三种拜占庭可靠广播算法，通过大规模实验揭示其性能权衡，并提供可扩展的开源测试工件。
AI 中文摘要
拜占庭可靠广播（BRB）和容忍消息敌手的拜占庭可靠广播（MBRB）是容错分布式系统中用于可靠传播的抽象。然而，它们的运行行为不仅受规范和渐近通信界限的影响，还受序列化、密码学、缓冲、编排、部署环境和故障注入语义的影响。本文实现并评估了 Bracha [Information and Computation, 1987]、Albouy 等人的 AFRT [TCS, 2023] 以及 Albouy 等人的 Coded MBRB [OPODIS, 2024]。我们在一个共享的 Go 代码库中实现了这些算法，并配备了通用的编排、仪器、基于解析器的规范检查、故障注入以及一个开源的可复现性工件。评估使用了 Shadow 网络模拟器中的单次广播、原生性能分析、Google Cloud Platform 部署以及一个分布式 FABRIC 测试平台，涵盖了多达 30 个节点的受控实验、多达 40 MB 的有效载荷、92,190 次运行以及 2,361,600 个经解析器检查的条目。结果表明，在评估的云环境中，对于较大的有效载荷，Coded MBRB 减少了传输数据并改善了延迟，但将成本转移到了密码学和编码计算上。Bracha 和 AFRT 在较小有效载荷下产生较低的 CPU 成本，但随着有效载荷的增长，其全有效载荷传播增加了处理、分配和网络成本。在测试的配置中，解析器未发现重复投递、冲突投递或投递与发送方有效载荷不同的值。本文贡献了实现层面的证据和一个可扩展的工件，用于将 BRB 和 MBRB 作为可执行的分布式系统组件进行基准测试，揭示了仅靠算法描述所隐藏的瓶颈和操作权衡。
英文摘要
Byzantine Reliable Broadcast (BRB) and Message-Adversary-Tolerant Byzantine Reliable Broadcast (MBRB) are reliable-dissemination abstractions for fault-tolerant distributed systems. Yet their operational behavior is shaped not only by specifications and asymptotic communication bounds, but also by serialization, cryptography, buffering, orchestration, deployment environment, and fault-injection semantics. This paper implements and evaluates Bracha [Information and Computation, 1987], AFRT by Albouy et al. [TCS, 2023], and Coded MBRB by Albouy et al. [OPODIS, 2024]. We implement the algorithms in a shared Go codebase with common orchestration, instrumentation, parser-based specification checks, fault injection, and an open-source reproducibility artifact. The evaluation uses single-shot broadcasts in the Shadow network simulator, native profiling, a Google Cloud Platform deployment, and a distributed FABRIC testbed, covering controlled experiments up to 30 nodes, payloads up to 40 MB, 92,190 runs, and 2,361,600 parser-checked entries. The results show that Coded MBRB reduces transmitted data and improves latency in the evaluated cloud setting for larger payloads, but shifts cost to cryptographic and coding computation. Bracha and AFRT incur lower CPU costs at smaller payloads, but their full-payload dissemination increases processing, allocation, and network costs as payloads grow. Across the tested configurations, the parser found no duplicate deliveries, conflicting deliveries, or deliveries of values different from the sender's payload. The paper contributes implementation-level evidence and an extensible artifact for benchmarking BRB and MBRB as executable distributed-system components, exposing bottlenecks and operational trade-offs that are hidden by algorithmic descriptions alone.
Commentsto be published in SSS 2026
