From Bracha to Coded MBRB: Benchmarking Byzantine Reliable Broadcast Implementations

📅 2026-09-07
📈 Citations: 0
Influential: 0
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🤖 AI Summary
该论文通过实现和评估三种拜占庭可靠广播算法,揭示了在不同负载下的性能瓶颈和操作权衡,使用了Go代码库、多种测试环境和故障注入等方法。
📝 Abstract
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.
Problem

Research questions and friction points this paper is trying to address.

Byzantine Reliable Broadcast
Message-Adversary-Tolerant Byzantine Reliable Broadcast
performance evaluation
distributed systems
fault tolerance
Innovation

Methods, ideas, or system contributions that make the work stand out.

Coded MBRB
reliable dissemination
fault-tolerant distributed systems
implementation benchmarking
Y
Yenan Wang
Chalmers University of Technology, Gothenburg, Sweden
J
Jesper Kullberg
Chalmers University of Technology, Gothenburg, Sweden
F
Fabian Paglianno Persson
Chalmers University of Technology, Gothenburg, Sweden
Elad Michael Schiller
Elad Michael Schiller
Professor of Computer Science and Engineering, Chalmers University of Technology
Fault-tolerant Distributed ComputingSelf-stabilization
T
Timothé Albouy
IMDEA Software Institute, Madrid, Spain