发表机构
EPFL; NVIDIA（洛桑联邦理工学院; 英伟达）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出KCensus框架，将快速路径设计转化为优化问题，合成针对特定设置的延迟最优共识方案，并在AWS上验证平均延迟降低16%。
AI 中文摘要
强一致性的地理复制通常依赖快速路径来减少在无故障或无争用常见情况下的延迟。然而，现有的快速路径方案是临时且受限的：每个方案都对应于由网络拓扑、工作负载和延迟目标所塑造的广阔设计空间中的一个点，因此没有单一方案能在所有设置中表现最佳。本文从新的视角审视快速路径方案，将其视为传播关于提案知识的机制。基于这一观点，我们识别出快速路径方案有效工作的一个关于知识传播的基本条件。随后，我们引入KCensus，一个将该条件转化为优化问题的框架，从而为给定设置合成最优的新的快速路径方案。我们使用KCensus构建了一个地理复制的键值存储，并在AWS区域间进行评估。我们的系统优于竞争协议，平均延迟降低高达16%。
英文摘要
Strongly consistent geo-replication often relies on fast paths to reduce latency in the common case of no failures or contention. Existing fast-path schemes, however, are ad hoc and restrictive: each corresponds to a point in a broad design space shaped by network topology, workload, and latency objective, so no single scheme works best across settings. This paper looks at fast-path schemes from a new perspective, as mechanisms that spread knowledge about proposals. With this view, we identify a fundamental condition on the spread of knowledge for a fast-path scheme to work. We then introduce KCensus, a framework that turns this condition into an optimization problem, synthesizing new fast-path schemes that are optimal for a given setting. We use KCensus to build a geo-replicated key-value store and evaluate it across AWS regions. Our system outperforms competing protocols, with up to 16% lower average latency.
Comments38 pages, 15 figures. Extended version of the paper to appear in the 22nd European Conference on Computer Systems (EuroSys '27), Rabat, Morocco. Includes appendices with full correctness and optimality proofs
