发表机构
University of Toronto（多伦多大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对分布式存储线性化读取算法在权衡空间中无法兼顾延迟与网络容忍度的问题，提出Pairwise Quorums及可运行时重配置的Ditto，覆盖并优于现有算法。
AI 中文摘要
线性化（Linearizability）给开发者一种错觉，即对分布式数据存储的操作是在单台机器上顺序执行的。提供这种保证代价高昂，因此提出了许多专门的读取算法，以在不牺牲线性化的前提下显著加快读取速度，这一点很重要，因为大多数工作负载以读取为主，数量级上远超写入。然而，这些算法中没有一种对所有部署都是最佳选择。我们通过一个框架来展示原因，该框架将这些算法分类到一个广义空间中，并对其延迟作为该广义化的函数进行了数学分析。该分析揭示了两个基本权衡：一个是读取与写入延迟之间的权衡，另一个是读取与写入对网络波动容忍度之间的权衡，这两者共同使得最佳算法取决于随时间变化的网络和工作负载条件。分析还揭示了一个空白：存在一种方法，可以在读取与并发写入重叠时最小化读取所经历的延迟，且对写入无任何代价，但现有算法只能将其应用于权衡空间的一个子集，迫使任何需要完整空间的部署只能接受严格较差的解决方案。我们用一种新算法Pairwise Quorums填补了这一空白，并构建了第二种算法Ditto，它能在运行时根据条件变化在空间中重新配置。两者结合在我们的模型下涵盖了所有现有算法：针对每一种算法，该组合要么严格更优，要么在存在真正权衡的地方重新配置以匹配之。
英文摘要
Linearizability gives developers the illusion that operations on a distributed datastore execute sequentially on a single machine. Providing it is expensive, so numerous specialized reads algo- rithms have been proposed to make reads significantly faster without sacrificing linearizability, which matters because most workloads are read-dominant by orders of magnitude. Yet, none of these algorithms is the best choice for every deployment. We show why with a framework that classifies these algorithms into a generalized space together with a mathematical analysis of their latencies as a function of this generalization. The analysis exposes two fundamental tradeoffs, one between read and write latency and one between read and write tolerance to network variance, which together leave the best algorithm dependent on network and workload conditions that vary over time. It also exposes a gap: there is a way to minimize the delay a read incurs when it overlaps a concurrent write, at no cost to writes, yet exist- ing algorithms can apply it only for a subset of the tradeoff space, forcing any deployment that needs the full space to settle for a strictly worse solution. We close this gap with a new algorithm, Pairwise Quorums, and build a second, Ditto, that reconfigures across the space at runtime as conditions change. Together they subsume every existing algorithm under our model: against each, the combination is either strictly better, or reconfigures to match it where a genuine tradeoff applies.
