Many NoSQL databases support quorum-based protocols, which require a subset of replicas (called a quorum) to respond to each write/read operation. These systems configure the quorum size to tune the operation latency and adopt multiple consistency levels. Some recent works illustrate that using probability models to quantify the chance of reading the last update is important because it could avoid returning stale values under eventual consistency. There are two challenging issues: (1) from inconsistent replicas, how to determine the minimum quorum size (i.e., the lowest access latency) to read the newest data at a specified probability; (2) node failure frequently happens in large-scale systems, how to guarantee the probability-based consistent reads. This article presents Probabilistic Consistency Guarantee (PCG), which is the first dynamic quorum decision and failure-aware quantification model. PCG model respectively quantifies the server-side consistency after the latest write, which reflects the object's time-varying update progress, and the possibility of reading this update when responding to the end-users. Our theoretical analysis derives several formulas to determine the quorum size of a read quorum and the consensus result selected from this quorum is the data updated by the last write at the user-specified probability. When some replicas are unavailable, our model knows how to rescale the quorum and read values from surviving replicas could reduce the stale reads caused by node failures. The experimental results in Cassandra demonstrate that the PCG model can achieve up to 77.7 percent more accurate predictions and reduce up to 48.9 percent read latency than those of the previous model.

中文翻译：

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基于部分仲裁的数据存储中的概率一致性保证

许多 NoSQL 数据库支持基于仲裁的协议，它需要一个副本子集（称为仲裁）来响应每个写/读操作。这些系统配置仲裁大小以调整操作延迟并采用多个一致性级别。最近的一些工作表明，使用概率模型来量化读取最后一次更新的机会很重要，因为它可以避免在最终一致性下返回陈旧值。有两个具有挑战性的问题：（1）从不一致的副本，如何确定以指定概率读取最新数据的最小仲裁大小（即最低访问延迟）；(2) 大规模系统中节点故障频繁发生，如何保证基于概率的一致性读取。本文介绍了概率一致性保证 (PCG)，这是第一个动态群体决策和故障感知量化模型。PCG 模型分别量化了最近一次写入后的服务器端一致性，反映了对象随时间变化的更新进度，以及响应最终用户时读取此更新的可能性。我们的理论分析推导出了几个公式来确定读取仲裁的仲裁大小，并且从该仲裁中选择的共识结果是用户指定概率的最后一次写入更新的数据。当某些副本不可用时，我们的模型知道如何重新调整仲裁，并从幸存的副本中读取值可以减少由节点故障引起的过时读取。Cassandra 中的实验结果表明，PCG 模型可以实现高达 77.7% 的准确预测并减少多达 48 个。