一句话：提出基于批次的一致性优化方法，静态推断操作集的一致性需求并据此在运行时安全地放松重排序。
分布式应用中，一致性优化需在保证应用级完整性条件的同时尽量放松操作顺序。本文提出基于批次的一致性（batch-based consistency），允许程序员用完整性属性定制一致性要求。其实现分两步：先静态推断一组有界操作执行所需的最优一致性约束，再用这些约束参数化一种新的分布式协议，在运行时安全时放松操作重排序。该方法覆盖常见一致性模型。作者在 Peepco 中实现，展示其支持部分数据复制的表达能力，并在不同设置下考察运行时性能影响。
We present batch-based consistency, a new approach for consistency optimization that allows programmers to specialize consistency with application-level integrity properties. We implement the approach with a two-step process: we statically infer optimal consistency requirements for executions of bounded sets of operations, and then, use the inferred requirements to parameterize a new distributed protocol to relax operation reordering at run time when it is safe to do so. Our approach supports standard notions of consistency. We implement batch-based consistency in Peepco , demonstrate its expressiveness for partial data replication, and examine Peepco’s run-time performance impact in different settings.
@article{paperbot3164,
title = {Peepco: Batch-Based Consistency Optimization},
author = {Ivan Kuraj and John Feser and Nadia Polikarpova and Armando Solar-Lezama},
journal = {Proceedings of the ACM on Programming Languages},
volume = {9},
number = {OOPSLA1},
year = {2025},
doi = {10.1145/3720513}
}