In distributed transaction processing, atomic commit protocol (ACP) is used to ensure database consistency. With the use of commodity compute nodes and networks, failures such as system crashes and network partitioning are common. It is therefore important for ACP to dynamically adapt to the operating condition for efficiency while ensuring the consistency of the database. Existing ACPs often assume stable operating conditions, hence, they are either non-generalizable to different environments or slow in practice. In this paper, we propose a novel and practical ACP, called Failure-Aware Atomic Commit (FLAC). In essence, FLAC includes three sub-protocols, which are specifically designed for three different environments: (i) no failure occurs, (ii) participant nodes might crash but there is no delayed connection, or (iii) both crashed nodes and delayed connection can occur. It models these environments as the failure-free, crash-failure, and network-failure robustness levels. During its operation, FLAC can monitor if any failure occurs and dynamically switch to operate the most suitable sub-protocol, using a robustness level state machine, whose parameters are fine-tuned by reinforcement learning. Consequently, it improves both the response time and throughput, and effectively handles nodes distributed across the Internet where crash and network failures might occur. We implement FLAC in a distributed transactional key-value storage system based on Google Percolator and evaluate its performance with both a micro benchmark and a macro benchmark of real workload. The results show that FLAC achieves up to 2.22x throughput improvement and 2.82x latency speedup, compared to existing ACPs for high-contention workloads.

本文提出了一种新颖实用的故障感知原子提交协议（FLAC），能够动态适应不同的操作环境以提高响应时间和吞吐量，通过对三个不同类型的环境进行分类，有效地处理分布式环境下的节点崩溃和网络失效，使用强化学习进行参数调整，并在基于Google Percolator的分布式键值存储系统上进行了性能测试，结果表明FLAC在高争用工作量下实现了高达2.22倍的吞吐量提高和2.82倍的延迟加速。

FLAC: 一种面向分布式事务的鲁棒性作故障感知原子提交协议