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In distributed transaction processing, atomic commit protocol (ACP) is usedto ensure database consistency. With the use of commodity compute nodes andnetworks, failures such as system crashes and network partitioning are common.It is therefore important for ACP to dynamically adapt to the operatingcondition for efficiency while ensuring the consistency of the database.Existing ACPs often assume stable operating conditions, hence, they are eithernon-generalizable to different environments or slow in practice.  In this paper, we propose a novel and practical ACP, called Failure-AwareAtomic Commit (FLAC). In essence, FLAC includes three protocols, which arespecifically 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 theseenvironments as the failure-free, crash-failure, and network-failure robustnesslevels. During its operation, FLAC can monitor if any failure occurs anddynamically switch to operate the most suitable protocol, using a robustnesslevel state machine, whose parameters are fine-tuned by reinforcement learning.Consequently, it improves both the response time and throughput, andeffectively handles nodes distributed across the Internet where crash andnetwork failures might occur. We implement FLAC in a distributed transactionalkey-value storage system based on Google Percolator and evaluate itsperformance with both a micro benchmark and a macro benchmark of real workload.The results show that FLAC achieves up to 2.22x throughput improvement and2.82x latency speedup, compared to existing ACPs for high-contention workloads.

FLAC: A Robust Failure-Aware Atomic Commit Protocol for Distributed Transactions