HakiCC: LLM-Driven Multi-Agent Design and Optimization of Concurrency Control Protocols

原文：HakiCC: LLM-Driven Multi-Agent Design and Optimization of Concurrency Control Protocols

作者：Farzad Habibi, Juncheng Fang, Faisal Nawab

来源：arXiv cs.DB（数据库）

正文

Computer Science > Databases

arXiv:2610.00889v1 (cs)

[Submitted on 1 Oct 2026]

Title:HakiCC: LLM-Driven Multi-Agent Design and Optimization of Concurrency Control Protocols

Authors:Farzad Habibi, Juncheng Fang, Faisal Nawab

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Abstract:Large language models (LLMs) have recently been applied in systems research as a tool to reduce human-intensive engineering effort through cost-efficient automation. Decades of research have produced a rich landscape of concurrency control (CC) protocols, each encoding distinct trade-offs in correctness, throughput, and abort behavior. However, most applications in practice default to 2PL or OCC, because selecting and adapting a protocol to a specific application requires expert knowledge that is rarely available to application designers. This is a wasted opportunity, as an application-specific CC protocol can yield significant performance advantages over a generic baseline, but designing one requires deep expertise in CC protocol design.

In this paper, we propose HakiCC, an LLM-driven multi-agent pipeline that automatically designs, verifies, and optimizes concurrency control protocols tailored to a given target application. HakiCC provides a two-stage pipeline. In Stage 1, a multi-agent system takes a workload description as input and generates an application-specific CC protocol implementation, which is iteratively repaired and verified for conflict-serializability. In Stage 2, the verified protocol is further optimized for that application through an LLM-driven evolutionary loop targeting correctness and throughput. We evaluate HakiCC on TPC-C and AuctionMark as target workloads, producing and reporting ten application-specific CC protocols. All ten are conflict-serializable after Stage 1; Stage 2 improves throughput for every protocol, with average gains of +50.6% for TPC-C protocols and +92.2% for AuctionMark protocols.

Subjects:

Databases (cs.DB); Distributed, Parallel, and Cluster Computing (cs.DC); Multiagent Systems (cs.MA)

Cite as:

arXiv:2610.00889 [cs.DB]

(or

arXiv:2610.00889v1 [cs.DB] for this version)

https://doi.org/10.48550/arXiv.2610.00889

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arXiv-issued DOI via DataCite (pending registration)

主题

数据库 · 分布式计算 · 多智能体

由「前沿雷达」于 2026-10-01 采集。正文取自原文页面，已保留出处链接。标题与正文版权归原作者所有。