发表机构
University of California, Irvine（加州大学尔湾分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出HakiCC，一个LLM驱动的多智能体流水线，自动设计、验证和优化特定应用的并发控制协议，在TPC-C和AuctionMark上生成十个协议，吞吐量平均提升50.6%和92.2%。
AI 中文摘要
大型语言模型（LLMs）最近已被应用于系统研究中，作为一种通过成本高效的自动化来减少人力密集型工程工作的工具。数十年的研究已经产生了丰富的并发控制（CC）协议体系，每种协议在正确性、吞吐量和中止行为方面都编码了不同的权衡。然而，实际中的大多数应用默认使用2PL或OCC，因为为特定应用选择和调整协议需要专家知识，而应用设计者很少具备这些知识。这是一个被浪费的机会，因为特定于应用的CC协议可以比通用基线产生显著的性能优势，但设计一个这样的协议需要深厚的CC协议设计专业知识。在本文中，我们提出了HakiCC，一个由LLM驱动的多智能体流水线，能够自动设计、验证和优化针对给定目标应用的并发控制协议。HakiCC提供了一个两阶段流水线。在第一阶段，一个多智能体系统以工作负载描述为输入，生成一个特定于应用的CC协议实现，并通过迭代修复和验证来确保冲突可串行性。在第二阶段，经过验证的协议通过一个以正确性和吞吐量为目标的LLM驱动的进化循环，针对该应用进一步优化。我们在TPC-C和AuctionMark上评估了HakiCC作为目标工作负载，生成并报告了十个特定于应用的CC协议。所有十个协议在第一阶段后都是冲突可串行化的；第二阶段提高了每个协议的吞吐量，TPC-C协议的平均增益为+50.6%，AuctionMark协议的平均增益为+92.2%。
英文摘要
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.
