Knob tuning plays a critical role in improving the performance of permissioned blockchains. However, efficient tuning remains challenging due to the architectural complexity of blockchains and the semantic gap between knob-specific logic and the numerical optimization requirements of tuning tools. In addition, configuration changes are often coupled across different stages of the transaction pipeline, making their performance impact difficult to isolate and predict. Since each trial requires deployment and distributed benchmarking, ineffective exploration incurs substantial cost. These challenges motivate BCTuner, a Large Language Model (LLM)-guided framework that combines knowledge-guided reasoning with structured search. BCTuner organizes multi-source tuning knowledge to support LLM-based reasoning over knob semantics, constraints, and deployment context. It formulates tuning as a Monte Carlo Tree Search (MCTS) process over structured action trajectories, where configurations are incrementally constructed, validated, evaluated, and refined rather than generated in one step. BCTuner further applies adaptive pruning to discard infeasible or low-potential branches before system evaluation. We evaluate BCTuner on Hyperledger Fabric and ChainMaker under diverse workloads and network settings. Experimental results show that BCTuner achieves up to 211.38% throughput improvement over default configurations and outperforms the state-of-the-art blockchain tuning method by up to 20% in performance, while requiring up to 8x fewer interactions with the blockchain system.

翻译：参数调优在提升许可型区块链性能中扮演关键角色。然而，由于区块链的架构复杂性以及参数特定逻辑与调优工具数值优化需求之间的语义鸿沟，高效调优仍面临挑战。此外，配置变更往往在事务管道的不同阶段产生耦合，导致难以隔离和预测其性能影响。由于每次试错都需要部署和分布式基准测试，低效的探索将产生大量成本。这些挑战催生了BCTuner——一种大语言模型引导的框架，将知识引导推理与结构化搜索相结合。BCTuner组织多源调优知识以支持基于大语言模型对参数语义、约束和部署上下文的推理。它将调优形式化为蒙特卡洛树搜索过程，作用于结构化动作轨迹，其中配置通过逐步构建、验证、评估和优化生成，而非一次性生成。BCTuner进一步应用自适应剪枝策略，在系统评估前剔除不可行或低潜力的分支。我们在Hyperledger Fabric和ChainMaker平台上，针对多样化工作负载和网络设置评估了BCTuner。实验结果表明，与默认配置相比，BCTuner实现了高达211.38%的吞吐量提升，性能上超越现有最先进的区块链调优方法最高达20%，同时所需与区块链系统的交互次数减少最多8倍。