Database Management Systems (DBMSs) are fundamental for managing large-scale and heterogeneous data, and their performance is critically influenced by configuration parameters. Effective tuning of these parameters is essential for adapting to diverse workloads and maximizing throughput while minimizing latency. Recent research has focused on automated configuration optimization using machine learning; however, existing approaches still exhibit several key limitations. Most tuning frameworks disregard the dependencies among parameters, assuming that each operates independently. This simplification prevents optimizers from leveraging relational effects across parameters, limiting their capacity to capture performancesensitive interactions. Moreover, to reduce the complexity of the high-dimensional search space, prior work often selects only the top few parameters for optimization, overlooking others that contribute meaningfully to performance. Bayesian Optimization (BO), the most common method for automatic tuning, is also constrained by its reliance on surrogate models, which can lead to unstable predictions and inefficient exploration. To overcome these limitations, we propose RelTune, a novel framework that represents parameter dependencies as a Relational Graph and learns GNN-based latent embeddings that encode performancerelevant semantics. RelTune further introduces Hybrid-Score-Guided Bayesian Optimization (HBO), which combines surrogate predictions with an Affinity Score measuring proximity to previously high-performing configurations. Experimental results on multiple DBMSs and workloads demonstrate that RelTune achieves faster convergence and higher optimization efficiency than conventional BO-based methods, achieving state-of-the-art performance across all evaluated scenarios.

翻译：数据库管理系统（DBMSs）是管理大规模异构数据的基础，其性能受配置参数的影响至关重要。有效调整这些参数对于适应多样化工作负载、最大化吞吐量同时最小化延迟至关重要。近期研究集中于利用机器学习进行自动化配置优化；然而，现有方法仍存在若干关键限制。大多数调优框架忽略参数间的依赖关系，假设每个参数独立运作。这种简化使优化器无法利用参数间的关联效应，限制了其捕捉性能敏感交互的能力。此外，为降低高维搜索空间的复杂性，先前工作通常仅选择少数几个参数进行优化，忽视了其他对性能有显著贡献的参数。贝叶斯优化（BO）作为自动调优中最常用的方法，也因其对代理模型的依赖而受限，可能导致预测不稳定和探索效率低下。为克服这些限制，我们提出了RelTune，一种新颖的框架，将参数依赖关系表示为关系图，并学习基于图神经网络的潜在嵌入，以编码与性能相关的语义。RelTune进一步引入了混合分数引导的贝叶斯优化（HBO），该方法结合了代理模型预测与亲和度分数，后者用于衡量与先前高性能配置的接近程度。在多个DBMS和工作负载上的实验结果表明，RelTune相比传统的基于BO的方法实现了更快的收敛速度和更高的优化效率，在所有评估场景中均达到了最先进的性能。