Page 132 - 《软件学报》2025年第12期

荣垂田 等: 多目标深度强化学习驱动的数据库系统参数优化技术 5513

1
(School of Computer Science and Technology, Tiangong University, Tianjin 300387, China)
2
(School of Information Engineering, Ningxia University, Yinchuan 750021, China)
Abstract: The tuning of database system parameters directly impacts its performance and the utilization of system resources. Relational
database management systems typically offer hundreds of parameters that can be adjusted to achieve optimal performance and service
capabilities. Database system performance optimization is traditionally carried out manually by experienced database administrators
(DBAs). However, due to the characteristics of parameter tuning, such as the large number of parameters, their heterogeneity, and the
complex correlations among them, traditional manual methods are inefficient, costly, and lack reusability. To enhance the efficiency of
database system performance optimization, automated parameter tuning techniques have become a key focus in the database field.
Reinforcement learning, with its ability to interact with the system environment and gradually improve through feedback, has been widely
applied in the optimization of complex systems. Some related studies have applied reinforcement learning or its variants to database
parameter tuning, but they have relied on single-objective optimization methods. Database system parameter tuning is a multi-objective
optimization task, usually performed under resource constraints. Therefore, existing methods have several limitations: (1) transforming the
multi-objective optimization problem into a single-objective optimization problem through simple linear transformations requires iterative
attempts, making optimizations costly; (2) existing methods cannot adapt to the dynamic changes in database system requirements, limiting
their adaptability; (3) reinforcement learning methods used in existing studies are designed for single-objective optimization, and their
applications to multi-objective tasks make it difficult to effectively align preferences (the weight coefficients of current objectives) with
corresponding optimal strategies, potentially leading to suboptimal solutions; (4) existing research primarily focuses on optimizing
throughput and latency, while ignoring resource utilization such as memory. To address these issues, this study proposes a multi-objective
deep deterministic policy gradient-based reinforcement learning algorithm (MODDPG). This method is a native multi-objective
reinforcement learning approach that does not require transforming the multi-objective task of database system parameters tuning into a
single-objective task, enabling it to efficiently adapt to dynamic changes in database system requirements. By improving the reward
mechanism of the reinforcement learning algorithm, the alignment between preferences and optimal strategies can be quickly achieved,
effectively avoiding suboptimal solutions. Consequently, the training process of the reinforcement learning model can be accelerated, and
the efficiency of database system parameter tuning can be improved. To further validate the generality of the proposed method, the multi-
objective optimization approach is extended to achieve a collaborative optimization goal of improving both database performance and
resource utilization. Experiments using TPC-C and SYSBench benchmarks demonstrate the effectiveness and practicality of the proposed
parameter tuning method. The results show significant advantages in terms of model training efficiency and the effectiveness of database
parameter tuning.
Key words: parameter optimization; database system; deep reinforcement learning; multi-objective optimization; performance optimization
数据库系统是数据管理的核心, 能够对大规模的数据进行高效的组织、存储, 提供高效的数据访问服务, 确保
数据的完整性和一致性, 已经成为企业信息和数据资产管理的核心, 也是业务运营、决策制定和创新发展的关键
基础. 数据库不仅是存储和管理数据的核心技术, 更是支撑复杂业务和系统高效、稳定运行的基础软件. 因此, 数
据库系统性能的优化不仅有利于提高数据管理的效率, 还有助于数据价值的挖掘和释放, 进一步提升业务处理效
率, 助力企业实现提质、降本、增效.
数据库系统的参数优化是提高性能和提供可靠性服务的关键, 主流的关系数据库系统通常包含数百个参数可
供调整和优化 [1] . 例如, MySQL 数据库中的 innodb_buffer_pool_size 用于调整内存缓存池大小; table_open_cache
用于调整会话中可打开表的数量 [2] ; innodb_buffer_pool_instances 用于指定将 InnoDB 缓冲池划分为独立实例的数
量; skip_name_resolve 设置为 ON 时, MySQL 不进行 DNS 解析, 可加快网络连接的速度. 根据实际的业务需求、
工作负载和硬件环境正确地对数据库系统的调优参数进行设置, 不仅可以直接改善数据库系统的响应速度和吞吐
量、提高系统资源的利用率, 还有利于提升信息系统整体的性能和可靠性.
数据库系统参数优化的难点在于如何选择合适的参数并设置合理的取值使系统的性能与负载的需求相匹配.
数据库系统产品通常会提供一些默认参数, 这些参数是数据库厂商根据历史运行经验提供的具有广泛的应用场景
和硬件适配能力的参数. 但是, 对于特定的应用需求或工作负载数据库系统使用默认的参数难以提供高性能、高
可靠的服务. 为了充分发挥数据库系统的性能优势、提高资源的利用率, DBA 通常需要根据业务需求、负载特点、
硬件环境等对数据库系统的参数进行优化. 数据库系统的参数调优是数据库领域公认的复杂工程问题, 数据库中