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<p>In order to reduce the burden of DBA, the knob tuning method based on reinforcement learning has been proposed and achieved good results in some cases. However, the performance of these solutions is not ideal as the workload features are not considered enough. To address these issues, we propose a database tuning system called ADWTune. In this model, ADWTune employs the idea of multiple sampling to gather workload data at different time points during the observation period. ADWTune uses these continuous data slices to characterize the dynamic changes in the workload. The key of ADWTune is its adaptive workload handling approach, which combines the dynamic features of workloads and the internal metrics of database as the state of the environment. At the same time, ADWTune includes a data repository, which reuses historical data to improve the adaptability of model to workload shifts. We conduct extensive experiments on various workloads. The experimental results demonstrate that ADWTune is better suited for dynamic environments than other methods based on reinforcement learning.</p>

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ADWTune: an adaptive dynamic workload tuning system with deep reinforcement learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年2月28日

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  • Cuixia Li,
  • Junhai Wang,
  • Jiahao Shi,
  • Liqiang Liu,
  • Shuyan Zhang

摘要

In order to reduce the burden of DBA, the knob tuning method based on reinforcement learning has been proposed and achieved good results in some cases. However, the performance of these solutions is not ideal as the workload features are not considered enough. To address these issues, we propose a database tuning system called ADWTune. In this model, ADWTune employs the idea of multiple sampling to gather workload data at different time points during the observation period. ADWTune uses these continuous data slices to characterize the dynamic changes in the workload. The key of ADWTune is its adaptive workload handling approach, which combines the dynamic features of workloads and the internal metrics of database as the state of the environment. At the same time, ADWTune includes a data repository, which reuses historical data to improve the adaptability of model to workload shifts. We conduct extensive experiments on various workloads. The experimental results demonstrate that ADWTune is better suited for dynamic environments than other methods based on reinforcement learning.