DOT: Dynamic Knob Selection and Online Sampling for Automated Database Tuning

• URL: http://arxiv.org/abs/2603.15540v1
• Date: Mon, 16 Mar 2026 17:05:34 GMT
• Title: DOT: Dynamic Knob Selection and Online Sampling for Automated Database Tuning
• Abstract summary: We present DOT, a dynamic knob selection and online sampling tuning algorithm.<n> DOT prunes low-importance tuning parameters and a Likelihood Ratio Test (LRT) strategy to balance exploration and exploitation.<n>Experiments show that DOT achieves matching or outperforming performance compared to state-of-the-art tuners.
• Score: 17.110740232814674
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Database Management Systems (DBMS) are crucial for efficient data management and access control, but their administration remains challenging for Database Administrators (DBAs). Tuning, in particular, is known to be difficult. Modern systems have many tuning parameters, but only a subset significantly impacts performance. Focusing on these influential parameters reduces the search space and optimizes performance. Current methods rely on costly warm-up phases and human expertise to identify important tuning parameters. In this paper, we present DOT, a dynamic knob selection and online sampling DBMS tuning algorithm. DOT uses Recursive Feature Elimination with Cross-Validation (RFECV) to prune low-importance tuning parameters and a Likelihood Ratio Test (LRT) strategy to balance exploration and exploitation. For parameter search, DOT uses a Bayesian Optimization (BO) algorithm to optimize configurations on-the-fly, eliminating the need for warm-up phases or prior knowledge (although existing knowledge can be incorporated). Experiments show that DOT achieves matching or outperforming performance compared to state-of-the-art tuners while substantially reducing tuning overhead.

Related papers

• Relation-Aware Bayesian Optimization of DBMS Configurations Guided by Affinity Scores [2.474203056060563]
  Database Management Systems (DBMSs) are fundamental for managing large-scale and heterogeneous data, and their performance is critically influenced by configuration parameters.<n>Recent research has focused on automated configuration optimization using machine learning; however, existing approaches still exhibit several key limitations.<n>We propose RelTune, a novel framework that represents parameter dependencies as a Graph and learns GNN-based latent embeddings that encode performancerelevant semantics.
  arXiv  Detail & Related papers  (2025-10-31T03:46:42Z)
• TR-PTS: Task-Relevant Parameter and Token Selection for Efficient Tuning [41.097430916756]
  Large pre-trained models achieve remarkable performance in vision tasks but are impractical for fine-tuning due to high computational and storage costs.<n>We propose Task-Relevant.<n>and Token Selection (TR-PTS), a task-driven framework that enhances both computational efficiency and accuracy.<n>We evaluate TR-PTS on benchmark, including FGVC and VTAB-1k, where it achieves state-of-the-art performance, surpassing full fine-tuning by 3.40% and 10.35%, respectively.
  arXiv  Detail & Related papers  (2025-07-30T17:47:13Z)
• BIPEFT: Budget-Guided Iterative Search for Parameter Efficient Fine-Tuning of Large Pretrained Language Models [63.52035708182815]
  We introduce a novel Budget-guided Iterative search strategy for automatic PEFT (BIPEFT) BIPEFT employs a new iterative search strategy to disentangle the binary module and rank dimension search spaces. Extensive experiments on public benchmarks demonstrate the superior performance of BIPEFT for downstream tasks with a low parameter budget.
  arXiv  Detail & Related papers  (2024-10-04T18:50:46Z)
• E2ETune: End-to-End Knob Tuning via Fine-tuned Generative Language Model [22.661022020554622]
  E2ETune is an end-to-end knob tuner powered by a fine-tuned generative language model.<n>We propose a novel data generation framework to efficiently produce a large amount of training data.<n>Then, these data are used to fine-tune a generative language model, yielding an end-to-end knob tuner.
  arXiv  Detail & Related papers  (2024-04-17T17:28:05Z)
• Efficient Architecture Search via Bi-level Data Pruning [70.29970746807882]
  This work pioneers an exploration into the critical role of dataset characteristics for DARTS bi-level optimization. We introduce a new progressive data pruning strategy that utilizes supernet prediction dynamics as the metric. Comprehensive evaluations on the NAS-Bench-201 search space, DARTS search space, and MobileNet-like search space validate that BDP reduces search costs by over 50%.
  arXiv  Detail & Related papers  (2023-12-21T02:48:44Z)
• PTP: Boosting Stability and Performance of Prompt Tuning with Perturbation-Based Regularizer [94.23904400441957]
  We introduce perturbation-based regularizers, which can smooth the loss landscape, into prompt tuning. We design two kinds of perturbation-based regularizers, including random-noise-based and adversarial-based. Our new algorithms improve the state-of-the-art prompt tuning methods by 1.94% and 2.34% on SuperGLUE and FewGLUE benchmarks, respectively.
  arXiv  Detail & Related papers  (2023-05-03T20:30:51Z)
• Sensitivity-Aware Visual Parameter-Efficient Fine-Tuning [91.5113227694443]
  We propose a novel visual. sensuous-aware fine-Tuning (SPT) scheme. SPT allocates trainable parameters to task-specific important positions. Experiments on a wide range of downstream recognition tasks show that our SPT is complementary to the existing PEFT methods.
  arXiv  Detail & Related papers  (2023-03-15T12:34:24Z)
• Performance-Driven Controller Tuning via Derivative-Free Reinforcement Learning [6.5158195776494]
  We tackle the controller tuning problem using a novel derivative-free reinforcement learning framework. We conduct numerical experiments on two concrete examples from autonomous driving, namely, adaptive cruise control with PID controller and trajectory tracking with MPC controller. Experimental results show that the proposed method outperforms popular baselines and highlight its strong potential for controller tuning.
  arXiv  Detail & Related papers  (2022-09-11T13:01:14Z)
• AUTOMATA: Gradient Based Data Subset Selection for Compute-Efficient Hyper-parameter Tuning [72.54359545547904]
  We propose a gradient-based subset selection framework for hyper- parameter tuning. We show that using gradient-based data subsets for hyper- parameter tuning achieves significantly faster turnaround times and speedups of 3$times$-30$times$.
  arXiv  Detail & Related papers  (2022-03-15T19:25:01Z)
• Amortized Auto-Tuning: Cost-Efficient Transfer Optimization for Hyperparameter Recommendation [83.85021205445662]
  We propose an instantiation--amortized auto-tuning (AT2) to speed up tuning of machine learning models. We conduct a thorough analysis of the multi-task multi-fidelity Bayesian optimization framework, which leads to the best instantiation--amortized auto-tuning (AT2)
  arXiv  Detail & Related papers  (2021-06-17T00:01:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.