Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Abstract and 1. Introduction Related Works
2.1 Traditional Index Selection Approaches
2.2 RL-based Index Selection Approaches


Index Selection Problem


Methodology
4.1 Formulation of the DRL Problem
4.2 Instance-Aware Deep Reinforcement Learning for Efficient Index Selection


System Framework of IA2
5.1 Preprocessing Phase
5.2 RL Training and Application Phase


Experiments
6.1 Experimental Setting
6.2 Experimental Results
6.3 End-to-End Performance Comparison
6.4 Key Insights


Conclusion and Future Work, and References Related Works
2.1 Traditional Index Selection Approaches
2.2 RL-based Index Selection Approaches Related Works Related Works 2.1 Traditional Index Selection Approaches 2.1 Traditional Index Selection Approaches 2.2 RL-based Index Selection Approaches 2.2 RL-based Index Selection Approaches Index Selection Problem Index Selection Problem Index Selection Problem Index Selection Problem Methodology
4.1 Formulation of the DRL Problem
4.2 Instance-Aware Deep Reinforcement Learning for Efficient Index Selection Methodology Methodology 4.1 Formulation of the DRL Problem 4.1 Formulation of the DRL Problem 4.2 Instance-Aware Deep Reinforcement Learning for Efficient Index Selection 4.2 Instance-Aware Deep Reinforcement Learning for Efficient Index Selection System Framework of IA2
5.1 Preprocessing Phase
5.2 RL Training and Application Phase System Framework of IA2 System Framework of IA2 System Framework of IA2 5.1 Preprocessing Phase 5.1 Preprocessing Phase 5.2 RL Training and Application Phase 5.2 RL Training and Application Phase Experiments
6.1 Experimental Setting
6.2 Experimental Results
6.3 End-to-End Performance Comparison
6.4 Key Insights Experiments Experiments 6.1 Experimental Setting 6.1 Experimental Setting 6.2 Experimental Results 6.2 Experimental Results 6.3 End-to-End Performance Comparison 6.3 End-to-End Performance Comparison 6.4 Key Insights 6.4 Key Insights Conclusion and Future Work, and References Conclusion and Future Work, and References Conclusion and Future Work, and References Conclusion and Future Work, and References 4 Methodology In this section, we outline our novel approach to the Index Selection Problem (ISP), significantly advancing the application of Deep Reinforcement Learning (DRL). Our methodology not only frames the ISP within a DRL context but also introduces a groundbreaking RL model, the Twin Delayed Deep Deterministic Policy Gradients-Temporal Difference-State-Wise Action Refinery (TD3-TD-SWAR). Drawing on the innovative work of [12] and [15], TD3-TD-SWAR is specifically designed to efficiently manage the ISP’s complex solution space. A key novelty of our approach is the adaptive action masking mechanism, which accelerates training and sharpens decision-making by filtering out less beneficial actions based on current conditions, achieving optimal index selection with minimal decision steps. 4.1 Formulation of the DRL Problem Deep Reinforcement Learning (DRL) offers a method for sequential decision-making to optimize database index configurations, addressing the Index Selection Problem (ISP) [7, 13]. The goal in ISP is for an agent to find an optimal index set 𝐼∗ from candidates to reduce workload execution costs. Given budget constraints, the agent strategically sequences new index additions, navigating the dynamic environment to find configurations that improve database performance over time, showcasing ISP as a fundamental DRL challenge that underscores strategic decision-making under constraints. While basic Deep Reinforcement Learning (DRL) techniques are applicable, the challenge of large action space, i.e., too many index candidates, necessitates an advanced solution. Our Twin Delayed Deep Deterministic Policy GradientsTemporal Difference-State-Wise Action Refinery (TD3-TDSWAR) model addresses this by refining action space pruning, ensuring efficiency and precision in tackling the complexities of index selection under storage and training limitations. Authors:
(1) Taiyi Wang, University of Cambridge, Cambridge, United Kingdom (Taiyi.Wang@cl.cam.ac.uk);
(2) Eiko Yoneki, University of Cambridge, Cambridge, United Kingdom (eiko.yoneki@cl.cam.ac.uk). Authors: Authors: (1) Taiyi Wang, University of Cambridge, Cambridge, United Kingdom (Taiyi.Wang@cl.cam.ac.uk); (2) Eiko Yoneki, University of Cambridge, Cambridge, United Kingdom (eiko.yoneki@cl.cam.ac.uk). This paper is available on arxiv under CC BY-NC-SA 4.0 Deed (Attribution-Noncommercial-Sharelike 4.0 International) license. This paper is available on arxiv under CC BY-NC-SA 4.0 Deed (Attribution-Noncommercial-Sharelike 4.0 International) license. available on arxiv available on arxiv

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Adaptive Action Masking: Accelerating Decision-Making in Database Tuning

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