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 5 System Framework of IA2 As shown in Figure 3, IA2 operates through a structured two-phase approach, leveraging deep reinforcement learning to optimize index selection for both trained workloads and unseen scenarios. It depicts IA2’s workflow, where the user’s input workload is processed to generate states and action pools for downstream RL agents. These agents then make sequential decisions on index additions, adhering to budget constraints, demonstrating IA2’s methodical approach to enhancing database performance through intelligent index selection. 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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Unseen Workload Optimization: The Two-Phase IA2 Approach

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