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 5.2 RL Training and Application Phase The RL Training and Application Phase of IA2 transitions from initial preprocessing to actively engaging with defined action spaces and state representations, marked by: TD3-TD-SWAR Algorithm Application: Leveraging the action space and state representations crafted in the preprocessing phase, IA2 employs the TD3-TD-SWAR algorithm, as outlined in Algorithm 1. Unlike merely operating on preprocessed data, this approach integrates action space restrictions—accounting for existing index candidates and their masking history. Each tuning step recalibrates masking TD3-TD-SWAR Algorithm Application possibilities for subsequent selections, embodying a strategy that adaptively masks actions irrelevant based on the current agent states. Adaptation to Workloads: Designed for flexibility, IA2 applies learned strategies to a range of workloads, efficiently adapting to both familiar and unseen environments, demonstrating its capability to handle diverse operational scenarios. Adaptation to Workloads: 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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Beyond Preprocessing: Sequential Indexing and Temporal Consistency in IA2

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