IA2: Leveraging Instance-Aware Index Advisor with Reinforcement Learning for Diverse Workloads

• URL: http://arxiv.org/abs/2404.05777v2
• Date: Wed, 10 Apr 2024 08:23:48 GMT
• Title: IA2: Leveraging Instance-Aware Index Advisor with Reinforcement Learning for Diverse Workloads
• Authors: Taiyi Wang, Eiko Yoneki,
• Abstract summary: Instance-Aware Index Advisor (IA2) is a novel deep reinforcement learning (DRL)-based approach for optimizing index selection in databases. IA2 introduces the Twin Delayed Deep Deterministic Policy Gradient - Temporal Difference State-Wise Action Refinery (TD3-TD-SWAR) model.
• Score: 0.46040036610482665
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This study introduces the Instance-Aware Index Advisor (IA2), a novel deep reinforcement learning (DRL)-based approach for optimizing index selection in databases facing large action spaces of potential candidates. IA2 introduces the Twin Delayed Deep Deterministic Policy Gradient - Temporal Difference State-Wise Action Refinery (TD3-TD-SWAR) model, enabling efficient index selection by understanding workload-index dependencies and employing adaptive action masking. This method includes a comprehensive workload model, enhancing its ability to adapt to unseen workloads and ensuring robust performance across diverse database environments. Evaluation on benchmarks such as TPC-H reveals IA2's suggested indexes' performance in enhancing runtime, securing a 40% reduction in runtime for complex TPC-H workloads compared to scenarios without indexes, and delivering a 20% improvement over existing state-of-the-art DRL-based index advisors.

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