IA2: Leveraging Instance-Aware Index Advisor with Reinforcement Learning for Diverse Workloads
<|reference_start|>IA2: Leveraging Instance-Aware Index Advisor with Reinforcement Learning for Diverse Workloads: 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.<|reference_end|>
@article{wang2024ia2:,
    title={IA2: Leveraging Instance-Aware Index Advisor with Reinforcement Learning
  for Diverse Workloads},
    author={Taiyi Wang, Eiko Yoneki},
    journal={arXiv preprint arXiv:2404.05777},
    year={2024},
    doi={10.1145/3642970.3655839},
    archivePrefix={arXiv},
    eprint={2404.05777},
    primaryClass={cs.DB cs.AI}
}