# Learning-Aided Heuristics Design for Storage System

arXiv: https://arxiv.org/abs/2106.07288 · HTML: https://mlsanity.com/paper/2106.07288/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 8 references that are in the map.
- First posted on arXiv 14 Jun 2021.

## Builds on (most cited first, 4 of 4)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [The Case for Learned Index Structures](https://mlsanity.com/paper/1712.01208/) (2017)
- [Learning Finite State Representations of Recurrent Policy Networks](https://mlsanity.com/paper/1811.12530/) (2018)

## Built on by (most cited first, 2 of 2)

- [StorageXTuner: An LLM Agent-Driven Automatic Tuning Framework for Heterogeneous Storage Systems](https://mlsanity.com/paper/2510.25017/) (2025)
- [LQoCo: Learning to Optimize Cache Capacity Overloading in Storage Systems](https://mlsanity.com/paper/2203.13678/) (2022)

## Abstract (arXiv)

Computer systems such as storage systems normally require transparent white-box algorithms that are interpretable for human experts. In this work, we propose a learning-aided heuristic design method, which automatically generates human-readable strategies from Deep Reinforcement Learning (DRL) agents. This method benefits from the power of deep learning but avoids the shortcoming of its black-box property. Besides the white-box advantage, experiments in our storage productions resource allocation scenario also show that this solution outperforms the systems default settings and the elaborately handcrafted strategy by human experts.