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arXiv · 2106.07288

Learning-Aided Heuristics Design for Storage System

Yingtian Tang·Han Lu·Xijun Li·Lei Chen·Mingxuan Yuan·Jia Zeng
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Abstract

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.

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Yingtian Tang, Han Lu, Xijun Li, Lei Chen, Mingxuan Yuan, Jia Zeng. 2021-06-14. Learning-Aided Heuristics Design for Storage System. https://arxiv.org/abs/2106.07288

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