Learning effective configurations in computer systems without hand-crafting models for every parameter is a long-standing problem. This paper investigates the use of deep reinforcement learning for runtime parameters of cloud databases under latency constraints. Cloud services serve up to thousands of concurrent requests per second and can adjust critical parameters by leveraging performance metrics. In this work, we use continuous deep reinforcement learning to learn optimal cache expirations for HTTP caching in content delivery networks. To this end, we introduce a technique for asynchronous experience management called delayed experience injection, which facilitates delayed reward and next-state computation in concurrent environments where measurements are not immediately available. Evaluation results show that our approach based on normalized advantage functions and asynchronous CPU-only training outperforms a statistical estimator.
@article{arxiv.1610.09903,
title = {Learning Runtime Parameters in Computer Systems with Delayed Experience Injection},
author = {Michael Schaarschmidt and Felix Gessert and Valentin Dalibard and Eiko Yoneki},
journal= {arXiv preprint arXiv:1610.09903},
year = {2016}
}