论文标题
喜p：使用深厚的增强学习，自动调整分布式文件系统的静态参数
Magpie: Automatically Tuning Static Parameters for Distributed File Systems using Deep Reinforcement Learning
论文作者
论文摘要
如今，分布式文件系统已被广泛使用，但是使用其默认配置通常不是最佳的。同时，调整配置参数通常具有挑战性且耗时。它需要专业知识和调整操作也可能很昂贵。静态参数尤其是这种情况，仅在重新启动系统或工作负载后，更改才会生效。我们提出了一种新颖的方法，即Magpie，该方法利用深厚的增强学习来通过策略性探索和利用配置参数空间来调整静态参数。为了增强静态参数的调整，我们的方法使用分布式文件系统的服务器和客户端指标来了解静态参数与性能之间的关系。我们的经验评估结果表明，Magpie可以明显提高分布式文件系统光泽的性能，在此过程中，我们的方法平均在调整朝着单个性能指标优化后，在默认配置方面取得了91.8％的吞吐量增益，而它达到了39.7％的吞吐量在基线上的吞吐量增长39.7％。
Distributed file systems are widely used nowadays, yet using their default configurations is often not optimal. At the same time, tuning configuration parameters is typically challenging and time-consuming. It demands expertise and tuning operations can also be expensive. This is especially the case for static parameters, where changes take effect only after a restart of the system or workloads. We propose a novel approach, Magpie, which utilizes deep reinforcement learning to tune static parameters by strategically exploring and exploiting configuration parameter spaces. To boost the tuning of the static parameters, our method employs both server and client metrics of distributed file systems to understand the relationship between static parameters and performance. Our empirical evaluation results show that Magpie can noticeably improve the performance of the distributed file system Lustre, where our approach on average achieves 91.8% throughput gains against default configuration after tuning towards single performance indicator optimization, while it reaches 39.7% more throughput gains against the baseline.
