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Distributed file systems are widely used nowadays, yet using their defaultconfigurations is often not optimal. At the same time, tuning configurationparameters is typically challenging and time-consuming. It demands expertiseand tuning operations can also be expensive. This is especially the case forstatic parameters, where changes take effect only after a restart of the systemor workloads. We propose a novel approach, Magpie, which utilizes deepreinforcement learning to tune static parameters by strategically exploring andexploiting configuration parameter spaces. To boost the tuning of the staticparameters, our method employs both server and client metrics of distributedfile systems to understand the relationship between static parameters andperformance. Our empirical evaluation results show that Magpie can noticeablyimprove the performance of the distributed file system Lustre, where ourapproach on average achieves 91.8% throughput gains against defaultconfiguration after tuning towards single performance indicator optimization,while it reaches 39.7% more throughput gains against the baseline.

Magpie: Automatically Tuning Static Parameters for Distributed File Systems using Deep Reinforcement Learning