FSbrain: An intelligent I/O performance tuning system

Abstract

To bridge the performance gap between network and storage, performance tuning of file systems according to different workloads has become an important work on the Internet of Things (IoT). However, existing methods on file system tuning mainly focused on tailoring the file system kernel codes, or cannot scope to high dimensional parameters and dynamically changing workloads. In this paper, we propose an automatic I/O performance tuning system FSbrain that recommends reasonable and performance-efficient configurations for file systems by using machine learning. It makes the configuration recommendations based on current workloads within an acceptable time, however, without any manual interventions or kernel code modifications. In specific, FSbrain mainly consists of two phases which are model training and configuration tuning. Since tuning attempts will cause frequent remounting on target file systems, we deploy FSbrain and conduct the tuning sessions on an experimental node called “shadow server”. We evaluate FSbrain on three representative file systems (namely, Ext4, F2FS, and PMFS). The experimental results show that the configuration recommended by FSbrain can improve the I/O performance by up to 1.28× averagely than the default configuration. Besides, FSbrain reduces the overall tuning time by 90% compared to manual tuning.