STUN: Reinforcement-Learning-Based Optimization of Kernel Scheduler Parameters for Static Workload Performance

Abstract

Modern Linux operating systems are being used in a wide range of fields, from small IoT embedded devices to supercomputers. However, most machines use the default Linux scheduler parameters implemented for general-purpose environments. The problem is that the Linux scheduler cannot utilize the features of the various hardware and software environments, and it is therefore, difficult to achieve optimal performance in the machines. In this paper, we propose STUN, an automatic scheduler optimization framework. STUN modifies the five scheduling policies of the Linux kernel and 10 parameters automatically to optimize for each workload environment. STUN decreases the training time and enhances the efficiency through a filtering mechanism and training reward algorithms. Using STUN, users can optimize the performance of their machines at the OS scheduler level without manual control of the scheduler. STUN showed an execution time and improved FPS of 18.3% and 22.4% on a face detection workload, respectively. In addition, STUN showed 26.97%, 54.42%, and 256.13% performance improvements for microbenchmarks with 4, 44, and 120 cores for each.

Keywords

• operating systems
• performance
• scheduler
• tuning
• reinforcement learning