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

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

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 featu...

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Bibliographic Details
Main Authors:	Hyeonmyeong Lee, Sungmin Jung, Heeseung Jo
Format:	Article
Language:	English
Published:	MDPI AG 2022-07-01
Series:	Applied Sciences
Subjects:	operating systems performance scheduler tuning reinforcement learning
Online Access:	https://www.mdpi.com/2076-3417/12/14/7072

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