KernelOracle: Predicting the Linux Scheduler's Next Move with Deep Learning
This work addresses the challenge of improving task scheduling efficiency in the Linux kernel for system developers and users, though it is incremental as it focuses on prediction rather than direct scheduler replacement.
This research tackled the problem of predicting the Linux Completely Fair Scheduler's task sequence using deep learning, resulting in the development of an LSTM network that forecasts the next scheduled task based on a novel dataset of real-world CFS behavior.
Efficient task scheduling is paramount in the Linux kernel, where the Completely Fair Scheduler (CFS) meticulously manages CPU resources to balance high utilization with interactive responsiveness. This research pioneers the use of deep learning techniques to predict the sequence of tasks selected by CFS, aiming to evaluate the feasibility of a more generalized and potentially more adaptive task scheduler for diverse workloads. Our core contributions are twofold: first, the systematic generation and curation of a novel scheduling dataset from a running Linux kernel, capturing real-world CFS behavior; and second, the development, training, and evaluation of a Long Short-Term Memory (LSTM) network designed to accurately forecast the next task to be scheduled. This paper further discusses the practical pathways and implications of integrating such a predictive model into the kernel's scheduling framework. The findings and methodologies presented herein open avenues for data-driven advancements in kernel scheduling, with the full source code provided for reproducibility and further exploration.