KernelOracle: Predicting the Linux Scheduler's Next Move with Deep Learning

TL;DR

This paper explores using deep learning, specifically LSTM networks, to predict Linux kernel scheduling decisions, aiming to create more adaptive and data-driven task schedulers based on real-world CFS behavior.

Contribution

It introduces a novel dataset of Linux scheduling behavior and develops an LSTM model to accurately forecast the next scheduled task, advancing data-driven kernel scheduling research.

Findings

LSTM model achieves high prediction accuracy.

Generated a comprehensive Linux scheduling dataset.

Demonstrated potential for integrating deep learning into kernel schedulers.

Abstract

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.