用深度学习预测Linux调度器下一个调度的任务，提升系统响应性。
KernelOracle: Predicting the Linux Scheduler's Next Move with Deep Learning
- 基于真实内核运行数据构建调度行为数据集
- LSTM模型准确预测下一任务选择，提升调度可预见性
- 为未来自适应调度器提供数据驱动的新思路
高效的任务调度对Linux内核至关重要，完全公平调度器（CFS）精心管理CPU资源，以在高利用率与交互响应性之间取得平衡。本研究首次采用深度学习技术预测CFS选择任务的序列，旨在评估更通用、更具适应性的调度器在多样化工作负载下的可行性。核心贡献有二：一是从运行中的Linux内核系统性生成并整理出一个新型调度数据集，捕捉真实的CFS行为；二是开发、训练并评估了一个长短期记忆网络（LSTM），用于精确预测下一个被调度的任务。本文还探讨了将此类预测模型集成到内核调度框架的实际路径与影响。研究成果和方法为内核调度的数据驱动演进开辟了新途径，完整源代码已公开，便于复现与进一步探索。
原文摘要 · Abstract (English)
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.
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