<ul><li>Efficient task scheduling is crucial in the Linux kernel, particularly with the Completely Fair Scheduler (CFS) managing CPU resources.</li><li>This research introduces deep learning methods to predict the task sequence chosen by CFS, aiming for a more adaptable scheduler for various workloads.</li><li>Key contributions include creating a unique scheduling dataset from a live Linux kernel to capture CFS behavior and training a Long Short-Term Memory (LSTM) network for forecasting the next task.</li><li>This study explores the potential integration of predictive models into the kernel's scheduling system, offering data-driven improvements in kernel scheduling. Full source code is shared for transparency and further research.</li></ul>

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

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