The integration of Machine Learning into Linux Kernel optimization has revolutionized system performance by enabling dynamic resource allocation, adaptive scheduling, and intelligent power management. This paper explores current trends and future directions in machine learning driven kernel optimization, highlighting key applications such as reinforcement learning for CPU scheduling, predictive memory management, and ML-based congestion control in networking. We analyse the advantages of ML over traditional rule-based methods, demonstrating how data-driven optimization enhances efficiency and responsiveness. However, challenges such as interpretability, real-time constraints, and computational overhead pose significant barriers to widespread adoption. To address these, we discuss emerging solutions, including Explainable AI (XAI), federated learning for privacy-preserving model training, and AutoML for automated performance tuning. This study provides a comprehensive review of machine learning’s role in optimizing the Linux Kernel and outlines future research directions to maximize its potential in next-generation operating systems.

Linux Kernel Optimization, Machine Learning in Operating Systems, Reinforcement Learning for CPU Scheduling, Memory Management using ML, Predictive Congestion Control, Explainable AI (XAI) in Kernel Optimization.

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