🤖 AI Summary
This work addresses the challenges of configuring and tuning the complex Linux kernel, where deploying machine learning (ML) models directly in kernel space is hindered by the absence of floating-point unit (FPU) support and prohibitive performance overhead. To overcome these limitations, the authors propose the first lightweight ML infrastructure tailored for the Linux kernel, enabling safe and efficient model inference without FPU usage through a cooperative kernel–user space design. The architecture comprises a kernel module, a lightweight inference agent, and a cross-space communication interface. A prototype implementation demonstrates the feasibility of this approach, and experimental results show that it introduces ML capabilities into the kernel with low overhead and high scalability, opening a new avenue for intelligent kernel optimization.
📝 Abstract
Linux kernel is a huge code base with enormous number of subsystems and possible configuration options that results in unmanageable complexity of elaborating an efficient configuration. Machine Learning (ML) is approach/area of learning from data, finding patterns, and making predictions without implementing algorithms by developers that can introduce a self-evolving capability in Linux kernel. However, introduction of ML approaches in Linux kernel is not easy way because there is no direct use of floating-point operations (FPU) in kernel space and, potentially, ML models can be a reason of significant performance degradation in Linux kernel. Paper suggests the ML infrastructure architecture in Linux kernel that can solve the declared problem and introduce of employing ML models in kernel space. Suggested approach of kernel ML library has been implemented as Proof Of Concept (PoC) project with the goal to demonstrate feasibility of the suggestion and to design the interface of interaction the kernel-space ML model proxy and the ML model user-space thread.