Machine Learning (ML) library in Linux kernel

TL;DR

This paper proposes an architecture and a proof-of-concept implementation for integrating machine learning models into the Linux kernel to enable self-evolving capabilities without significant performance degradation.

Contribution

It introduces a novel ML infrastructure architecture for Linux kernel and demonstrates its feasibility through a proof-of-concept implementation.

Findings

Feasibility of integrating ML models into Linux kernel.

Designed interface for kernel-space ML model proxy.

Proof-of-concept implementation validates approach.

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.