New Proposal Explores Machine Learning Assistance for Linux Kernel Behavior

Kernel Gets a Brain Boost: Linux Mulls ML-Powered Decision-Making—Without Losing Its Soul

In a move that could quietly revolutionize how Linux manages itself under the hood, IBM engineer Viacheslav Dubeyko has floated a bold proposal on the Linux kernel mailing list: What if the kernel could tap into machine learning—without letting AI run the show?

Before you picture a rogue AI kernel taking over your server, let’s be crystal clear. This isn’t about embedding ML models directly into the kernel’s core. Instead, Dubeyko envisions a lightweight “ML proxy” inside the kernel that would act like a bridge—exposing structured data such as performance metrics and internal state to machine learning models running safely in user space. The kernel stays in charge, receiving optional recommendations from the ML models, but ultimately deciding whether to apply, test, or ignore them.

This architecture keeps the kernel fully deterministic—a sacred principle in Linux development—while letting ML models experiment and learn outside its critical boundaries. Training, inference, and model refinement all happen in user space, ensuring zero risk to kernel stability. Existing Linux mechanisms like sysfs, character devices, FUSE, or eBPF could serve as the transport layer for this data exchange.

But wait—there’s more. Dubeyko’s proposal includes a feedback loop where the kernel evaluates the effectiveness of any ML-suggested changes and reports back efficiency metrics to user space. This creates a virtuous cycle: models get smarter based on real-world kernel behavior, but the kernel itself never loses control.

To prove the concept isn’t just vaporware, Dubeyko has already published an early proof-of-concept implementation of the proposed ML library on GitHub and posted an RFC (Request for Comments) patch series to the Linux kernel mailing list. These patches are explicitly experimental, designed to spark discussion rather than demand immediate inclusion in the mainline kernel.

So, will Linux kernels soon be making smarter, ML-assisted decisions? That’s the million-dollar question. The patches haven’t been merged yet, and the community is still debating the merits and risks. But if this idea gains traction, it could mark a subtle yet profound shift in how Linux evolves—keeping human-written kernel logic as the foundation while letting machine learning serve as a powerful advisory layer.

For now, the proposal remains exploratory. Whether ML-assisted kernel subsystems will move beyond experimentation is anyone’s guess. But one thing’s for sure: the conversation around AI and Linux just got a lot more interesting.

Tags: Linux kernel, machine learning, ML proxy, kernel subsystems, IBM, Viacheslav Dubeyko, user space, sysfs, FUSE, eBPF, RFC, experimental, AI in kernel, kernel performance, feedback loop, GitHub, kernel mailing list, deterministic kernel, ML library

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