linux-kernel - RE: [LSF/MM/BPF TOPIC] Machine Learning (ML) library in Linux kernel

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Message-ID: <83e395c84c9bfa52f1abccf12ff6d39547d6bede.camel@ibm.com>
Date: Mon, 9 Feb 2026 22:07:39 +0000
From: Viacheslav Dubeyko <Slava.Dubeyko@....com>
To: "21cnbao@...il.com" <21cnbao@...il.com>
CC: "linux-mm@...ck.org" <linux-mm@...ck.org>,
        Pavan Rallabhandi
	<Pavan.Rallabhandi@....com>,
        "linux-fsdevel@...r.kernel.org"
	<linux-fsdevel@...r.kernel.org>,
        "linux-kernel@...r.kernel.org"
	<linux-kernel@...r.kernel.org>,
        "lsf-pc@...ts.linux-foundation.org"
	<lsf-pc@...ts.linux-foundation.org>,
        "bpf@...r.kernel.org"
	<bpf@...r.kernel.org>
Subject: RE: [LSF/MM/BPF TOPIC] Machine Learning (ML) library in Linux kernel

Hi Barry,

On Mon, 2026-02-09 at 18:25 +0800, Barry Song wrote:
> On Sat, Feb 7, 2026 at 3:40 AM Viacheslav Dubeyko <Slava.Dubeyko@....com> wrote:
> > 
> > Hello,
> > 
> [...]
> > 
> > The continuous learning model can be adopted during training phase.
> > It implies that kernel subsystem can receive ML model recommendations
> > even during training phase. ML model proxy on kernel side can estimate
> > the current kernel subsystem state, tries to apply the ML model
> > recommendations, and estimate the efficiency of applied recommendations.
> > Generally speaking, ML model proxy on kernel side can consider several
> > modes of interaction with ML model recommendations: (1) emergency mode,
> > (2) learning mode, (3) collaboration mode, (4) recommendation mode.
> > The emergency mode is the mode when kernel subsystem is in critical state
> > and it is required to work as efficient as possible without capability of
> > involving the ML model recommendations (for example, ML model
> > recommendations are completely inadequate or load is very high).
> > The learning mode implies that kernel subsystem can try to apply
> > the ML model recommendations for some operations with the goal of
> > estimation the maturity of ML model. Also, ML model proxy can degrade
> > the mode to learning state if ML model recommendations becomes inefficient.
> > The collaboration mode has the goal of using ML recommendations in
> > 50% of operations with the goal of achieving mature state of ML model.
> > And, finally, ML model proxy can convert kernel subsystem in recommendation
> > mode if ML model is mature enough and efficiency of applying
> > the ML recommendations is higher than using human-made algorithms.
> 
> Hi Slava,
> 
> Do we have any concrete examples where an ML-based proxy,
> together with its userspace ML agent, has demonstrated
> measurable performance improvements over well-designed,
> human-crafted kernel algorithms?
> 
> Such examples could be in scheduling, filesystem I/O, or memory
> reclamation and readahead. I think having a real, data-backed
> example would be much more helpful for this discussion than
> reasoning about an abstract framework without a concrete use
> case.
> 

This patchset [1] is the first step of declaring the ML library API with the
goal of discussing it. As the next step, I am considering of using ML library
API for implementing two real-life use-cases: (1) GC subsystem of LFS file
systems (NILFS2, F2FS, SSDFS), (2) ML-based DAMON approach. I see multiple
potential real-life use-cases of ML library. But let me start from these two
ones and, then, we will able to extend the approach for other use-cases. The
goal of this talk is to hear the opinion of the community and to elaborate the
proper vision of ML library architecture.

Thanks,
Slava.

[1]
https://lore.kernel.org/linux-fsdevel/20260206191136.2609767-1-slava@dubeyko.com/T/#t