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

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Message-ID: <3d3bed663019bf93c0f26baf68654568ce8d1935.camel@ibm.com>
Date: Tue, 10 Feb 2026 19:57:31 +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

On Tue, 2026-02-10 at 11:06 +0800, Barry Song wrote:
> On Tue, Feb 10, 2026 at 6:07 AM Viacheslav Dubeyko
> <Slava.Dubeyko@....com> wrote:
> > 
> > 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.
> 
> I’m very interested in your real-world use case.
> If you have any early-stage prototype code that demonstrates the full
> flow from user space to kernel space—including both the kernel ML proxy
> and the user-space ML agent (for example, for filesystem garbage
> collection)—I’d be glad to take a look if you’re able to share it.
> 
> 

I am going to extend for real-life use-case the early-stage prototype code [1].
The [2] is the Linux kernel with integrated ML library. And [3] is patchset that
I've shared recently of this early-stage prototype code.

It will be great to hear your opinion. :)

Thanks,
Slava.

[1] https://github.com/kernel-ml-lib/ml-lib
[2] https://github.com/kernel-ml-lib/ml-lib-linux
[3]
https://lore.kernel.org/linux-fsdevel/20260206191136.2609767-1-slava@dubeyko.com/T/#t