linux-kernel - Re: [RFC Patch 0/7] kernel: Introduce multikernel architecture support

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Message-ID: <CAM_iQpXMjFs4LmpRVNqxeBnFp4KxEfgi9cB_Jwuy7VgOSqTfsQ@mail.gmail.com>
Date: Mon, 22 Sep 2025 14:55:41 -0700
From: Cong Wang <xiyou.wangcong@...il.com>
To: Hillf Danton <hdanton@...a.com>
Cc: linux-kernel@...r.kernel.org, linux-mm@...ck.org, 
	multikernel@...ts.linux.dev
Subject: Re: [RFC Patch 0/7] kernel: Introduce multikernel architecture support

On Sat, Sep 20, 2025 at 6:47 PM Hillf Danton <hdanton@...a.com> wrote:
>
> On Thu, 18 Sep 2025 15:25:59 -0700 Cong Wang wrote:
> > This patch series introduces multikernel architecture support, enabling
> > multiple independent kernel instances to coexist and communicate on a
> > single physical machine. Each kernel instance can run on dedicated CPU
> > cores while sharing the underlying hardware resources.
> >
> > The multikernel architecture provides several key benefits:
> > - Improved fault isolation between different workloads
> > - Enhanced security through kernel-level separation
> > - Better resource utilization than traditional VM (KVM, Xen etc.)
> > - Potential zero-down kernel update with KHO (Kernel Hand Over)
> >
> Could you illustrate a couple of use cases to help understand your idea?

Sure, below are a few use cases on my mind:

1) With sufficient hardware resources: each kernel gets isolated resources
with real bare metal performance. This applies to all VM/container use cases
today, just with pure better performance: no virtualization, no noisy neighbor.

More importantly, they can co-exist. In theory, you can run a multiernel with
a VM inside and with a container inside the VM.

2) Active-backup kernel for mission-critical tasks: after the primary kernel
crashes, a backup kernel in parallel immediately takes over without interrupting
the user-space task.

Dual-kernel systems are very common for automotives today.

3) Getting rid of the OS to reduce the attack surface. We could pack everything
properly in an initramfs and run it directly without bothering a full
OS. This is
similar to what unikernels or macro VM's do today.

4) Machine learning in the kernel. Machine learning is too specific to
workloads,
for instance, mixing real-time scheduling and non-RT can be challenging for
ML to tune the CPU scheduler, which is an essential multi-goal learning.

5) Per-application specialized kernel: For example, running a RT kernel
and non-RT kernel in parallel. Memory footprint can also be reduced by
reducing the 5-level paging tables when necessary.

I hope this helps.

Regards,
Cong