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

Cong Wang <[email protected]> Mon, 22 Sep 2025 14:55:41 -0700
Newsgroups dev.linux.lists.multikernel,org.kernel.vger.linux-kernel,org.kvack.linux-mm
Message-ID <CAM_iQpXMjFs4LmpRVNqxeBnFp4KxEfgi9cB_Jwuy7VgOSqTfsQ@mail.gmail.com>
On Sat, Sep 20, 2025 at 6:47=E2=80=AFPM Hillf Danton <[email protected]> wro=
te:
>
> 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 case=
s
today, just with pure better performance: no virtualization, no noisy neigh=
bor.

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

2) Active-backup kernel for mission-critical tasks: after the primary kerne=
l
crashes, a backup kernel in parallel immediately takes over without interru=
pting
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 everyt=
hing
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