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