firecracker-containerd VS firecracker

My team is working on multi-cloud AWS Bottlerocket remix (Azure, GCP) with opt-in support for [firecracker-containerd](https://github.com/firecracker-microvm/firecracker-containerd) for our in-house CNCF distro, investigating microkernels applicability (tldr; they are not production-ready). We test kubernetes compat and migration plans for over 40+ cherry-picked solutions, and facing numerous compat issues for every k8s update. We do have support for Container Managed Control Planes described above, as well.

You could use a container sandbox like gVisor, light virtual machines as containers (Kata containers, firecracker + containerd) or full virtual machines (virtlet as a CRI).

There is this project, which I have never used, but seems promising. https://github.com/firecracker-microvm/firecracker-container...

, "should represent the path to a file that contains a JSON which stores the entire configuration for all of the microVM's resources" (okay this is fair enough). Also, they stipulate, "**The JSON must contain the configuration for the guest kernel and rootfs, as these are mandatory, but all of the other resources are optional, so it's your choice if you want to configure them or not. Because using this configuration method will also start the microVM, you need to specify all desired pre-boot configurable resources in that JSON.**" **File Names for the Pre-Boot Resources** (included within the greater repo here): 1. **firecracker.yaml** - Names of resources are contained here ; 'file nad the names of their fields are the same that are used in API requests' (cool) 2. **tests/framework/vm_config.json** (boilerplate config file to guide us - great) > *"After the machine is booted, you can still use the socket to send API requests for post-boot operations."* (this honestly feels clunky as a mf) ### Conclusion Somewhat of a pain in the ass (just looking through the directions); the fact that we'd have to go grab a uncompressed kernel image + file system image (ext4) is kind of a fucking hassle / burden. Was hoping for a solution more akin to Docker where it can just be spun up real quick & then deployed. But they claim that this 'jailer' feature (that they keep hyping) will **ensure** (I guess?) that whatever is done within the container will remain within the container (and not escape). I haven't seen anything that sticks out about this project that leads me to believe that it possesses that capability, but I definitely don't want to rule it out. ### Extra Documentation + Information 1. **OSv Running on 'Firecracker'** (yay more work though) - http://blog.osv.io/blog/2019/04/19/making-OSv-run-on-firecraker/ 2. **Building OSv Images Using Docker** - http://blog.osv.io/blog/2015/04/27/docker/ 3. **firecracker containerd** (this is something that's probably important for the overall mission of what we want to accomplish here) - https://github.com/firecracker-microvm/firecracker-containerd ### Firecracker Containerd **Description** - "*firecracker-containerd enables containerd to manage containers as Firecracker microVMs*" - "This repository enables the use of a container runtime, containerd, to manage Firecracker microVMs. Like traditional containers, Firecracker microVMs offer fast start-up and shut-down and minimal overhead. Unlike traditional containers, however, they can provide an additional layer of isolation via the KVM hypervisor." **They Also Identify Potential Use-Cases in the Repo Such as** 1. "*Sandbox a partially or fully untrusted third party container in its own microVM. This would reduce the likelihood of leaking secrets via the third party container, for example.*" 2. "*Bin-pack disparate container workloads on the same host, while maintaining a high level of isolation between containers. Because the overhead of Firecracker is low, the achievable container density per host should be comparable to running containers using kernel-based container runtimes, without the isolation compromise of such solutions. Multi-tenant hosts would particularly benefit from this use case.*" Really interesting feature of this repo here is: "*A root file filesystem image builder that constructs a firecracker microVM root filesystem containing runc and the firecracker-containerd agent.*" (that could save a lot of time on that whole filesystem image thing that they were mentioning prior) **Additional Links of Importance** 1. **Getting Started Guide** - https://github.com/firecracker-microvm/firecracker-containerd/blob/main/docs/getting-started.md 2. **Quickstart Guide** - https://github.com/firecracker-microvm/firecracker-containerd/blob/main/docs/quickstart.md 3. **A Root Filesystem Image Builder** - https://github.com/firecracker-microvm/firecracker-containerd/blob/main/tools/image-builder 4. **Runtime Linking Containerd** - https://github.com/firecracker-microvm/firecracker-containerd/blob/main/runtime **Documentation All Located Here** - https://github.com/firecracker-microvm/firecracker-containerd/tree/main/docs (definitely fucking needed because there's a lot here to wrap one's head around) - **Design Approaches Doc** - https://github.com/firecracker-microvm/firecracker-containerd/blob/main/docs/design-approaches.md - **Shim Architecture** - https://github.com/firecracker-microvm/firecracker-containerd/blob/main/docs/shim-design.md - **Launching 4k VMs Using Firecracker** - https://github.com/firecracker-microvm/firecracker-demo - **firectl** (CLI options for manipulating this tool from terminal ; this is important as well) - https://github.com/firecracker-microvm/firectl [damn, there's a lot that came with this here!]

Exactly, it is about secure multi-tennancy. If I recall correctly firecracker doesn't replace containerd, microVMs still runs some sort of it. Anyway, you still need a base OS because container doesn't have the whole OS image. Also I think you can have multiple containers in a single Fargate task so they have to be isolated too.

How does that compare to firecracker-containerd?

https://github.com/firecracker-microvm/firecracker-container...

This repository enables the use of a container runtime, containerd, to manage Firecracker microVMs. Like traditional containers, Firecracker microVMs offer fast start-up and shut-down and minimal overhead. Unlike traditional containers, however, they can provide an additional layer of isolation via the KVM hypervisor.

I'm really impressed by fly.io, and the candidness with which they share some of their really awesome technology. Being container-first is the next step for PaaS IMO and they are ahead of the pack.

I aim to build a platform like theirs someday (probably not any time soon) but I don't think I'd do any of what they're doing -- it feels unnecessary. Bear with me as I recently learned that they use nomad[0] and some of these suggestions are kubernetes projects but I'd love to hear why the following technologies were decided against (if they were):

- kata-containers[1] (it does the whole container -> VM flow for you, automatically, nemu, firecracker) with multiple VMM options[2]

- linuxkit[3] (let's say you didn't go with kata-containers, this is another container->VM path)

- firecracker-containerd[4] (very minimal keep-your-container-but-run-it-as-a-VM)

- kubevirt[5] (if you just want to actually run VMs, regardless of how you built them)

- Ceph[6] for storage -- make LVM pools and just give them to Ceph, you'll get blocks, distributed filesystems (CephFS), and object gateways (S3/Swift) out of it (in the k8s space Rook manages this)

As an aside to all this, there's also LXD, which supports running "system" (user namespace isolated) containers, VMs (somewhat recent[7][8]), live migration via criu[9], management/migration of underlying filesystems, runs on LVM or zfs[10], it's basically all-in-one, but does fall behind in terms of ecosystem since everyone else is aboard the "cloud native"/"works-with-kubernetes" train.

I've basically how I plan to run a service like fly.io if I ever did -- so maybe my secret is out, but I sure would like to know just how much of this fly.io got built on (if any of it), and/or what was turned down.

[0]: https://news.ycombinator.com/item?id=26745514

[1]: https://github.com/kata-containers/kata-containers

[2]: https://github.com/kata-containers/kata-containers/blob/2fc7...

[3]: https://github.com/linuxkit/linuxkit

[4]: https://github.com/firecracker-microvm/firecracker-container...

[5]: https://github.com/kubevirt/kubevirt

[6]: https://docs.ceph.com/

[7]: https://discuss.linuxcontainers.org/t/running-virtual-machin...

[8]: https://github.com/lxc/lxd/issues/6205

[9]: https://criu.org/Main_Page

[10]: https://linuxcontainers.org/lxd/docs/master/storage

https://github.com/firecracker-microvm/firecracker-container...

This architecture leverages microVMs for rapid scaling and high-density workloads. But does it work for GPU? The answer is no. You can look at the old 2019 GitHub issue and the comments to it to get the bigger picture of why it is so.

Hi, I'm the CEO of the company that built this SDK.

We're a company called E2B [0]. We're building and open-source [1] secure environments for running untrusted AI-generated code and AI agents. We call these environments sandboxes and they are built on top of micro VM called Firecracker [2].

You can think of us as giving small cloud computers to LLMs.

We recently created a dedicated SDK for building custom code interpreters in Python or JS/TS. We saw this need after a lot of our users have been adding code execution capabilities to their AI apps with our core SDK [3]. These use cases were often centered around AI data analysis so code interpreter-like behavior made sense

The way our code interpret SDK works is by spawning an E2B sandbox with Jupyter Server. We then communicate with this Jupyter server through Jupyter Kernel messaging protocol [4].

We don't do any wrapping around LLM, any prompting, or any agent-like framework. We leave all of that on users. We're really just a boring code execution layer that sats at the bottom that we're building specifically for the future software that will be building another software. We work with any LLM. Here's how we added code interpreter to Claude [5].

Our long-term plan is to build an automated AWS for AI apps and agents.

Happy to answer any questions and hear feedback!

[0] https://e2b.dev/

[1] https://github.com/e2b-dev

[2] https://github.com/firecracker-microvm/firecracker

[3] https://e2b.dev/docs

[4] https://jupyter-client.readthedocs.io/en/latest/messaging.ht...

[5] https://github.com/e2b-dev/e2b-cookbook/blob/main/examples/c...

As far as I know, Fly uses Firecracker for their VMs. I've been following Firecracker for a while now (even using it in a project), and they don't support GPUs out of the box (and have no plan to support it [1]).

I'm curious to know how Fly figured their own GPU support with Firecracker. In the past they had some very detailed technical posts on how they achieved certain things, so I'm hoping we'll see one on their GPU support in the future!

[1]: https://github.com/firecracker-microvm/firecracker/issues/11...

I pass through a GPU and USB hub to a VM running on a machine in the garage. An optical video cable and network compatible USB extender brings the interface to a different room making it my primary “desktop” computer (and an outdated laptop as a backup device). Doesn’t get more silent and cool than this. Another VM on the garage machine gets a bunch of hard drives passed through to it.

That said, hardware passthrough/VFIO is likely out of the current realistic scope for this project. VM boot times can be optimized if you never look for hardware to initialize in the first place. Though they are still likely initializing a network interface of some sort.

“MicroVM” seems to be a term used when as much as possible is stripped from a VM, such as with https://github.com/firecracker-microvm/firecracker

According to their own FAQ it is indeed: https://github.com/firecracker-microvm/firecracker/blob/main...

What about microVMs like firecracker?

Dynamic memory management - Firecracker's RAM footprint starts low, but once a workload inside allocates RAM, Firecracker will never return it to the host system. After running several workloads inside, you end up with an idling VM that consumes 32 GB of RAM on the host, even though it doesn't need any of it.

Firecracker has a balloon device you can inflate (ie: acquire as much memory inside the VM as possible) and then deflate... returning the memory to the host.

https://github.com/firecracker-microvm/firecracker/blob/main...

Very few things in those companies are being written in Rust, and half of those projects chose Rust around ideological reasons rather than technical, with plenty of 'unsafe' thrown in for performance reasons

https://github.com/firecracker-microvm/firecracker/search?q=...

The fact that 'unsafe' even exists in Rust means it's no better than C with some macros.

Don't get me wrong, Rust has it's place, like all the other languages that came about for various reasons, but it's not going to gain wide adoption.

Future of programming consists of 2 languages - something like C that has a small instruction set for adopting to new hardware, and something that is very high level, higher than Python with LLM in the background. Everything in the middle is fodder.

https://github.com/firecracker-microvm/firecracker is the one that comes to mind, but most of these are internal.