Real-time container runtime support
an invention by a_faerber
Updated almost 2 years ago. 1 hackers ♥️. 3 followers.

Project Description

As per discussions in the SOAFEE SIG that SUSE is a founding member of, container users will be in need of running workloads with mixed criticality.

Maybe the easiest starting point will be allowing to assign containerized processes real-time priorities.

During last Hack Week, code review had confirmed no process priorities were being set in runc, but work towards experimental code changes got interrupted.

Goal for this Hackweek

Goal is to create a proof of concept where initially a hardcoded process priority gets assigned to a container (which would confirm we found the right place and have the needed capability permissions). This includes figuring out a development set-up for these container components. SUCCESS! Nice values such as -5 (range -20 to 19) could be assigned to a Tumbleweed container executed via podman on Tumbleweed x86_64, using a modified locally built and installed (PREFIX=/usr) runc binary in the initProcess code path.

Next step would be to alternatively assign a real-time process priority (different syscall and number range). SUCCESS! Among others, FIFO scheduler with real-time priority 42 (range 1 to 99) could be assigned to the Tumbleweed container's bash process.

A further step would be figuring out how to pass such meta information from container manifest through orchestrator to the runtime components, so that the priority does not need to be hardcoded and can be applied to one specific container only.

Out of scope will likely be investigating alternative container components, such as crun in place of runc.

It is understood real-time process priorities can be investigated on regular current Tumbleweed or SLE kernels, without requiring a SLERT kernel with PREEMPT_RT patchset specifically (although that would still be the deployment use case).

Resources

SUSE Labs Conference 2022 paper "SOAFEE: The quest for mixed criticality" by A. Färber, sections "Operating system and real-time" and "Kubernetes and real-time".

Looking for hackers with the skills:

kubernetes containers runc go k3s

This project is part of:

Hack Week 22 Hack Week 21

Activity

  • almost 2 years ago: joachimwerner liked this project.
  • almost 2 years ago: a_faerber added keyword "k3s" to this project.
  • almost 2 years ago: a_faerber added keyword "go" to this project.
  • almost 2 years ago: a_faerber added keyword "kubernetes" to this project.
  • almost 2 years ago: a_faerber added keyword "containers" to this project.
  • almost 2 years ago: a_faerber added keyword "runc" to this project.
  • almost 2 years ago: a_faerber started this project.
  • almost 2 years ago: a_faerber originated this project.


    • Comments

    • afaerber
      almost 2 years ago by afaerber | Reply

      Results presented in SOAFEE MCO tiger team: 20230214_SUSE_Hackweek_real-time.pdf

      Code is pushed to GitHub now: https://github.com/afaerber/runc/commits/hackweek22

    Similar Projects

    SUSE AI Meets the Game Board by moio

    Use tabletopgames.ai’s open source TAG and PyTAG frameworks to apply Statistical Forward Planning and Deep Reinforcement Learning to two board games of our own design. On an all-green, all-open source, all-AWS stack!
    A chameleon playing chess in a train car, as a metaphor of SUSE AI applied to games

    Results: Infrastructure Achievements

    We successfully built and automated a containerized stack to support our AI experiments. This included:

    A screenshot of k9s and nvtop showing PyTAG running in Kubernetes with GPU acceleration

    ./deploy.sh and voilà - Kubernetes running PyTAG (k9s, above) with GPU acceleration (nvtop, below)

    Results: Game Design Insights

    Our project focused on modeling and analyzing two card games of our own design within the TAG framework:

    • Game Modeling: We implemented models for Dario's "Bamboo" and Silvio's "Totoro" and "R3" games, enabling AI agents to play thousands of games ...in minutes!
    • AI-driven optimization: By analyzing statistical data on moves, strategies, and outcomes, we iteratively tweaked the game mechanics and rules to achieve better balance and player engagement.
    • Advanced analytics: Leveraging AI agents with Monte Carlo Tree Search (MCTS) and random action selection, we compared performance metrics to identify optimal strategies and uncover opportunities for game refinement .

    Cards from the three games

    A family picture of our card games in progress. From the top: Bamboo, Totoro, R3

    Results: Learning, Collaboration, and Innovation

    Beyond technical accomplishments, the project showcased innovative approaches to coding, learning, and teamwork:

    • "Trio programming" with AI assistance: Our "trio programming" approach—two developers and GitHub Copilot—was a standout success, especially in handling slightly-repetitive but not-quite-exactly-copypaste tasks. Java as a language tends to be verbose and we found it to be fitting particularly well.
    • AI tools for reporting and documentation: We extensively used AI chatbots to streamline writing and reporting. (Including writing this report! ...but this note was added manually during edit!)
    • GPU compute expertise: Overcoming challenges with CUDA drivers and cloud infrastructure deepened our understanding of GPU-accelerated workloads in the open-source ecosystem.
    • Game design as a learning platform: By blending AI techniques with creative game design, we learned not only about AI strategies but also about making games fun, engaging, and balanced.

    Last but not least we had a lot of fun! ...and this was definitely not a chatbot generated line!

    The Context: AI + Board Games

    Rancher/k8s Trouble-Maker by tonyhansen

    Project Description

    When studying for my RHCSA, I found trouble-maker, which is a program that breaks a Linux OS and requires you to fix it. I want to create something similar for Rancher/k8s that can allow for troubleshooting an unknown environment.

    Goal for this Hackweek

    Create a basic framework for creating Rancher/k8s cluster lab environments as needed for the Break/Fix Create at least 5 modules that can be applied to the cluster and require troubleshooting

    Resources

    https://github.com/rancher/terraform-provider-rancher2 https://github.com/rancher/tf-rancher-up

    Introducing "Bottles": A Proof of Concept for Multi-Version CRD Management in Kubernetes by aruiz

    Description

    As we delve deeper into the complexities of managing multiple CRD versions within a single Kubernetes cluster, I want to introduce "Bottles" - a proof of concept that aims to address these challenges.

    Bottles propose a novel approach to isolating and deploying different CRD versions in a self-contained environment. This would allow for greater flexibility and efficiency in managing diverse workloads.

    Goals

    • Evaluate Feasibility: determine if this approach is technically viable, as well as identifying possible obstacles and limitations.
    • Reuse existing technology: leverage existing products whenever possible, e.g. build on top of Kubewarden as admission controller.
    • Focus on Rancher's use case: the ultimate goal is to be able to use this approach to solve Rancher users' needs.

    Resources

    Core concepts:

    • ConfigMaps: Bottles could be defined and configured using ConfigMaps.
    • Admission Controller: An admission controller will detect "bootled" CRDs being installed and replace the resource name used to store them.
    • Aggregated API Server: By analyzing the author of a request, the aggregated API server will determine the correct bottle and route the request accordingly, making it transparent for the user.

    Setup Kanidm as OIDC provider on Kubernetes by jkuzilek

    Description

    I am planning to upgrade my homelab Kubernetes cluster to the next level and need an OIDC provider for my services, including K8s itself.

    Goals

    • Successfully configure and deploy Kanidm on homelab cluster
    • Integrate with K8s auth
    • Integrate with other services (Envoy Gateway, Container Registry, future deployment of Forgejo?)

    Resources

    Harvester Packer Plugin by mrohrich

    Description

    Hashicorp Packer is an automation tool that allows automatic customized VM image builds - assuming the user has a virtualization tool at their disposal. To make use of Harvester as such a virtualization tool a plugin for Packer needs to be written. With this plugin users could make use of their Harvester cluster to build customized VM images, something they likely want to do if they have a Harvester cluster.

    Goals

    Write a Packer plugin bridging the gap between Harvester and Packer. Users should be able to create customized VM images using Packer and Harvester with no need to utilize another virtualization platform.

    Resources

    Hashicorp documentation for building custom plugins for Packer https://developer.hashicorp.com/packer/docs/plugins/creation/custom-builders

    Source repository of the Harvester Packer plugin https://github.com/m-ildefons/harvester-packer-plugin

    Enable the containerized Uyuni server to run on different host OS by j_renner

    Description

    The Uyuni server is provided as a container, but we still require it to run on Leap Micro? This is not how people expect to use containerized applications, so it would be great if we tested other host OSs and enabled them by providing builds of necessary tools for (e.g. mgradm). Interesting candidates should be:

    • openSUSE Leap
    • Cent OS 7
    • Ubuntu
    • ???

    Goals

    Make it really easy for anyone to run the Uyuni containerized server on whatever OS they want (with support for containers of course).

    Technical talks at universities by agamez

    Description

    This project aims to empower the next generation of tech professionals by offering hands-on workshops on containerization and Kubernetes, with a strong focus on open-source technologies. By providing practical experience with these cutting-edge tools and fostering a deep understanding of open-source principles, we aim to bridge the gap between academia and industry.

    For now, the scope is limited to Spanish universities, since we already have the contacts and have started some conversations.

    Goals

    • Technical Skill Development: equip students with the fundamental knowledge and skills to build, deploy, and manage containerized applications using open-source tools like Kubernetes.
    • Open-Source Mindset: foster a passion for open-source software, encouraging students to contribute to open-source projects and collaborate with the global developer community.
    • Career Readiness: prepare students for industry-relevant roles by exposing them to real-world use cases, best practices, and open-source in companies.

    Resources

    • Instructors: experienced open-source professionals with deep knowledge of containerization and Kubernetes.
    • SUSE Expertise: leverage SUSE's expertise in open-source technologies to provide insights into industry trends and best practices.

    ClusterOps - Easily install and manage your personal kubernetes cluster by andreabenini

    Description

    ClusterOps is a Kubernetes installer and operator designed to streamline the initial configuration and ongoing maintenance of kubernetes clusters. The focus of this project is primarily on personal or local installations. However, the goal is to expand its use to encompass all installations of Kubernetes for local development purposes.
    It simplifies cluster management by automating tasks and providing just one user-friendly YAML-based configuration config.yml.

    Overview

    • Simplified Configuration: Define your desired cluster state in a simple YAML file, and ClusterOps will handle the rest.
    • Automated Setup: Automates initial cluster configuration, including network settings, storage provisioning, special requirements (for example GPUs) and essential components installation.
    • Ongoing Maintenance: Performs routine maintenance tasks such as upgrades, security updates, and resource monitoring.
    • Extensibility: Easily extend functionality with custom plugins and configurations.
    • Self-Healing: Detects and recovers from common cluster issues, ensuring stability, idempotence and reliability. Same operation can be performed multiple times without changing the result.
    • Discreet: It works only on what it knows, if you are manually configuring parts of your kubernetes and this configuration does not interfere with it you can happily continue to work on several parts and use this tool only for what is needed.

    Features

    • distribution and engine independence. Install your favorite kubernetes engine with your package manager, execute one script and you'll have a complete working environment at your disposal.
    • Basic config approach. One single config.yml file with configuration requirements (add/remove features): human readable, plain and simple. All fancy configs managed automatically (ingress, balancers, services, proxy, ...).
    • Local Builtin ContainerHub. The default installation provides a fully configured ContainerHub available locally along with the kubernetes installation. This configuration allows the user to build, upload and deploy custom container images as they were provided from external sources. Internet public sources are still available but local development can be kept in this localhost server. Builtin ClusterOps operator will be fetched from this ContainerHub registry too.
    • Kubernetes official dashboard installed as a plugin, others planned too (k9s for example).
    • Kubevirt plugin installed and properly configured. Unleash the power of classic virtualization (KVM+QEMU) on top of Kubernetes and manage your entire system from there, libvirtd and virsh libs are required.
    • One operator to rule them all. The installation script configures your machine automatically during installation and adds one kubernetes operator to manage your local cluster. From there the operator takes care of the cluster on your behalf.
    • Clean installation and removal. Just test it, when you are done just use the same program to uninstall everything without leaving configs (or pods) behind.

    Planned features (Wishlist / TODOs)

    • Containerized Data Importer (CDI). Persistent storage management add-on for Kubernetes to provide a declarative way of building and importing Virtual Machine Disks on PVCs for

    ADS-B receiver with MicroOS by epaolantonio

    I would like to put one of my spare Raspberry Pis to good use, and what better way to see what flies above my head at any time? add-emoji

    There are various ready-to-use distros already set-up to provide feeder data to platforms like Flightradar24, ADS-B Exchange, FlightAware etc... The goal here would be to do it using MicroOS as a base and containerized decoding of ADS-B data (via tools like dump1090) and web frontend (tar1090).

    Goals

    • Create a working receiver using MicroOS as a base, and containers based on Tumbleweed
    • Make it easy to install
    • Optimize for maximum laziness (i.e. it should take care of itself with minimum intervention)

    Resources

    • 1x Small Board Computer capable of running MicroOS
    • 1x RTL2832U DVB-T dongle
    • 1x MicroSD card
    • https://github.com/antirez/dump1090
    • https://github.com/flightaware/dump1090 (dump1090 fork by FlightAware)
    • https://github.com/wiedehopf/tar1090

    Project status (2024-11-22)

    So I'd say that I'm pretty satisfied with how it turned out. I've packaged readsb (as a replacement for dump1090), tar1090, tar1090-db and mlat-client (not used yet).

    Current status:

    • Able to set-up a working receiver using combustion+ignition (web app based on Fuel Ignition)
    • Able to feed to various feeds using the Beast protocol (Airplanes.live, ADSB.fi, ADSB.lol, ADSBExchange.com, Flyitalyadsb.com, Planespotters.net)
    • Able to feed to Flightradar24 (initial-setup available but NOT tested! I've only tested using a key I already had)
    • Local web interface (tar1090) to easily visualize the results
    • Cockpit pre-configured to ease maintenance

    What's missing:

    • MLAT (Multilateration) support. I've packaged mlat-client already, but I have to wire it up
    • FlightAware support

    Give it a go at https://g7.github.io/adsbreceiver/ !

    Project links

    SUSE AI Meets the Game Board by moio

    Use tabletopgames.ai’s open source TAG and PyTAG frameworks to apply Statistical Forward Planning and Deep Reinforcement Learning to two board games of our own design. On an all-green, all-open source, all-AWS stack!
    A chameleon playing chess in a train car, as a metaphor of SUSE AI applied to games

    Results: Infrastructure Achievements

    We successfully built and automated a containerized stack to support our AI experiments. This included:

    A screenshot of k9s and nvtop showing PyTAG running in Kubernetes with GPU acceleration

    ./deploy.sh and voilà - Kubernetes running PyTAG (k9s, above) with GPU acceleration (nvtop, below)

    Results: Game Design Insights

    Our project focused on modeling and analyzing two card games of our own design within the TAG framework:

    • Game Modeling: We implemented models for Dario's "Bamboo" and Silvio's "Totoro" and "R3" games, enabling AI agents to play thousands of games ...in minutes!
    • AI-driven optimization: By analyzing statistical data on moves, strategies, and outcomes, we iteratively tweaked the game mechanics and rules to achieve better balance and player engagement.
    • Advanced analytics: Leveraging AI agents with Monte Carlo Tree Search (MCTS) and random action selection, we compared performance metrics to identify optimal strategies and uncover opportunities for game refinement .

    Cards from the three games

    A family picture of our card games in progress. From the top: Bamboo, Totoro, R3

    Results: Learning, Collaboration, and Innovation

    Beyond technical accomplishments, the project showcased innovative approaches to coding, learning, and teamwork:

    • "Trio programming" with AI assistance: Our "trio programming" approach—two developers and GitHub Copilot—was a standout success, especially in handling slightly-repetitive but not-quite-exactly-copypaste tasks. Java as a language tends to be verbose and we found it to be fitting particularly well.
    • AI tools for reporting and documentation: We extensively used AI chatbots to streamline writing and reporting. (Including writing this report! ...but this note was added manually during edit!)
    • GPU compute expertise: Overcoming challenges with CUDA drivers and cloud infrastructure deepened our understanding of GPU-accelerated workloads in the open-source ecosystem.
    • Game design as a learning platform: By blending AI techniques with creative game design, we learned not only about AI strategies but also about making games fun, engaging, and balanced.

    Last but not least we had a lot of fun! ...and this was definitely not a chatbot generated line!

    The Context: AI + Board Games

    Hack on rich terminal user interfaces by amanzini

    Description

    TUIs (Textual User Interface) are a big classic of our daily workflow. Many linux users 'live' in the terminal and modern implementations have a lot to offer : unicode fonts, 24 bit colors etc.

    Goals

    • Explore the current available solution on modern languages and implement a PoC , for example a small maze generator, porting of a classic game or just display the HackWeek cute logo.
    • Practice some Go / Rust coding and programming patterns
    • Fiddle around, hack, learn, have fun
    • keep a development diary, practice on project documentation

    Follow this link for source code repository

    Some ideas for inspiration:

    Related projects:

    Resources

    WebUI for your data by avicenzi

    A single place to view every bit of data you have.

    Problem

    You have too much data and you are a data hoarder.

    • Family photos and videos.
    • Lots of eBooks, TV Shows, Movies, and else.
    • Boxes full of papers (taxes, invoices, IDs, certificates, exams, and else).
    • Bank account statements (multiple currencies, countries, and people).

    Maybe you have some data on S3, some on your NAS, and some on your local PC.

    • How do you get it all together?
    • How do you link a bank transaction to a product invoice?
    • How to tag any object type and create a collection out of it (mix videos, photos, PDFs, transactions)?
    • How to store this? file/folder structure does not work, everything is linked together

    Project Description

    The idea is a place where you can throw all your data, photos, videos, documents, binaries, and else.

    Create photo albums, document collections, add tags across multiple file-formats, link content, and else.

    The UI should be easy to use, where the data is not important for now (could be all S3 or local drive).

    Similar proposals

    The closest I found so far is https://perkeep.org/, but this is not what I'm looking for.

    Goal for this Hackweek

    Create a web UI, in Svelte ideally, perhaps React.

    It should be able to show photos and videos at least.

    Resources

    None so far, this is just an idea.

    Jenny Static Site Generator by adam.pickering

    Description

    For my personal site I have been using hugo. It works, but I am not satisfied: every time I want to make a change (which is infrequently) I have to read through the documentation again to understand how hugo works. I don't find the documentation easy to use, and the structure of the repository that hugo requires is unintuitive/more complex than what I need. So, I have decided to write my own simple static site generator in Go. It is named Jenny, after my wife.

    Goals

    • Pages can be written in markdown (which is automatically converted to HTML), but other file types are also allowed
    • Easy to understand and use
      • Intuitive, simple design
      • Clear documentation
      • Hot reloading
      • Binaries provided for download
    • Future maintenance is easy
      • Automated releases

    Resources

    https://github.com/adamkpickering/jenny

    ClusterOps - Easily install and manage your personal kubernetes cluster by andreabenini

    Description

    ClusterOps is a Kubernetes installer and operator designed to streamline the initial configuration and ongoing maintenance of kubernetes clusters. The focus of this project is primarily on personal or local installations. However, the goal is to expand its use to encompass all installations of Kubernetes for local development purposes.
    It simplifies cluster management by automating tasks and providing just one user-friendly YAML-based configuration config.yml.

    Overview

    • Simplified Configuration: Define your desired cluster state in a simple YAML file, and ClusterOps will handle the rest.
    • Automated Setup: Automates initial cluster configuration, including network settings, storage provisioning, special requirements (for example GPUs) and essential components installation.
    • Ongoing Maintenance: Performs routine maintenance tasks such as upgrades, security updates, and resource monitoring.
    • Extensibility: Easily extend functionality with custom plugins and configurations.
    • Self-Healing: Detects and recovers from common cluster issues, ensuring stability, idempotence and reliability. Same operation can be performed multiple times without changing the result.
    • Discreet: It works only on what it knows, if you are manually configuring parts of your kubernetes and this configuration does not interfere with it you can happily continue to work on several parts and use this tool only for what is needed.

    Features

    • distribution and engine independence. Install your favorite kubernetes engine with your package manager, execute one script and you'll have a complete working environment at your disposal.
    • Basic config approach. One single config.yml file with configuration requirements (add/remove features): human readable, plain and simple. All fancy configs managed automatically (ingress, balancers, services, proxy, ...).
    • Local Builtin ContainerHub. The default installation provides a fully configured ContainerHub available locally along with the kubernetes installation. This configuration allows the user to build, upload and deploy custom container images as they were provided from external sources. Internet public sources are still available but local development can be kept in this localhost server. Builtin ClusterOps operator will be fetched from this ContainerHub registry too.
    • Kubernetes official dashboard installed as a plugin, others planned too (k9s for example).
    • Kubevirt plugin installed and properly configured. Unleash the power of classic virtualization (KVM+QEMU) on top of Kubernetes and manage your entire system from there, libvirtd and virsh libs are required.
    • One operator to rule them all. The installation script configures your machine automatically during installation and adds one kubernetes operator to manage your local cluster. From there the operator takes care of the cluster on your behalf.
    • Clean installation and removal. Just test it, when you are done just use the same program to uninstall everything without leaving configs (or pods) behind.

    Planned features (Wishlist / TODOs)

    • Containerized Data Importer (CDI). Persistent storage management add-on for Kubernetes to provide a declarative way of building and importing Virtual Machine Disks on PVCs for

    Cluster API Provider for Harvester by rcase

    Project Description

    The Cluster API "infrastructure provider" for Harvester, also named CAPHV, makes it possible to use Harvester with Cluster API. This enables people and organisations to create Kubernetes clusters running on VMs created by Harvester using a declarative spec.

    The project has been bootstrapped in HackWeek 23, and its code is available here.

    Work done in HackWeek 2023

    • Have a early working version of the provider available on Rancher Sandbox : *DONE *
    • Demonstrated the created cluster can be imported using Rancher Turtles: DONE
    • Stretch goal - demonstrate using the new provider with CAPRKE2: DONE and the templates are available on the repo

    Goals for HackWeek 2024

    • Add support for ClusterClass
    • Add e2e testing
    • Add more Unit Tests
    • Improve Status Conditions to reflect current state of Infrastructure
    • Improve CI (some bugs for release creation)
    • Testing with newer Harvester version (v1.3.X and v1.4.X)
    • Due to the length and complexity of the templates, maybe package some of them as Helm Charts.
    • Other improvement suggestions are welcome!

    DONE in HackWeek 24:

    Thanks to @isim and Dominic Giebert for their contributions!

    Resources

    Looking for help from anyone interested in Cluster API (CAPI) or who wants to learn more about Harvester.

    This will be an infrastructure provider for Cluster API. Some background reading for the CAPI aspect:

    ClusterOps - Easily install and manage your personal kubernetes cluster by andreabenini

    Description

    ClusterOps is a Kubernetes installer and operator designed to streamline the initial configuration and ongoing maintenance of kubernetes clusters. The focus of this project is primarily on personal or local installations. However, the goal is to expand its use to encompass all installations of Kubernetes for local development purposes.
    It simplifies cluster management by automating tasks and providing just one user-friendly YAML-based configuration config.yml.

    Overview

    • Simplified Configuration: Define your desired cluster state in a simple YAML file, and ClusterOps will handle the rest.
    • Automated Setup: Automates initial cluster configuration, including network settings, storage provisioning, special requirements (for example GPUs) and essential components installation.
    • Ongoing Maintenance: Performs routine maintenance tasks such as upgrades, security updates, and resource monitoring.
    • Extensibility: Easily extend functionality with custom plugins and configurations.
    • Self-Healing: Detects and recovers from common cluster issues, ensuring stability, idempotence and reliability. Same operation can be performed multiple times without changing the result.
    • Discreet: It works only on what it knows, if you are manually configuring parts of your kubernetes and this configuration does not interfere with it you can happily continue to work on several parts and use this tool only for what is needed.

    Features

    • distribution and engine independence. Install your favorite kubernetes engine with your package manager, execute one script and you'll have a complete working environment at your disposal.
    • Basic config approach. One single config.yml file with configuration requirements (add/remove features): human readable, plain and simple. All fancy configs managed automatically (ingress, balancers, services, proxy, ...).
    • Local Builtin ContainerHub. The default installation provides a fully configured ContainerHub available locally along with the kubernetes installation. This configuration allows the user to build, upload and deploy custom container images as they were provided from external sources. Internet public sources are still available but local development can be kept in this localhost server. Builtin ClusterOps operator will be fetched from this ContainerHub registry too.
    • Kubernetes official dashboard installed as a plugin, others planned too (k9s for example).
    • Kubevirt plugin installed and properly configured. Unleash the power of classic virtualization (KVM+QEMU) on top of Kubernetes and manage your entire system from there, libvirtd and virsh libs are required.
    • One operator to rule them all. The installation script configures your machine automatically during installation and adds one kubernetes operator to manage your local cluster. From there the operator takes care of the cluster on your behalf.
    • Clean installation and removal. Just test it, when you are done just use the same program to uninstall everything without leaving configs (or pods) behind.

    Planned features (Wishlist / TODOs)

    • Containerized Data Importer (CDI). Persistent storage management add-on for Kubernetes to provide a declarative way of building and importing Virtual Machine Disks on PVCs for