Project Description
Network management is very important for cloud and Edge. CNF(cloud-native network function) is next-generation VNF. CNF will be supported in Edge computing in the future. It is very important specially in 5G network. A cloud-native 5G network provides the completely digitized platform necessary to deploy new cloud services and to take advantage of cloud-native 5G benefits like massive IoT, edge computing, and network slicing. Cloud Native Network Functions will ultimately help operators pivot from Non-standlone (NSA) 5G architecture which depends on a 4G core network to operate, to standalone (SA) 5G. Standalone 5G pairs 5G radios with a cloud-native 5G core network. We would like to build a demo network based on K8S.
Goal for this Hackweek
- prepare environment for CNF demo
- build K8s cloud
- build a demo of the CNF
- Change source code and document
Resources
comment: # https://github.com/cncf/cnf-testsuite/
comment: # https://github.com/cncf/cnf-testbed/
Looking for hackers with the skills:
This project is part of:
Hack Week 22
Activity
Comments
-
almost 2 years ago by epenchev | Reply
This looks an interesting POC from RedHat https://www.redhat.com/architect/autoscale-5g-core, and they shared the knowledge as well https://github.com/fenar/cnvopen5gcore/tree/Release-1.0 . It's based on open5gs (https://github.com/open5gs/open5gs) for the 5G CNF, Istio for service mesh and UERANSIM for 5G UE and RAN simulator. Although it's applied and configured to run on RedHat Openshift I think it will be cool to have something like this on Rancher
as well.Reply to lizhang
Agree with you. It will be very nice if we can enable open5G on Rancher. There are lot of work to do that.
# A First Level Header ## A Second Level Header Use one asterisk to *emphasize* Use two asterisks for **strong emphasis** - Use hyphens - for unordereed - lists This is an [link to example.com](http://example.com/) This is an image  This is a user link @hans This is a project link hw#some-cool-title
-
-
almost 2 years ago by lizhang | Reply
For network stack, here is the reference. For performance of network, SRIOV, DPDK should be used. For vswitch, VPP has much better performance then OVS. https://ligato.io/blog/cnf-ligato-fdio/ https://cloud.redhat.com/blog/building-cnf-applications-with-openshift-pipelines
-
Reply to epenchev
This looks an interesting POC from RedHat https://www.redhat.com/architect/autoscale-5g-core, and they shared the knowledge as well https://github.com/fenar/cnvopen5gcore/tree/Release-1.0 .
It's based on open5gs (https://github.com/open5gs/open5gs) for the 5G CNF, Istio for service mesh and UERANSIM for 5G UE and RAN simulator.
Although it's applied and configured to run on RedHat Openshift I think it will be cool to have something like this on Rancher as well.
# A First Level Header ## A Second Level Header Use one asterisk to *emphasize* Use two asterisks for **strong emphasis** - Use hyphens - for unordereed - lists This is an [link to example.com](http://example.com/) This is an image  This is a user link @hans This is a project link hw#some-cool-title
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!
AI + Board Games
Board games have long been fertile ground for AI innovation, pushing the boundaries of capabilities such as strategy, adaptability, and real-time decision-making - from Deep Blue's chess mastery to AlphaZero’s domination of Go. Games aren’t just fun: they’re complex, dynamic problems that often mirror real-world challenges, making them interesting from an engineering perspective.
As avid board gamers, aspiring board game designers, and engineers with careers in open source infrastructure, we’re excited to dive into the latest AI techniques first-hand.
Our goal is to develop an all-open-source, all-green AWS-based stack powered by some serious hardware to drive our board game experiments forward!
Project Goals
Set Up the Stack:
- Install and configure the TAG and PyTAG frameworks on SUSE Linux Enterprise Base Container Images.
- Integrate with the SUSE AI stack for GPU-accelerated training on AWS.
- Validate a sample GPU-accelerated PyTAG workload on SUSE AI.
- Ensure the setup is entirely repeatable with Terraform and configuration scripts, documenting results along the way.
Design and Implement AI Agents:
- Develop AI agents for the two board games, incorporating Statistical Forward Planning and Deep Reinforcement Learning techniques.
- Fine-tune model parameters to optimize game-playing performance.
- Document the advantages and limitations of each technique.
Test, Analyze, and Refine:
- Conduct AI vs. AI and AI vs. human matches to evaluate agent strategies and performance.
- Record insights, document learning outcomes, and refine models based on real-world gameplay.
Technical Stack
- Frameworks: TAG and PyTAG for AI agent development
- Platform: SUSE AI
- Tools: AWS for high-performance GPU acceleration
Why This Project Matters
This project not only deepens our understanding of AI techniques by doing but also showcases the power and flexibility of SUSE’s open-source infrastructure for supporting high-level AI projects. By building on an all-open-source stack, we aim to create a pathway for other developers and AI enthusiasts to explore, experiment, and deploy their own innovative projects within the open-source space.
Our Motivation
We believe hands-on experimentation is the best teacher.
Combining our engineering backgrounds with our passion for board games, we’ll explore AI in a way that’s both challenging and creatively rewarding. Our ultimate goal? To hack an AI agent that’s as strategic and adaptable as a real human opponent (if not better!) — and to leverage it to design even better games... for humans to play!
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.
Improve Development Environment on Uyuni by mbussolotto
Description
Currently create a dev environment on Uyuni might be complicated. The steps are:
- add the correct repo
- download packages
- configure your IDE (checkstyle, format rules, sonarlint....)
- setup debug environment
- ...
The current doc can be improved: some information are hard to be find out, some others are completely missing.
Dev Container might solve this situation.
Goals
Uyuni development in no time:
- using VSCode:
- setting.json should contains all settings (for all languages in Uyuni, with all checkstyle rules etc...)
- dev container should contains all dependencies
- setup debug environment
- implement a GitHub Workspace solution
- re-write documentation
Lots of pieces are already implemented: we need to connect them in a consistent solution.
Resources
- https://github.com/uyuni-project/uyuni/wiki
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? 
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
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).
Edge Image Builder and mkosi for Uyuni by oholecek
Description
One part of Uyuni system management tool is ability to build custom images. Currently Uyuni supports only Kiwi image builder.
Kiwi however is not the only image building system out there and with the goal to also become familiar with other systems, this projects aim to add support for Edge Image builder and systemd's mkosi systems.
Goals
Uyuni is able to
- provision EIB and mkosi build hosts
- build EIB and mkosi images and store them
Resources
- Uyuni - https://github.com/uyuni-project/uyuni
- Edge Image builder - https://github.com/suse-edge/edge-image-builder
- mkosi - https://github.com/systemd/mkosi