There is a number of possible improvements to the architecture of SUSE Manager / Salt integration that should be investigated in order to improve the reliability and scalability of the backend:

  1. Actions are currently scheduled in the minions using the schedule module of Salt. This brings problems with reliability as for instance a minion can be down at the specified schedule time which leads to actions not being executed. Scalability can be an issue as actions being scheduled for many minions might return results to the server at the same time. Instead it might be better to keep control over scheduled actions on the server to allow batching of actions as well as downtimes of minions or even the server. There is a work in progress branch to get started.
  2. For receiving action results we are currently relying on a websocket connection to the Salt event bus in order to receive job return events. This is problematic as the connection might be interrupted leading to the server missing events. Instead we could make use of a master-side returner to write the action results directly into the postgresql database. This would further allow to have setups with multiple Salt masters returning job results to the same database.

Looking for hackers with the skills:

susemanager salt backend java python postgresql

This project is part of:

Hack Week 15

Activity

  • almost 8 years ago: dvosburg liked this project.
  • almost 8 years ago: whdu liked this project.
  • almost 8 years ago: moio joined this project.
  • almost 8 years ago: moio liked this project.
  • almost 8 years ago: j_renner added keyword "python" to this project.
  • almost 8 years ago: j_renner added keyword "postgresql" to this project.
  • almost 8 years ago: dmacvicar liked this project.
  • almost 8 years ago: j_renner started this project.
  • almost 8 years ago: j_renner liked this project.
  • almost 8 years ago: j_renner added keyword "susemanager" to this project.
  • almost 8 years ago: j_renner added keyword "salt" to this project.
  • almost 8 years ago: j_renner added keyword "backend" to this project.
  • almost 8 years ago: j_renner added keyword "java" to this project.
  • almost 8 years ago: j_renner originated this project.

  • Comments

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    A chameleon playing chess in a train car, as a metaphor of SUSE AI applied to games


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

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    Results: Game Design Insights

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    • 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

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    • 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.

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    The Context: AI + Board Games


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