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The JBoss Community is planning to participate in Google Summer of Code in 2025.

All contributors & developers are welcome to participate in the https://summerofcode.withgoogle.com/ program with the JBoss Community. 

If you are a contributor looking forward to participating in the GSoC 2025 with the JBoss Community:

  • Feel free to browse the growing idea list below.
  • Please don't hesitate to contact the mentor(s) indicated in the proposal for any related clarification and to discuss proposals.
  • You can have a look at ideas list of previous years for inspiration.
  • Please see our contributor guide.
  • You may find a sample GSoC proposal document here which was for this idea.

Contributors: Please read the list above and also read our contributor guide.

A note to mentors

MENTORS: Red Hat employees can change this page directly to add ideas. Please be extra careful to not get other mentor's edits discarded.
Red Hatters should have linked their jboss.org account with Red Hat and can be checked on https://sso.jboss.org/login

Non-Red Hatters can add a comment to the page and admins will make sure the idea is added to the page.


Table of Contents

Administrators and Mentors

We will list the potential mentors in this place. For now, if you have any questions, please contact the GSoC administrators:

George Zaronikas (gzaronikas) and Sokratis Zappis (szappis AT redhat DOT com)

Communication channels

Gitter    : JBossOutreach/GSoC - Gitter 


Please take note - These channels are about generic doubts. For project-specific doubts you will need to contact project mentors and channels specified in the project description.


Idea template (for mentors)

Project title

Summary of idea:

-Idea

-Feature A

-Feature B

Knowledge prerequisite: Languages/Technologies goes here

Github repo:

Project size: medium (~175 hours) or large (~350 hours)

Skill level: Beginner/Intermediate/Advanced

Contact(s) / potential mentors(s): Mentor(s) name and contact details

Associated JBoss community project(s):

Idea Proposals


Kiali

Summary of idea: Improving Kiali UI and Developing the Backstage Plugin Integration

  • The Kiali project is an open-source service mesh observability tool that integrates with Istio to provide advanced visualizations and monitoring. The goal of this project is twofold:

    1. Enhance Kiali's User Interface (UI): Streamline and modernize the user experience, making it more intuitive and accessible for users.
      1. Design and implement an updated, more user-friendly UI.
      2. Focus on enhancing navigation, responsiveness, and accessibility.
    2. Develop a Backstage Plugin for Kiali: Integrate Kiali into Backstage, an open platform for building developer portals. This plugin will provide seamless access to Kiali’s features within the Backstage ecosystem, allowing developers to view and manage their service mesh directly from Backstage.
      1. Develop a Backstage plugin that integrates Kiali’s core functionalities.
      2. Provide options for users to access mesh-related metrics, traces, and visualizations directly from the Backstage interface.
      3. Include authentication mechanisms for secure access to Kiali’s data.
    3. Documentation and Tutorials
      1. Comprehensive documentation for both the UI changes and Backstage plugin.
      2. User guide and developer documentation to help others set up, contribute to, and extend the integration.
  • Image registry is QUAY.
  • Pipeline and builds are done by GitHub CI.

Outcome: The proposal will improve the usability and accessibility of Kiali while providing a new integration with the Backstage platform, enabling a broader range of users to interact with the Kiali tool.

Possible tasks for this project:

  • Adapt Kiali Wizards to new React purpose
  • Work in the backstage plugin area to add new components.
  • Developer community website to show stats
  • Write documentation

Knowledge prerequisite: React

Github repo: https://github.com/kiali/kiali

Project size: large (~350 hours)

Skill level: Intermediate

Contact / potential mentors: Alberto Gutierrez (aljesusg@redhat.com | aljesusg@gmail.com)

Associated community projects: 


From Reactive to Proactive: AI-Enhanced Performance Insights in Kruize

Summary of Idea:

In dynamic containerized environments, predicting and preventing performance bottlenecks is crucial for maintaining efficiency and cost-effectiveness. Kruize provides container right-sizing recommendations based on performance metrics such as CPU and memory usage, collected from Prometheus. Currently, recommendations are generated using a percentile-based approach.

To enhance this, we propose integrating a time series and regression-based models into Kruize. These models will analyze historical performance data to generate more proactive, data-driven recommendations. The integration of these models will improve predictive accuracy, enable real-time and batch data processing, and ultimately help users optimize their infrastructure more effectively.

Project Features:

  • Collect historical usage data from Prometheus, perform feature engineering (e.g., smoothing, normalization, trend extraction) to enhance model accuracy.
  • Store processed data efficiently for real-time and batch analysis.
  • Implement and train a time series model (e.g., ARIMA, Prophet, or LSTM).
  • Develop and evaluate a regression-based model (e.g., Random Forest, XGBoost).
  • Compare their performance in terms of prediction accuracy, latency, and robustness.
  • Integrate both models into Kruize to generate performance recommendations. 

Knowledge Prerequisite:

  • Languages: Python, Java, Shell Scripting 
  • Technologies: Scikit-Learn, Pandas, NumPy, Matplotlib
  • Machine Learning Concepts: Time Series Analysis, Regression Models, Forecasting
  • Experience with containerized environments, shell scripting and performance tuning is a plus.

GitHub Repository: https://github.com/kruize/autotune.git 

Project Size: Medium (~175 Hours)

Skill Level: Intermediate

Contact(s) / Potential Mentor(s): 

Associated Community Project(s):



 


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