Embed a researcher-focused dashboard to group and preview experiments in the Django portal. The goal is to improve how past experiment runs can be tracked, grouped, and arranged for faster lookup and insights.

Proposed Improvements:

• Group submitted experiments by project, application, allocation, etc.
• Clean, customizable dashboard elements (e.g., charts) to preview past experiments.

Currently, all Airavata services are packaged and deployed as Java bundles. The goal is to containerize each service by wrapping it within a Dockerfile, allowing seamless deployment on container-enabled resources while also enabling local execution for development purposes.

This enhancement has potential to improve deployment consistency, simplify dependency management, and provide greater flexibility in running Airavata services across different environments, for both testing and production use cases.

Objective

Enhance the current development workflow by incorporating a simulated High-Performance Computing (HPC) environment into Apache Airavata's existing Integrated Development Environment (IDE) integration. This will enable developers to test and validate features locally without relying on physical HPC resources.

Requirements

• Simulated HPC Environment Integration
  • Dockerized Slurm Simulation: Develop a Docker container that emulates an HPC environment using Slurm, facilitating the testing of job scheduling and execution.​
  • Seamless IDE Integration: Ensure that this simulated environment integrates smoothly with the existing IDE setup, allowing developers to initiate and monitor jobs as they would in a real HPC setting.

• Development of Comprehensive Test Scenarios
  • Job Submission Tests: Create scripts to test various job submission scenarios, including successful executions, intentional failures, and long-running processes.​
  • Feature Validation: Ensure that all features exposed by Apache Airavata can be tested within this simulated environment.

The Airavata Django Portal currently runs on Python 3.6, which reached its end-of-life (EOL) in 2022. Continuing to use an unsupported Python version poses security risks and limits access to new features and package updates. Upgrading to a supported version (Python 3.12 or later) will ensure long-term maintainability, security, and compatibility with modern dependencies.

Status of Python versions

Impact:
• Improved security and stability
• Access to the latest language features and performance improvements
• Compatibility with actively maintained third-party packages

Proposed Solution: Update the codebase and dependencies for compatibility with Python 3.12+, and ensure everything works as expected post-upgrade.

Develop a devops dashboard to monitor Apache Airavata services, enabling real-time tracking of service health, uptime, and logs independent of the science gateway(s).
This centralized tool will help administrators efficiently monitor service performance and troubleshoot issues. The dashboard will feature a user-friendly monitoring UI that displays real-time status updates and logs for each service.

Proposed Solution:

• A logging subproces alongside each service, pushing logs to an external service.
• A devops dashboard that aggregates the logs and provides a unified view into the system.
• API calls from devops dashboard to each service, for proactive health-checking.
• Ability to monitor multiple gateways from the same dashboard.

Objective

Replace existing Ansible deployment scripts with OpenTofu configurations to improve deployment efficiency and maintainability for bare-metal environments.

Requirements

• Assessment of Current Ansible Scripts
  • Review Existing Playbooks: Analyze the current Ansible playbooks located in the Airavata GitHub repository to understand the deployment processes and dependencies.
  • Identify Core Components: Determine the essential services and configurations managed by Ansible, such as Kafka, RabbitMQ, Zookeeper, MariaDB, etc.

• Development of OpenTofu Configurations
  • Define Infrastructure as Code (IaC): Utilize OpenTofu's declarative language to codify the infrastructure components identified in the assessment phase.
  • Module Creation: Develop reusable modules for each service

Embed a researcher-focused dashboard to group and preview experiments in the Django portal. The goal is to improve how past experiment runs can be tracked, grouped, and arranged for faster lookup and insights.

Proposed Improvements:

• Group submitted experiments by project, application, allocation, etc.
• Clean, customizable dashboard elements
  • (e.g.,
  charts) to preview past experiments.

• • Kafka, RabbitMQ, Zookeeper) to promote consistency and ease of management.

• Testing and Validation
  • Simulate Deployments: Use OpenTofu's planning capabilities to simulate deployments, ensuring configurations align with the desired infrastructure state.
  • Iterative Refinement: Address any discrepancies or issues identified during testing to refine the OpenTofu configurations.

• Documentation
  • Update Deployment Guides: Revise existing documentation to reflect the new OpenTofu-based deployment process, providing clear instructions for users

Currently, all Airavata services are packaged and deployed as Java bundles. The goal is to containerize each service by wrapping it within a Dockerfile, allowing seamless deployment on container-enabled resources while also enabling local execution for development purposes.

• • .

Objective

Enhance the current development workflow by incorporating a simulated High-Performance Computing (HPC) environment into Apache Airavata's existing Integrated Development Environment (IDE) integration. This will enable developers to test and validate features locally without relying on physical HPC resources.

Requirements

• Simulated HPC Environment Integration
  • Dockerized Slurm Simulation: Develop a Docker container that emulates an HPC environment using Slurm, facilitating the testing of job scheduling and execution.​
  • Seamless IDE Integration: Ensure that this simulated environment integrates smoothly with the existing IDE setup, allowing developers to initiate and monitor jobs as they would in a real HPC setting.

• Development of Comprehensive Test Scenarios
  • Job Submission Tests: Create scripts to test various job submission scenarios, including successful executions, intentional failures, and long-running processes.​
  • Feature Validation: Ensure that all features exposed by Apache Airavata can be tested within this simulated environment.

• User-Friendly Setup
  • Simplified Configuration: Design the setup process to require minimal configuration, enabling developers to initiate the environment and execute tests with just a few commands

The Airavata Django Portal currently runs on Python 3.6, which reached its end-of-life (EOL) in 2022. Continuing to use an unsupported Python version poses security risks and limits access to new features and package updates. Upgrading to a supported version (Python 3.12 or later) will ensure long-term maintainability, security, and compatibility with modern dependencies.

Status of Python versions

Impact:
• Improved security and stability
• Access to the latest language features and performance improvements
• Compatibility with actively maintained third-party packages

Proposed Solution: Update the codebase and dependencies for compatibility with Python 3.12+, and ensure everything works as expected post-upgrade.

Website: https://hertzbeat.apache.org/

Github: http://github.com/apache/hertzbeat/

*Background*

Apache HertzBeat is an open-source real-time monitoring tool that supports a wide range of monitoring targets, including web services, databases, middleware, and more. It features high performance, scalability, and security.

With the advancement of artificial intelligence (AI) technologies, integrating AI with monitoring systems can significantly enhance their usability and interactivity. By developing an AI Agent based on the Model Context Protocol (MCP), we aim to enable conversational interaction for querying monitoring information, adding new monitoring tasks, and retrieving monitoring metrics. This will provide a more user-friendly and intelligent monitoring management experience.

*Objectives*

1. Research and Implementation: Develop an AI Agent based on Apache HertzBeat and the MCP protocol to enable conversational interaction with users.

2. Functional Implementation:

• Query Monitoring And Alarm Information: Allow users to query the status of monitoring targets (e.g., normal, abnormal) and retrieve metrics data (e.g., CPU usage, memory usage, response time), alarm data through conversational commands.
• Add New Monitoring Tasks: Enable users to add new monitoring targets (e.g., web services, databases, middleware) and configure alert thresholds via conversational commands.
• Retrieve Monitoring Metrics Data: Allow users to obtain metrics data for specific monitoring targets and support data visualization via conversational commands.

*Requirements Analysis*

• Apache HertzBeat: As the core backend for the monitoring system, it provides functions for data collection, storage, and management.

• MCP Protocol: An open protocol that enables seamless integration between LLM applications and external data sources and tools.

• Front-end Interaction: Develop a user-friendly interface that supports voice or text input and displays monitoring information and interaction results.

*Recommended Skills*

• Java + TypeScript: Apache HertzBeat is developed based on this technology stack. Therefore, mastering these technologies is crucial for integrating with HertzBeat.
• SpringAi: It is recommended to use SpringAi to build the AI agent.
• LLM + MCP: You need to have an understanding of LLM (Large Language Models) and the MCP protocol. SpringAi seem supports the MCP protocol or consider use the mcp-sdk directly.

*Size*

• Difficulty: Hard
• Project size: ~350 hours

*Potential Mentors*

• Chao Gong: gongchao@apache.org 
   
• Shenghang Zhang: shenghang@apache.org

Synopsis

Apache Mahout has been evolving, with a recent shift in focus toward Quantum Computing (Qumat). However, the official website does not currently reflect this transition, making it difficult for developers and contributors to engage with Mahout’s new direction. Additionally, legacy components like MapReduce and Samsara are no longer actively developed but still occupy prominent space on the website.

This project aims to refactor the Apache Mahout website to:

1. Bring Quantum Computing (Qumat) front and center as the new core focus of the project.
2. Deprecate outdated technologies (MapReduce and Samsara) while keeping the documentation intact with clear deprecation warnings.
3. Improve website structure, navigation, and content organization to enhance accessibility and usability.

By executing these changes, this project will ensure that new and existing users can quickly access relevant information while keeping historical documentation available in a structured manner.

Benefits to the Community

A well-organized and up-to-date website is essential for any open-source project. This proposal offers multiple benefits to the Apache Mahout community:

1. Highlighting Quantum Computing (Qumat)

• Restructure the website so that Qumat-related content is the primary focus.
• Ensure that all documentation, blogs, and tutorials related to Qumat are easily discoverable from the homepage.

2. Deprecating MapReduce and Samsara

• Add clear deprecation warnings to pages related to MapReduce and Samsara.
• Ensure these technologies remain accessible for historical reference but indicate that they are no longer actively maintained.

3. Improved Navigation and Accessibility

• Design a more intuitive navigation system for easy exploration of different sections.
• Ensure smooth access to documentation, blogs, and learning resources.

4. Updating Outdated Content

• Perform a full website audit to identify obsolete articles, guides, and references.
• Refresh and rewrite content where necessary, focusing on Mahout’s latest advancements.

5. Engaging New Contributors

• A modern, user-friendly website will attract more developers, researchers, and open-source contributors to the project.

Deliverables

1. Website Restructuring

• Modify the homepage and navigation bar to prominently feature Quantum Computing (Qumat) as the main focus.
• Ensure Qumat-related documentation and blog posts are front and center.

2. Deprecation of MapReduce and Samsara

• Add banner notifications on all MapReduce and Samsara pages marking them as deprecated.
• Ensure clear explanations so users understand these technologies are no longer in active development.

3. Content Review & Updates

• Perform a recursive LS audit to identify outdated and redundant content.
• Update old blogs and articles to align with Mahout’s latest developments.

4. Improved Website Navigation

• Implement a modern, responsive, and mobile-friendly navigation system.
• Optimize loading speed and ensure smooth user experience.

5. Documentation Enhancement

• Ensure all essential documentation is accessible from the homepage.
• Improve the readability and structure of the docs.

Technical Details

The project will utilize:

• HTML, CSS, JavaScript for website front-end improvements.
• Modern front-end frameworks (if required) to enhance UX/UI.
• Shell scripting or Python to perform a recursive LS audit of the website structure.
• Version control via GitHub for tracking changes and ensuring collaboration.

Expected Outcomes

✅ A refactored website that clearly emphasizes Quantum Computing (Qumat).
✅ A deprecated but accessible archive for MapReduce and Samsara.
✅ An updated and well-structured content repository for Mahout users and contributors.
✅ An intuitive, user-friendly website that engages both new and existing users.

Timeline (12+ Weeks, Full-Time Commitment - 30 hrs/week)

Community Bonding (Weeks 1-2)

• Engage with mentors and the Mahout community.
• Gather feedback on website restructuring priorities.
• Set up the development environment and review existing website architecture.

Phase 1: Planning & Initial Development (Weeks 3-6)

• Redesign homepage and navigation bar to prioritize Qumat.
• Identify and start modifying MapReduce and Samsara pages with deprecation warnings.
• Conduct a recursive LS audit to locate outdated files and redundant content.

Phase 2: Implementation & Testing (Weeks 7-10)

• Implement the new website navigation and homepage.
• Update and restructure documentation and blog content.
• Optimize the website’s file structure based on LS audit findings.
• Conduct extensive testing for responsiveness, accessibility, and performance.

Phase 3: Content Finalization & Refinement (Weeks 11-12+)

• Finalize deprecation notices for MapReduce and Samsara.
• Ensure all Qumat-related content is easily accessible.
• Perform last-minute optimizations and bug fixes.
• Gather final feedback from the community and document all changes.

🔹 Total Timeline: 350+ hrs

Why This Should Be a GSoC Project

This project directly aligns with Google Summer of Code’s mission to enhance open-source software. By modernizing the Apache Mahout website, we ensure that its new focus on Quantum Computing (Qumat) is clearly reflected, making it easier for developers and researchers to engage with Mahout’s latest advancements.

Additionally, this project is well-scoped for GSoC, combining front-end development, content management, and structured auditing—all crucial aspects for a website overhaul.

Mentorship & Feasibility

• The project has clear, well-defined goals and structured milestones.
• It will be mentored by an experienced Apache Mahout maintainer who is applying for the mentor role.
• The tasks are technically feasible within the GSoC timeframe.

Conclusion

Refactoring the Apache Mahout website is essential for reflecting its new focus on Quantum Computing (Qumat) while ensuring historical documentation remains accessible. By modernizing the site, we enhance usability, improve accessibility, and help new users quickly understand Mahout’s direction.

This project will significantly enhance Mahout’s online presence and ensure the community stays well-informed and engaged.

Synopsis

Apache Doris is a real-time data warehouse that utilizes columnar storage. Currently, Doris applies default encoding methods based on column data types. This project aims to evaluate the efficiency of these default encodings (e.g., encoding/decoding time and compression ratios) using benchmark datasets like TPC-DS, HTTP logs, and TPC-H. The findings will guide optimizations to improve performance.

Key Objectives

• A. Develop a tool to evaluate encoding efficiency. The tool will take a column of data and an encoding method as input and output metrics such as compression ratio and processing speed.

• B. Optimize dictionary encoding for string columns. Current implementations apply dictionary encoding by default without evaluating data suitability, leading to inefficiencies for non-dictionary-friendly data.

• C. Assess the effectiveness of BitShuffle encoding for enhancing downstream compression.

  Benefits to the Community

• 
  Improve data compression efficiency in Apache Doris.
• Enhance query performance through optimized encoding/decoding.

  Technical Details

• Languages/Tools: C++ for encoding logic, GitHub for version control.

• Methodology:

• • 
    Benchmark existing encoding methods (e.g., dictionary, BitShuffle).
  • 
    Develop an evaluation framework to measure compression ratios and processing overhead.
  • Implement optimizations for specific data types and use cases.

    Timeline (12+ Weeks, Full-Time Commitment - 30 hrs/week)

1. Community Bonding (Weeks 1-2)

1. 1. 
      Engage with mentors and the Doris community.
   2. 
      Set up the development environment and study the codebase.
   3. 
      Document current column encoding strategies for all data types.
2. Phase 1: Planning & Initial Development (Weeks 3-6)

1. 1. 
      Build a tool to evaluate encoding schemes across data types.
   2. 
      Run benchmarks using TPC-DS, HTTP logs, and TPC-H datasets.
2. Phase 2: Analysis & Optimization (Weeks 7-10)

1. 1. Optimize Dictionary Encoding: Automatically detect and skip non-dictionary-friendly data (e.g., high-cardinality strings).

1. 1. BitShuffle Evaluation: Quantify its impact on compression ratios and processing speed.

1. 1. 
      Address additional optimization opportunities identified during analysis.
2. Phase 3: Finalization & Refinement (Weeks 11-12+)

1. 
   Refine code and documentation based on community feedback.
2. Submit PRs and ensure their merge into the Doris master branch.
    
   🔹 Total Effort: 350+ hours

   Expected Outcomes

1. 
   A tool to evaluate encoding efficiency for all Doris column types.
2. 
   Optimized dictionary encoding logic with automated suitability checks.
3. 
   Improved BitShuffle integration for enhanced compression.
4. Additional optimizations identified during the project.
   This project will strengthen Apache Doris’s performance in real-time analytics scenarios while fostering collaboration within the open-source community.

Contact Information *
Mentor Name: [Yongqiang Yang](dataroaring@apache.org^{Image Added}) , Apache Doris PMC member

• 
  Mentor Name:[Chen Zhang](zhangchen@apache.org^{Image Added}) Apache Doris Committer

Website: https://hertzbeat.apache.org/

Github: http://github.com/apache/hertzbeat/

*Background*

Apache HertzBeat is an open-source real-time monitoring tool that supports a wide range of monitoring targets, including web services, databases, middleware, and more. It features high performance, scalability, and security.

With the advancement of artificial intelligence (AI) technologies, integrating AI with monitoring systems can significantly enhance their usability and interactivity. By developing an AI Agent based on the Model Context Protocol (MCP), we aim to enable conversational interaction for querying monitoring information, adding new monitoring tasks, and retrieving monitoring metrics. This will provide a more user-friendly and intelligent monitoring management experience.

*Objectives*

1. Research and Implementation: Develop an AI Agent based on Apache HertzBeat and the MCP protocol to enable conversational interaction with users.

2. Functional Implementation:

• Query Monitoring And Alarm Information: Allow users to query the status of monitoring targets (e.g., normal, abnormal) and retrieve metrics data (e.g., CPU usage, memory usage, response time), alarm data through conversational commands.
• Add New Monitoring Tasks: Enable users to add new monitoring targets (e.g., web services, databases, middleware) and configure alert thresholds via conversational commands.
• Retrieve Monitoring Metrics Data: Allow users to obtain metrics data for specific monitoring targets and support data visualization via conversational commands.

*Requirements Analysis*

• Apache HertzBeat: As the core backend for the monitoring system, it provides functions for data collection, storage, and management.

• MCP Protocol: An open protocol that enables seamless integration between LLM applications and external data sources and tools.

• Front-end Interaction: Develop a user-friendly interface that supports voice or text input and displays monitoring information and interaction results.

*Recommended Skills*

• Java + TypeScript: Apache HertzBeat is developed based on this technology stack. Therefore, mastering these technologies is crucial for integrating with HertzBeat.
• SpringAi: It is recommended to use SpringAi to build the AI agent.
• LLM + MCP: You need to have an understanding of LLM (Large Language Models) and the MCP protocol. SpringAi seem supports the MCP protocol or consider use the mcp-sdk directly.

*Size*

• Difficulty: Hard
• Project size: ~350 hours

*Potential Mentors*

• Chao Gong: gongchao@apache.org 
   
• Shenghang Zhang: shenghang@apache.org

Synopsis

Apache Mahout has been evolving, with a recent shift in focus toward Quantum Computing (Qumat). However, the official website does not currently reflect this transition, making it difficult for developers and contributors to engage with Mahout’s new direction. Additionally, legacy components like MapReduce and Samsara are no longer actively developed but still occupy prominent space on the website.

This project aims to refactor the Apache Mahout website to:

1. Bring Quantum Computing (Qumat) front and center as the new core focus of the project.
2. Deprecate outdated technologies (MapReduce and Samsara) while keeping the documentation intact with clear deprecation warnings.
3. Improve website structure, navigation, and content organization to enhance accessibility and usability.

By executing these changes, this project will ensure that new and existing users can quickly access relevant information while keeping historical documentation available in a structured manner.

Benefits to the Community

A well-organized and up-to-date website is essential for any open-source project. This proposal offers multiple benefits to the Apache Mahout community:

1. Highlighting Quantum Computing (Qumat)

• Restructure the website so that Qumat-related content is the primary focus.
• Ensure that all documentation, blogs, and tutorials related to Qumat are easily discoverable from the homepage.

2. Deprecating MapReduce and Samsara

• Add clear deprecation warnings to pages related to MapReduce and Samsara.
• Ensure these technologies remain accessible for historical reference but indicate that they are no longer actively maintained.

3. Improved Navigation and Accessibility

• Design a more intuitive navigation system for easy exploration of different sections.
• Ensure smooth access to documentation, blogs, and learning resources.

4. Updating Outdated Content

• Perform a full website audit to identify obsolete articles, guides, and references.
• Refresh and rewrite content where necessary, focusing on Mahout’s latest advancements.

5. Engaging New Contributors

• A modern, user-friendly website will attract more developers, researchers, and open-source contributors to the project.

Deliverables

1. Website Restructuring

• Modify the homepage and navigation bar to prominently feature Quantum Computing (Qumat) as the main focus.
• Ensure Qumat-related documentation and blog posts are front and center.

2. Deprecation of MapReduce and Samsara

• Add banner notifications on all MapReduce and Samsara pages marking them as deprecated.
• Ensure clear explanations so users understand these technologies are no longer in active development.

3. Content Review & Updates

• Perform a recursive LS audit to identify outdated and redundant content.
• Update old blogs and articles to align with Mahout’s latest developments.

4. Improved Website Navigation

• Implement a modern, responsive, and mobile-friendly navigation system.
• Optimize loading speed and ensure smooth user experience.

5. Documentation Enhancement

• Ensure all essential documentation is accessible from the homepage.
• Improve the readability and structure of the docs.

Technical Details

The project will utilize:

• HTML, CSS, JavaScript for website front-end improvements.
• Modern front-end frameworks (if required) to enhance UX/UI.
• Shell scripting or Python to perform a recursive LS audit of the website structure.
• Version control via GitHub for tracking changes and ensuring collaboration.

Expected Outcomes

✅ A refactored website that clearly emphasizes Quantum Computing (Qumat).
✅ A deprecated but accessible archive for MapReduce and Samsara.
✅ An updated and well-structured content repository for Mahout users and contributors.
✅ An intuitive, user-friendly website that engages both new and existing users.

Timeline (12+ Weeks, Full-Time Commitment - 30 hrs/week)

Community Bonding (Weeks 1-2)

• Engage with mentors and the Mahout community.
• Gather feedback on website restructuring priorities.
• Set up the development environment and review existing website architecture.

Phase 1: Planning & Initial Development (Weeks 3-6)

• Redesign homepage and navigation bar to prioritize Qumat.
• Identify and start modifying MapReduce and Samsara pages with deprecation warnings.
• Conduct a recursive LS audit to locate outdated files and redundant content.

Phase 2: Implementation & Testing (Weeks 7-10)

• Implement the new website navigation and homepage.
• Update and restructure documentation and blog content.
• Optimize the website’s file structure based on LS audit findings.
• Conduct extensive testing for responsiveness, accessibility, and performance.

Phase 3: Content Finalization & Refinement (Weeks 11-12+)

• Finalize deprecation notices for MapReduce and Samsara.
• Ensure all Qumat-related content is easily accessible.
• Perform last-minute optimizations and bug fixes.
• Gather final feedback from the community and document all changes.

🔹 Total Timeline: 350+ hrs

Why This Should Be a GSoC Project

This project directly aligns with Google Summer of Code’s mission to enhance open-source software. By modernizing the Apache Mahout website, we ensure that its new focus on Quantum Computing (Qumat) is clearly reflected, making it easier for developers and researchers to engage with Mahout’s latest advancements.

Additionally, this project is well-scoped for GSoC, combining front-end development, content management, and structured auditing—all crucial aspects for a website overhaul.

Mentorship & Feasibility

• The project has clear, well-defined goals and structured milestones.
• It will be mentored by an experienced Apache Mahout maintainer who is applying for the mentor role.
• The tasks are technically feasible within the GSoC timeframe.

Conclusion

Refactoring the Apache Mahout website is essential for reflecting its new focus on Quantum Computing (Qumat) while ensuring historical documentation remains accessible. By modernizing the site, we enhance usability, improve accessibility, and help new users quickly understand Mahout’s direction.