Call For Contributors (Technical/Non-Technical)
GitHub Link: https://github.com/olasupo/bubbln_network-automation
Network automation has become a cornerstone of modern networking, offering unparalleled efficiency and scalability. Tools like Ansible, Chef, and Puppet have streamlined the deployment and management of network infrastructure, but they still rely heavily on human intervention for playbook preparation. This introduces the potential for human errors, leading to inefficiencies and delays in network deployment.
The project named Bubbln introduces a groundbreaking solution by integrating artificial intelligence (AI) into Ansible, eliminating human errors in playbook preparation and significantly reducing the Mean Time to Deployment (MTTD). This innovative approach not only enhances the productivity and efficiency of network engineers but has also been proven through experiments on a simple network of four routers to support the following functions:
Current Capabilities
- AI-Driven Playbook Generation: Bubbln leverages ChatGPT to automatically generate network playbooks based on user-defined configurations.
- Error Elimination: By removing human errors from the playbook preparation process, Bubbln ensures a higher level of accuracy in network configurations.
- Improved Efficiency: With AI automation, Bubbln speeds up the deployment of network configurations, reducing the time required for manual playbook creation.
- Customizable Configurations: Users can input specific router protocols (OSPF or EIGRP), interface configurations, and other network details to tailor the generated playbooks.
Limitations
Although, Bubbln has been able to tick some feature checkboxes mentioned above, there is a very large room for improvement, thus necessitating the call for independent collaborators for testing and improvement of the tool. Please find below some identified limitations:
- Complex Configurations: Bubbln is optimized for simpler network configurations and may require manual adjustments for highly complex setups.
- Limited Protocol Support: While supporting common protocols like OSPF and EIGRP, Bubbln may not cover all possible network protocols.
- Dependency on Input Quality: The accuracy of generated playbooks heavily depends on the accuracy of user-provided input.
- Tendencies of ChatGPT:
- ChatGPT, the AI model used in Bubbln, may sometimes misinterpret, or generate errors based on ambiguous or incomplete user inputs.
- Ambiguous Instructions: If user instructions are vague or unclear, ChatGPT might produce inaccurate or unexpected playbook outputs.
- Inconsistent Responses: ChatGPT's responses can vary based on the context of the input, leading to inconsistencies in playbook generation.
- Sensitivity to Formatting: ChatGPT may struggle with non-standard or inconsistent formatting in user-provided data, resulting in errors in playbook structure.
- No Real-Time Monitoring: Bubbln focuses on playbook generation and does not include real-time network monitoring or troubleshooting capabilities.
- Potential Security Risks: As with any automation tool, improper configurations or misinterpretation of instructions could lead to security vulnerabilities.
- Training Data Limitations: ChatGPT's responses are based on the training data it was exposed to, which might not cover every possible network scenario.
- Unforeseen Scenarios: Bubbln may encounter scenarios that were not adequately covered during its training, leading to unexpected results.
- Handling Edge Cases: Edge cases, such as rare network configurations or unique protocols, may not be handled optimally by Bubbln.
- Ongoing Model Updates: ChatGPT models are continually evolving, and updates to the underlying AI model might introduce new behaviours or errors.
- Debugging and Error Handling:
- When errors occur during playbook generation, Bubbln may not provide detailed error messages or debugging information, making troubleshooting challenging.
- Users might need to manually inspect and validate the generated playbooks for correctness.
