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This paper introduces GeNet, a novel framework for automating communications network engineering in enterprise environments. To address the complexity, time-consuming nature, and error-prone nature of traditional manual network engineering methods, GeNet leverages large-scale language models (LLMs) to streamline the network design workflow. Using visual and textual modalities, it interprets and updates network topology and device configurations based on user intent. GeNet was evaluated using an enterprise network scenario adopted from a Cisco certification exercise, demonstrating its ability to accurately interpret network topology images, reduce network engineer effort, and accelerate the network design process. It particularly highlights the importance of accurate topology understanding when handling intents that require network topology modifications.
Takeaways, Limitations
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Takeaways:
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A New Approach to Automating Enterprise Network Engineering
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Support for efficient network design through an LLM-based multimodal framework.
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The potential for reducing engineering time and errors through accurate interpretation of network topology images is presented.
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Emphasizes the importance of modifying network topology and demonstrates the need for accurate topology understanding.
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Limitations:
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Lack of extensive testing and validation in real-world business environments.
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Applicability to various network equipment and protocols needs to be reviewed.
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Potential errors and reliability issues due to limitations of LLM
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Further research is needed on performance and scalability in complex network environments.
