Enhancing Network Management Using Code Generated by Large Language Models

• URL: http://arxiv.org/abs/2308.06261v1
• Date: Fri, 11 Aug 2023 17:49:15 GMT
• Title: Enhancing Network Management Using Code Generated by Large Language Models
• Authors: Sathiya Kumaran Mani, Yajie Zhou, Kevin Hsieh, Santiago Segarra, Ranveer Chandra, and Srikanth Kandula
• Abstract summary: We introduce a novel approach to facilitate a natural-language-based network management experience, utilizing large language models (LLMs) to generate task-specific code from natural language queries. This method tackles the challenges of explainability, scalability, and privacy by allowing network operators to inspect the generated code. We design and evaluate a prototype system using benchmark applications, showcasing high accuracy, cost-effectiveness, and the potential for further enhancements.
• Score: 15.557254786007325
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Analyzing network topologies and communication graphs plays a crucial role in contemporary network management. However, the absence of a cohesive approach leads to a challenging learning curve, heightened errors, and inefficiencies. In this paper, we introduce a novel approach to facilitate a natural-language-based network management experience, utilizing large language models (LLMs) to generate task-specific code from natural language queries. This method tackles the challenges of explainability, scalability, and privacy by allowing network operators to inspect the generated code, eliminating the need to share network data with LLMs, and concentrating on application-specific requests combined with general program synthesis techniques. We design and evaluate a prototype system using benchmark applications, showcasing high accuracy, cost-effectiveness, and the potential for further enhancements using complementary program synthesis techniques.

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