Enhancing Network Management Using Code Generated by Large Language
  Models
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.
64d9af962668b3a3684d3a9b
Sathiya Kumaran Mani
claimed_verified
2023-08-17T23:38:15.760Z
64dea237e672e4d2c55d6c07
/avatars/81d63a4a35f572b49e2292bb6fd5b994.svg
Sathiya Kumaran Mani
user
SathiyaMani
64d9af962668b3a3684d3a9c
Yajie Zhou
claimed_verified
2025-06-09T10:15:13.399Z
64825efa2e73ce18ad32d007
/avatars/f39772eb5960d3faf556d16a87940316.svg
Yajie Zhou
user
lesleychou
64d9af962668b3a3684d3a9d
Kevin Hsieh
claimed_verified
2023-08-16T13:23:26.989Z
6488e4b0e1b9ce78d3c4a979
/avatars/d652bc88ec6a7c9c63bffa0c29b9c742.svg
Kevin Hsieh
user
kevinhsieh
64d9af962668b3a3684d3a9e
Santiago Segarra
64d9af962668b3a3684d3a9f
Ranveer Chandra
64d9af962668b3a3684d3aa0
Srikanth Kandula
Enhancing Network Management Using Code Generated by Large Language
  Models
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.
A natural-language-based system using large language models generates task-specific code for network management, improving accuracy and privacy.
large language models natural language queries network management task-specific code explainability program synthesis