Instructing network devices via large language models
Francesco Dall'Agata
Instructing network devices via large language models.
Rel. Alessio Sacco, Guido Marchetto. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2024
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Abstract
In recent years, there have been significant advancements in Artificial Intelligence (AI), particularly in Natural Language Processing (NLP). These advancements have introduced a groundbreaking approach to effectively manage the escalating complexity of network environments, simplifying network operations by allowing operators to use declarative interfaces instead of imperative ones. This paradigm shift, known as "Intent-Driven Networking," facilitates the conversion of human language into network configurations through the use of NLP techniques, eliminating the necessity for manual coding or execution. Furthermore, the advent of state-of-the-art tools such as GPT, LLama, and PaLM, which possess remarkable capabilities in understanding and generating complex natural language, opens up exciting possibilities for Intent-Driven Networking.
This thesis aims to exploit these innovative tools, collectively referred to as Large Language Models (LLMs), by developing a prototype capable of translating high-level policies into actionable network configurations within the realm of Software Defined Networks (SDN)
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