A CoT Reasoning-Based Computation and Network Resource Deployment Intent Translation Framework

Advancements in distributed machine learning have led to a demand for engineers with knowledge in AI, hardware, and networking. Research aims to automate deployment based on user intent to improve development efficiency. Large Language Models (LLMs) assist in translating user intent for resource allocation but face challenges in translation accuracy and contextual integrity. The Chain-of-Thought (CoT) improves LLM reasoning by breaking down problems into sequential steps. However, the reasoning steps and dependencies between the model requirements and the computation and network configurations are complex, requiring the selection of an appropriate thought path to construct the CoT framework. To address these issues, we propose a computation and network resource deployment intent translation framework based on CoT reasoning and create a benchmark for user intent in distributed learning. This framework translates user intent into hardware and network configurations, using LLMs to optimize translation accuracy through logical reasoning. Experiments on four LLM models show significant accuracy improvements with CoT compared to traditional prompts. The feasibility of our framework has been validated through its implementation and testing on a real-world testbed.