Large Language Model Enhanced Autonomous Agents for Proactive Fault-Tolerant Edge Networks

Addressing network fault tolerance tasks with various scenarios and domains is a crucial step towards achieving autonomous networks. Despite the abundance of artificial intelligence models designed for specific network scenarios and fault tasks, they may encounter challenges in delivering optimal performance across all environments. Considering the exhibited exceptional abilities of large language models (LLMs) in content generation and task planning, we advocate that LLMs can act as automated schedulers to manage existing fault tolerance models. Through the current network scenarios and task prompts, appropriate models are invoked to orchestrate optimization strategies. By leveraging pre-optimized knowledge and tools, we propose an LLM-based edge network fault-tolerant paradigm (LLM-ENFT), which autonomously manages the full cycle of perceiving, diagnosing, and recovering network faults. Experiments conducted on the edge network testbed based on Mininet and Ryu show that LLM-ENFT exhibits strong resilience in the face of network congestion and link failure.