A Survey on Intelligent Network Operations and Performance Optimization Based on Large Language Models

Abstract

As Large Language Models (LLMs) have achieved significant success in handling multi-modal tasks such as text, images, videos, and sounds, particularly showcasing emergent capabilities in natural language tasks, they hold great potential for network operations that similarly involve vast amounts of text data, fault data, and log files. This paper focuses on the development of LLMs, detailing their fundamental principles and application scenarios across different domains. It highlights the remarkable capabilities of LLMs in tasks such as fault diagnosis, causal inference, and intelligent question answering, and applies these abilities to the field of network operations. Moreover, the paper reviews some of the key issues and technical barriers faced by intelligent networks, such as efficiently monitoring networks in real-time and providing timely alerts when necessary. In addition to examining the utilization of LLM in network operations, this paper introduces a framework for intelligent network operations and performance optimization, leveraging LLM. The objective of this framework is to bolster network robustness and furnish users with exceptional, personalized network services. Ultimately, we conclude by delineating the challenges encountered in LLM-based intelligent network operations and performance optimization, while presenting potential solutions to overcome these hurdles and propel the comprehensive deployment of LLM-driven network intelligence.