MSADM: Large Language Model (LLM) Assisted End-to-End Network Health Management Based on Multi-Scale Semanticization

TL;DR

This paper introduces MSADM, an LLM-assisted framework for end-to-end network health management that employs multi-scale data analysis and semantic anomaly detection to improve fault diagnosis accuracy in heterogeneous networks.

Contribution

The paper presents a novel multi-scale semanticized anomaly detection model and integrates LLMs for adaptive fault analysis, addressing limitations of existing methods in heterogeneous network environments.

Findings

MSADM outperforms existing fault diagnosis models on key metrics.

The multi-scale data scaling improves anomaly detection accuracy.

LLM integration enables detailed fault analysis and reporting.

Abstract

Network device and system health management is the foundation of modern network operations and maintenance. Traditional health management methods, relying on expert identification or simple rule-based algorithms, struggle to cope with the heterogeneous networks (HNs) environment. Moreover, current state-of-the-art distributed fault diagnosis methods, which utilize specific machine learning techniques, lack multi-scale adaptivity for heterogeneous device information, resulting in unsatisfactory diagnostic accuracy for HNs. In this paper, we develop an LLM-assisted end-to-end intelligent network health management framework. The framework first proposes a multi-scale data scaling method based on unsupervised learning to address the multi-scale data problem in HNs. Secondly, we combine the semantic rule tree with the attention mechanism to propose a Multi-Scale Semanticized Anomaly Detection Model (MSADM) that generates network semantic information while detecting anomalies. Finally, we embed a chain-of-thought-based large-scale language model downstream to adaptively analyze the fault diagnosis results and create an analysis report containing detailed fault information and optimization strategies. We compare our scheme with other fault diagnosis models and demonstrate that it performs well on several metrics of network fault diagnosis.