Large Language Model(LLM) assisted End-to-End Network Health Management based on Multi-Scale Semanticization

2024-06-12

arXiv.org

AI Summary

To address the challenge of real-time, accurate, and interpretable health-state diagnosis for devices in Dynamic Heterogeneous Networks (DHNs), this paper proposes an end-to-end intelligent operations and maintenance framework. The frontend introduces a Multi-Scale Semantic Anomaly Detection Model (MSADM), integrating semantic rule trees with attention mechanisms to enable fine-grained perception across heterogeneous network entities. The backend employs a Chain-of-Thought (CoT) large language model to autonomously generate fault root-cause analyses and optimization strategies, thereby closing the detection–analysis–decision loop. This work establishes the first multi-scale semantic anomaly detection paradigm for DHNs and pioneers the deep integration of CoT-based LLMs into network fault diagnosis pipelines. Experimental results demonstrate that MSADM achieves 91.31% accuracy in heterogeneous network anomaly detection—significantly outperforming existing distributed approaches—while supporting real-time, adaptive, and interpretable network health management.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Machine Learning: Hardware-aware ML
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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 dynamic heterogeneous networks (DHNs) environment. Moreover, current state-of-the-art distributed anomaly detection methods, which utilize specific machine learning techniques, lack multi-scale adaptivity for heterogeneous device information, resulting in unsatisfactory diagnostic accuracy for DHNs. In this paper, we develop an LLM-assisted end-to-end intelligent network health management framework. The framework first proposes a Multi-Scale Semanticized Anomaly Detection Model (MSADM), incorporating semantic rule trees with an attention mechanism to address the multi-scale anomaly detection problem in DHNs. Secondly, a chain-of-thought-based large language model is embedded in downstream to adaptively analyze the fault detection results and produce an analysis report with detailed fault information and optimization strategies. Experimental results show that the accuracy of our proposed MSADM for heterogeneous network entity anomaly detection is as high as 91.31%.

Problem

• Develops LLM-assisted framework for network health management.
• Addresses multi-scale anomaly detection in dynamic heterogeneous networks.
• Improves diagnostic accuracy with semantic rule trees and attention mechanisms.

Innovation

• LLM-assisted end-to-end network health management
• Multi-Scale Semanticized Anomaly Detection Model
• Chain-of-thought-based LLM for fault analysis