• Computer Science > Networking and Internet Architecture [Submitted on 12 Jun 2024 (v1), last revised 25 Feb 2026 (this version, v3)] Title:MSADM: Large Language Model (LLM) Assisted End-to-End Network Health Management Based on Multi-Scale Semanticization View PDF HTML (experimental)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

Article Summaries:

  • Computer Science > Networking and Internet Architecture [Submitted on 12 Jun 2024 (v1), last revised 25 Feb 2026 (this version, v3)] Title:MSADM: Large Language Model (LLM) Assisted End-to-End Network Health Management Based on Multi-Scale Semanticization View PDF HTML (experimental)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

Sources: