Anomaly Detection and Early Warning Mechanism for Intelligent Monitoring Systems in Multi-Cloud Environments Based on LLM

TL;DR

This paper presents a novel anomaly detection and early warning system for multi-cloud environments that leverages Large-Scale Language Models to improve accuracy, adaptability, and real-time response in intelligent monitoring systems.

Contribution

It introduces a multi-level feature extraction method combining LLMs with traditional machine learning, enhancing anomaly detection and adaptability across diverse cloud environments.

Findings

Higher detection accuracy compared to traditional systems

Reduced latency in anomaly detection

Improved resilience and active management of cloud infrastructure

Abstract

With the rapid development of multi-cloud environments, it is increasingly important to ensure the security and reliability of intelligent monitoring systems. In this paper, we propose an anomaly detection and early warning mechanism for intelligent monitoring system in multi-cloud environment based on Large-Scale Language Model (LLM). On the basis of the existing monitoring framework, the proposed model innovatively introduces a multi-level feature extraction method, which combines the natural language processing ability of LLM with traditional machine learning methods to enhance the accuracy of anomaly detection and improve the real-time response efficiency. By introducing the contextual understanding capabilities of LLMs, the model dynamically adapts to different cloud service providers and environments, so as to more effectively detect abnormal patterns and predict potential failures. Experimental results show that the proposed model is significantly better than the traditional anomaly detection system in terms of detection accuracy and latency, and significantly improves the resilience and active management ability of cloud infrastructure.