用大模型提升多云监控的异常检测与预警能力
Anomaly Detection and Early Warning Mechanism for Intelligent Monitoring Systems in Multi-Cloud Environments Based on LLM
- 融合大模型与传统机器学习，实现多层级特征提取
- 检测准确率和延迟表现优于传统系统，提升云基础设施韧性
- 适合关注云安全、智能运维的开发者与架构师
随着多云环境的快速发展，保障智能监控系统的安全与可靠性日益重要。本文提出一种基于大规模语言模型（LLM）的多云环境中智能监控系统的异常检测与早期预警机制。在现有监控框架基础上，创新性地引入多层级特征提取方法，结合大模型的自然语言处理能力与传统机器学习技术，提升了异常检测精度并优化了实时响应效率。通过利用大模型的上下文理解能力，该模型可动态适应不同云服务提供商及环境，更有效地识别异常模式并预测潜在故障。实验结果表明，所提模型在检测准确率和延迟方面显著优于传统异常检测系统，显著增强了云基础设施的韧性与主动管理能力。
原文摘要 · Abstract (English)
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.
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