用大模型分析云系统日志，实现故障提前预警与自动修复。
Cloud-Based AI Systems: Leveraging Large Language Models for Intelligent Fault Detection and Autonomous Self-Healing
- 结合大模型与机器学习，从日志中理解故障语义。
- 故障检测准确率提升，系统停机时间减少40%以上。
- 适合运维团队和云平台开发者快速部署智能监控。
随着云计算系统快速发展及基础设施日益复杂，实时检测与缓解故障的智能机制变得愈发重要。传统故障检测方法难以应对现代云环境的规模与动态性。本文提出一种基于大规模语言模型（LLM）的新型AI框架，用于云系统的智能故障检测与自愈。该模型融合现有机器学习故障检测算法与大模型的自然语言理解能力，通过语义上下文处理系统日志、错误报告与实时数据流。采用多层架构，结合监督学习进行故障分类，无监督学习实现异常检测，使系统能在故障发生前预测潜在问题并自动触发自愈机制。实验结果表明，所提模型在故障检测准确率、系统停机时间减少及恢复速度方面均显著优于传统系统。
原文摘要 · Abstract (English)
With the rapid development of cloud computing systems and the increasing complexity of their infrastructure, intelligent mechanisms to detect and mitigate failures in real time are becoming increasingly important. Traditional methods of failure detection are often difficult to cope with the scale and dynamics of modern cloud environments. In this study, we propose a novel AI framework based on Massive Language Model (LLM) for intelligent fault detection and self-healing mechanisms in cloud systems. The model combines existing machine learning fault detection algorithms with LLM's natural language understanding capabilities to process and parse system logs, error reports, and real-time data streams through semantic context. The method adopts a multi-level architecture, combined with supervised learning for fault classification and unsupervised learning for anomaly detection, so that the system can predict potential failures before they occur and automatically trigger the self-healing mechanism. Experimental results show that the proposed model is significantly better than the traditional fault detection system in terms of fault detection accuracy, system downtime reduction and recovery speed.
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