AI 中文总结
该研究提出名为SH-PDOPS的AI驱动云原生企业可靠性框架，整合机器学习、Kubernetes等技术，实现故障检测、自修复等功能，提升企业运营效率与系统韧性。
AI 中文摘要
本文提出一种AI驱动的云原生企业可靠性框架，旨在提升现代分布式基础设施中的预测分析、智能DevOps自动化与系统韧性。该框架整合机器学习模型、Kubernetes编排、可观测性流水线及自动事件响应机制，以增强可靠性工程实践。研究利用云原生技术探索预测性故障检测、异常监控、基础设施自修复及CI/CD优化。实验评估显示，该框架可提升企业环境的运营效率、减少停机时间并增强可扩展性，为将人工智能与DevOps方法论结合以实现自适应自主基础设施管理提供了实用方案。
英文摘要
This paper presents an AI-driven cloud-native enterprise reliability framework designed to improve predictive analytics, intelligent DevOps automation, and system resilience in modern distributed infrastructures. The proposed framework integrates machine learning models, Kubernetes orchestration, observability pipelines, and automated incident response mechanisms to enhance reliability engineering practices. The study explores predictive failure detection, anomaly monitoring, self-healing infrastructure, and CI/CD optimization using cloud-native technologies. Experimental evaluation demonstrates improved operational efficiency, reduced downtime, and enhanced scalability for enterprise environments. The framework provides a practical approach for combining artificial intelligence with DevOps methodologies to achieve adaptive and autonomous infrastructure management.
Comments6 pages, 7 figures, submitted with the github link and for educational purposes
