发表机构
Microsoft Azure Networking（微软Azure网络部门）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文追溯云网络AI运维的演进，提出含五代的成熟度模型，分析过渡障碍，给出自主运维的实用路线图。
AI 中文摘要
过去十年，云网络基础设施的运营模式发生了根本性转变，从人工驱动的手动故障排查，逐步经过脚本自动化、基于规则的系统、AI辅助运维（AIOps），发展为完全自主的故障解决。本文追溯云网络基础设施中AI运维（AIOps）的演进过程，梳理出支撑各代际过渡的架构模式、组织挑战与技术转折点。基于超大规模网络基础设施的生产运营经验，本文提出了成熟度模型，将运营分为五个不同代际，分析了阻碍代际间过渡的技术与组织障碍，明确了表明已具备提升自主性的就绪度指标。研究表明，从响应式到自主运维的路径不仅是技术问题，还需要工具、信任框架、知识管理实践及运营文化的协同演进，研究结果为逐步采用自主AI运维的基础设施组织提供了实用路线图。
英文摘要
The operational model for cloud network infrastructure has undergone a fundamental transformation over the past decade. What began as manual, human-driven troubleshooting has evolved through scripted automation, rule-based systems, and AI-assisted operations into fully autonomous incident resolution. This paper traces the evolution of AI operations (AIOps) in cloud network infrastructure, identifying the architectural patterns, organizational challenges, and technical inflection points that enabled each generational transition. Drawing from production experience operating network infrastructure at hyperscale, we present a maturity model that characterizes five distinct operational generations, analyze the technical and organizational barriers that impede transitions between generations, and document the metrics that indicate readiness for increased autonomy. We show that the path from reactive to autonomous operations is not merely a technology problem but requires co-evolution of tooling, trust frameworks, knowledge management practices, and operational culture. Our findings provide a practical roadmap for infrastructure organizations seeking to adopt progressively autonomous AI operations.
Comments12 pages, 5 figures, 3 tables
