首个面向通信网络的AI运维框架，解决复杂拓扑与数据稀缺难题。
TelOps: AI-driven Operations and Maintenance for Telecommunication Networks
- 结合机制、数据与经验知识构建端到端智能运维体系
- 在真实工业网络上实现故障诊断验证，突破传统方法局限
- 适合电信运营商及网络自动化研究者参考
通信网络（TNs）在过去一个世纪中已成为数据通信最重要的基础设施。运维（O&M）对保障通信可用性、有效性与效率至关重要。与流行于IT系统（如云）的AIOps不同，通信网络运维面临三大根本挑战：网络组件的拓扑依赖性、软件高度异构性以及故障数据受限。本文提出TelOps，首个面向通信网络的AI驱动运维框架，系统融合机制、数据与实证知识。通过与AIOps的全面对比，并在真实工业通信网络上开展典型运维任务（故障诊断）的概念验证研究，证明其可行性。作为首个系统的通信网络智能运维框架，TelOps为将AI技术应用于通信网络自动化开辟了新路径。
原文摘要 · Abstract (English)
Telecommunication Networks (TNs) have become the most important infrastructure for data communications over the last century. Operations and maintenance (O&M) is extremely important to ensure the availability, effectiveness, and efficiency of TN communications. Different from the popular O&M technique for IT systems (e.g., the cloud), artificial intelligence for IT Operations (AIOps), O&M for TNs meets the following three fundamental challenges: topological dependence of network components, highly heterogeneous software, and restricted failure data. This article presents TelOps, the first AI-driven O&M framework for TNs, systematically enhanced with mechanism, data, and empirical knowledge. We provide a comprehensive comparison between TelOps and AIOps, and conduct a proof-of-concept case study on a typical O&M task (failure diagnosis) for a real industrial TN. As the first systematic AI-driven O&M framework for TNs, TelOps opens a new door to applying AI techniques to TN automation.
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