TelOps: AI-driven Operations and Maintenance for Telecommunication Networks
This work tackles the problem of automating operations and maintenance for telecommunication networks, which is crucial for ensuring network availability and efficiency, representing a novel domain-specific advancement.
The paper introduces TelOps, the first AI-driven operations and maintenance framework for telecommunication networks, addressing challenges like topological dependence and restricted failure data, and demonstrates its application in a failure diagnosis case study on a real industrial network.
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.