TelOps: AI-driven Operations and Maintenance for Telecommunication Networks

TL;DR

TelOps introduces a pioneering AI-driven framework tailored for telecommunication network operations and maintenance, addressing unique challenges and demonstrating effectiveness through a real-world case study.

Contribution

It is the first systematic AI-based O&M framework for TNs, incorporating mechanisms, data, and empirical knowledge to enhance network management.

Findings

Effective failure diagnosis in a real industrial TN

Comparison shows TelOps outperforms traditional methods

Framework addresses topological and heterogeneity challenges

Abstract

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.