FLAM: Locating and Mitigating 5GC Network Failure with Knowledge Graphs in China Telecom’s Network

Abstract

China Telecom’s fifth-generation core (5GC) network is complicated owing to its characterizations of service-based architecture and network functions virtualization. Thus, it is vulnerable to network failures. When a failure occurs, operations and maintenance (O&M) experts need to first analyze the root cause based on their professional experience, and then recommend an available solution for the failure. However, 5GC network failures occur frequently, and most of them are similar. Thus, inviting O&M experts to the 5GC network scene is expensive and time-consuming (hourly level). In this paper, we propose a knowledge and data-driven 5GC network called failure location and automated mitigation (FLAM) mechanism. Particularly, FLAM demonstrates the expertise in dealing with various network failures by using knowledge graphs. Four state-of-the-art machine learning algorithms were compared in FLAM to determine which one can better locate the root cause of network failures. A real-time checking module was also designed to automatically diagnose the related network functions for network failures. Based on China Telecom’s real-wild data of network failures, the proposed mechanism was evaluated considering in the metrics of algorithm complexity and location accuracy. Experimental results show that the decision tree model had an accuracy of ∼ 99% for locating the root cause of network failures, outperforming the random forest, support vector machine, and k-nearest neighbor algorithms.