Anomaly Detection in Cloudnative B5G Systems using Observability and Machine Learning COTS Solutions

The advent of B5G networks has revolutionized the telecommunications landscape by transitioning hard-ware resources to software components, predominantly running on cloud-based infrastructures. However, this 'soft-warization' extends across the radio access, transport, and core networks, introducing complex challenges in real-time network management. In this context of the 'softwarization', it is imperative to make the behavior of B5G systems readily observable for effective management and fault diagnosis. This article presents a comprehensive empirical investigation of observability within a B5G system, specifically focusing on its radio access and core networks. The study enhances the system's observability by combining advanced metric analysis and log parsing. Our method integrates Commercial Off-The-Shelf machine learning algorithms to diagnose anomalies and automate failure tasks. Besides that, our evaluation of the Cloud-Native Observability Tools services revealed a significant memory footprint, accounting for 86% of the total memory usage and 22% overall CPU utilization. The findings also highlight that our approach mitigates the issue of non-standardization in log data, thereby facilitating proactive failure anticipation. This study can aggregate significant value for developing automated, self-healing B5G network systems.