Anomaly Detection in Cloud-native B5G Systems using Observability and Machine Learning COTS Solutions

Gustavo Zanatta Bruno; Karlla B. Chaves Rodrigues; Kleber Vieira Cardoso; Sand Luz Correa; Cristiano Bonato Both

doi:10.5753/jisa.2023.3551

Authors

Gustavo Zanatta Bruno University of Vale do Rio do Sinos https://orcid.org/0000-0002-1424-3404
Karlla B. Chaves Rodrigues Federal University of Goiás https://orcid.org/0009-0009-4023-3910
Kleber Vieira Cardoso Federal University of Goiás https://orcid.org/0000-0001-5152-5323
Sand Luz Correa Federal University of Goiás https://orcid.org/0000-0003-1863-4661
Cristiano Bonato Both University of Vale do Rio do Sinos https://orcid.org/0000-0002-9776-4888

DOI:

https://doi.org/10.5753/jisa.2023.3551

Keywords:

Observability, 5G Systems, Metrics, Log Processing, Machine Learning, COTS

Abstract

The advent of B5G networks has revolutionized the telecommunications landscape by transitioning hardware resources to software components, predominantly running on cloud-based infrastructures. However, this ‘softwarization’ 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, selfhealing B5G network systems.

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Anomaly Detection in Cloud-native B5G Systems using Observability and Machine Learning COTS Solutions

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