Predictive OMS Switchover towards Proactive Disaster Recovery in 5G Networks

• Charles F. Santos IFRN / UFRN
• Augusto V. Neto UFRN
• Ramon R. Fontes UFRN
• Roger Immich UFRN
• Vicente Sousa Jr. UFRN
• Helber W. da Silva IFRN / UFRN

DOI: https://doi.org/10.5753/sbrc.2025.6402

Abstract

The increasing complexity and critical nature of Operations and Maintenance Systems (OMS) in 5G mobile networks necessitate robust Disaster Recovery (DR) solutions to ensure continuous service and minimal downtime. Disaster Recovery Systems (DRS) are essential for maintaining network resilience by facilitating seamless failover and recovery processes. The primary function of switchover enables a DRS to ensure 5G service continuity during unforeseen disasters. This research addresses the limitations of traditional rulebased decision-making techniques that often rely on binary switchover logic, which can be inadequate for the intricate demands of 5G networks. We propose the predictive OMS Switchover (pOM2S), a machine learning (ML)-driven approach that utilizes data on computing and networking metrics to estimate the switchover period for each redundant candidate. In doing so, our solution is able to select the backup OMS that can perform the fastest in a disaster event. Evaluated in a 5G emulation testbed simulating real-world conditions, results demonstrated Random Forest’s superior accuracy (MAE: 1.68s, R²: 0.94) over Linear Regression, Artificial Neural Networks, and Support Vector Machine techniques. Experimental results validate pOM2S’s effectiveness in balancing predictive precision and operational practicality, suggesting that the predictive decision-making method relies on a highly accurate model.

Keywords: 5G/6G networks and applications, Fault tolerance and resilience Future Internet, Management, operation, design, and analysis of networks, Network simulation and emulation Performance, scalability, and reliability, Anomaly and Attack Detection and Prevention

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