Predictive Disaster Recovery for Multi-Redundant Operations and Maintenance 5G Network Systems
Charles F. Santos, Augusto Neto, Ramon R. Fontes, Roger Immich, Vicente A. de Sousa, Helber W. Da Silva · 2025
The rapid evolution of 5G networks has introduced unprecedented challenges in maintaining service continuity, particularly in the eHealth mission-critical vertical. This paper presents the Proactive Disaster Recovery System (PDRS), a machine learning-driven disaster recovery system for 5G Operations and Maintenance Systems (OMS) in mission-critical eHealth verticals. Unlike traditional reactive approaches using binary switchover logic, PDRS enables proactive failover by: (1) continuously monitoring OMS status through KPIs, (2) predicting disasters via real-time analytics, and (3) selecting the optimal OMS backup instances from multi-redundant candidates using migration cost estimates. Proof-of-concept evaluation demonstrates PDRS’ superiority over baseline methods in maintaining service continuity, particularly for low-latency eHealth applications requiring 99.999% availability. Results highlight the necessity of predictive strategies for 5G network resilience in critical medical services.