Leveraging Infrastructure Monitoring for User Experience Forecasting in Container-Based 5G Core

Integrating intelligence in mobile networks enhances resource management and ensures Service Level Agreements (SLAs) compliance. However, the complexity of subsystems and interactions in the network-slicing lifecycle poses challenges. Existing studies often rely on simulated datasets or monitoring systems, which face limitations in generalization, privacy, and real-time responsiveness due to data quality and complexity of the Machine Learning (ML) algorithm. Although advanced monitoring systems improve accuracy, their high computational cost necessitates low-overhead alternatives. This paper demonstrates that generic computing and network metrics in container-based 5G core can accurately reveal user experience in mobile networks, achieving a 10.5232% Mean Absolute Percentage Error (MAPE) with feature-based and sequence-based ML algorithms.