A Monitoring, Observability and Analytics Framework to Improve the Sustainability of B5G Technologies

As mobile generations advance, blurring the lines between physical and digital worlds, the deployment of ultra-dense networks poses challenges in resource allocation and energy efficiency. This article revolves around the environmental sustainability of Fifth Generation (5G) and Beyond 5G (B5G) mobile networks as cloud-native technologies, highlighting the use phase. It aims to represent a monitoring, observability and analytical framework to devise a feature selection methodology within well-known monitoring tools, aligning with environmental sustainability objectives. Notable open-source monitoring applications (e.g., Kepler and Scaphandre) are employed to collect data on resource utilization and energy consumption for containers and physical servers. Dependence criteria indexes, Hilbert-Schmidt Independence Criterion (HSIC) and Pearson, identify interdependencies among timeseries from different tools. Addressing challenges, the paper resolves inconsistent dataset lengths, highlights the Pearson-HSIC trade-off, and underscores the need for alignment in feature selection with compatibility to resource utilization. Key findings include Scaphandre's underestimation of memory impact on power consumption, Kepler's limitations in host metrics. Finally, the results enable the development of an anomaly detection mechanism.