Analyzing the PMachine-Learning-Based 5G Network Function Scaling via Black- and White-Box KPIsower Consumption in Cloud-Native 5/6G Ecosystems

The diffusion of the Fifth-Generation (5G) of mobile radio networks will be the main driver in the digital transformation towards a new hyper-connected society. In order to satisfy the stringent demands of 5G-ready applications over the limited resources available at the edge, scaling mechanisms become crucial to guarantee the performance levels envisaged for 5G. Such mechanisms must be able to automatically perform according to the real-time user demands, the availability of computing resources and the state of Network Functions (NFs) and applications. In this context, this paper proposes a deep learning model, based on Artificial Neural Networks (ANNs), for the dynamic and automated orchestration of NFs. The novelty of this model is its independence from specific 5G NF implementations; this is due to the nature of the Key Performance Indicators (KPIs) used in this work, which are related to both execution environment (standard “black-box” KPIs) and standard 5G APIs (“whitebox” KPIs). Results obtained on the orchestration of a Session Management Function (SMF) reach an accuracy of 97~98% for the training and validation phases and above 95% for the deployed model, as well as higher overall accuracy by ~5% and computational resource savings with respect to a thresholdbased scheme