AI-Empowered Management and Orchestration of Vehicular Systems in the Beyond 5G Era

Abstract

The complexity of orchestrating Beyond 5G services, such as vehicular, demands novel approaches to remove limitations of existing techniques, as these might cause a large delay in orchestration operations, and thus, negatively impact the service performance. For instance, the human-in-the-loop approach is slow and prone to errors, and closed loop control using rule-based algorithms is difficult to design, as an abundant number of parameters need to be configured. Applying Artificial Intelligence (Al)/Machine Learning (ML), in combination with Network Function Virtualization (NFV) and Software Defined Networking (SDN), seems a promising solution for enabling automation and intelligence that will optimize orchestration operations. In this article, we study the challenges in current ETSI NFV orchestration solutions for B5G C-V2X edge services; propose an Al/ML-based closed-loop orchestration framework; propose how and which Al/ML techniques can alleviate the identified challenges and what are the implications resulting from applying certain Al/ML techniques; and discuss A//ML-based system enablers for B5G C-V2X services.