5G-slicing-enabled scalable SDN core network: Toward an ultra-low latency of autonomous driving service

5G networks are anticipated to support a plethora of innovative and promising network services. These services have heterogeneous performance requirements (e.g., high-rate traffic, low latency and high reliability). To meet them, 5G networks are entailed to endorse flexibility that can be fulfilled through the deployment of new emerging technologies, mainly Software-Defined Networking (SDN), Network Functions Virtualization (NFV) and Network Slicing. In this paper, we focus on an interesting automotive vertical use case: autonomous vehicles. Our aim is to enhance the quality of service of autonomous driving application. To this end, we design a framework that uses the aforementioned technologies to enhance the quality of service of the autonomous driving application. The framework is made of i) a distributed and scalable SDN core network architecture that deploys fog, edge and cloud computing technologies; ii) a network slicing function that maps autonomous driving functionalities into service slices; and iii) a network and service slicing system model that promotes a four-layer logical architecture to improve the transmission efficiency and satisfy the low latency constraint. In addition, we present a theoretical analysis of the propagation delay and the handling latency based on GI/M/1 queuing system. Simulation results show that our framework meets the low-latency requirement of the autonomous driving application as it incurs low propagation delay and handling latency for autonomous driving traffic compared to best-effort traffic.