5G-Slicing-Enabled Scalable SDN Core Network: Toward an Ultra-Low Latency of Autonomous Driving Service

TLDR

5G networks are expected to support diverse services with varying performance needs, including high‑rate traffic, low latency, and high reliability. The study targets autonomous vehicles, aiming to improve the quality of service for autonomous driving applications. The authors propose a distributed, scalable SDN core that integrates fog, edge, and cloud computing, employs network slicing to map autonomous driving functions into service slices, and uses a four‑layer logical architecture with GI/M/1 queuing analysis to reduce propagation and handling latency. Simulations demonstrate that the framework meets the low‑latency requirement for autonomous driving traffic, achieving lower propagation and handling delays than best‑effort traffic.

Abstract

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 1) a distributed and scalable SDN core network architecture that deploys fog, edge and cloud computing technologies; 2) a network slicing function that maps autonomous driving functionalities into service slices; and 3) 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.