Slicing-Based Task Offloading in Space-Air-Ground Integrated Vehicular Networks

A slicing-based collaborative task offloading framework for space-air-ground integrated vehicular networks is proposed in this study, which can provide differentiated quality-of-service (QoS) guarantees for task offloading for high-speed vehicles while maximizing the number of completed tasks. A service-oriented radio access network (RAN) slicing framework is presented that supports slicing window adaptation, spectrum and computing resource orchestration, and collaboration among heterogeneous base stations. Based on the queuing model, the collaborative decision-making of RAN slicing and task offloading is modeled as a problem of maximizing the number of long-term task completions, which consists of three subproblems-slicing window division, resource slicing, and task scheduling-which are solved by a multi-access edge computing (MEC)-enabled controller, forming a closed loop with the slicing window as the period. When a new slicing window arrives, the controller determines its duration according to task traffic fluctuations and allocates resources to RAN slices through an optimization method. A double deep Q-learning network (DDQN)-based algorithm is developed for scheduling workflow on small time scales within a slicing window. Simulation results demonstrate that the proposed scheme performs better than existing approaches in terms of adaptability, task completion rate, and control overhead.