Joint Task Offloading and Resource Allocation for Vehicular Edge Computing Based on V2I and V2V Modes

• Published in: IEEE transactions on intelligent transportation systems 2023-04, Vol.24 (4), p.4277-4292
• Main Authors: Fan, Wenhao, Su, Yi, Liu, Jie, Li, Shenmeng, Huang, Wei, Wu, Fan, Liu, Yuan'an
• Format: Article
• Language: English
• Subjects: Algorithms; Benders decomposition; Complexity; Computation offloading; Delays; Edge computing; Heuristic methods; Internet of Vehicles; Optimization; Resource allocation; Resource management; Roadsides; Task analysis; Task scheduling; V2I; V2V; Vehicle-to-infrastructure; Vehicles; Vehicular ad hoc networks
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2022.3230430

In an internet of vehicle (IoV) scenario, vehicular edge computing (VEC) exploits the computing capabilities of the vehicles and roadside unit (RSU) to enhance the task processing capabilities of the vehicles. Resource management is essential to the performance improvement of the VEC system. In this paper, we propose a joint task offloading and resource allocation scheme to minimize the total task processing delay of all the vehicles through task scheduling, channel allocation, and computing resource allocation for the vehicles and RSU. Different from the existing works, our scheme: 1) considers task diversity by profiling the tasks of the vehicles by multiple attributes including data size, computation amount, delay tolerance, and task type; 2) considers vehicle classification by dividing the vehicles into 4 sets according to whether they have task offloading requirements or provide task processing services; 3) considers task processing flexibility by deciding for each vehicle to process its tasks locally, to offload the tasks to the RSU via V2I (Vehicle to Infrastructure) connections, or to the other vehicles via V2V (Vehicle to Vehicle) connections. An algorithm based on the Generalized Benders Decomposition (GBD) and Reformulation Linearization (RL) methods is designed to optimally solve the optimization problem. A heuristic algorithm is also designed to provide the sub-optimal solution with low computational complexity. We analyze the convergence and complexity of the proposed algorithms and conduct extensive simulations in 6 scenarios. The simulation results demonstrate the superiority of our scheme in comparison with 4 other schemes.