Research On Joint Task Offloading And Resource Management Tecghnologies For Vehicular Edge Computing

By applying edge computing technology to the Internet of Vehicles,the computing,storage,and communication resources of vehicles and base stations can be combined to enhance the task processing capabilities of Internet of Vehicles applications.Through task offloading,task caching and resource allocation,the task processing delay can be effectively reduced,the utilization of system resources can be improved,and the purpose of ensuring user service quality and enhancing system performance can be achieved.Task offloading and resource management technology are the core of the edge computing system of the Internet of Vehicles.However,the existing work in this field fails to make full use of the various communication modes of vehicles,lack of joint optimization of various related resources,and lack of consideration for different driving states of vehicles.In view of the above problems,this paper deeply studies the joint task offloading and resource management technology for the edge computing of the Internet of Vehicles.The main research contents include:(1)We propose a joint multi-type task offloading and resource allocation scheme to minimize the total task processing delay of all vehicles through optimally scheduling the tasks,allocating the wireless channels,and computing resources.In our scheme,the tasks are divided into different types.The Base Station(BS)can process all types of tasks,whereas,a vehicle only supports a limited number of task types.The vehicles are classified into 4 sets according to whether they generate task offloading requirements and provide task offloading services.Each vehicle in the system can choose to process its tasks locally,to offload the tasks to the BS via a V2I connection,or to another vehicle via a V2V connection.A task scheduling and resource allocation algorithm based on the Reformulation Linearization(RL)and Generalized Benders Decomposition(GBD)techniques is designed to optimally solve the Mixed Integer Non-linear Programming(MINLP)optimization problem.A heuristic algorithm is also designed to provide the sub-optimal solution with low computational complexity.The superiority of our scheme is demonstrated in extensive simulations through comparisons with several other schemes of existing works.(2)A joint task offloading and resource allocation scheme is proposed for a parked-and-moving-vehicles-assisted MEC scenario consisting of multiple devices,parked vehicles,and moving vehicles covered by a BS equipped with an edge server.The tasks of the devices can be either offloaded to the BS or further offloaded from the BS to the vehicles.The service time that a moving vehicle can provide its task offloading service before it moves out of the coverage of the BS,is taken into account of our system model.Our scheme aims to minimize the total priority-weighted task processing delay for all the devices through offloading the tasks to the edge server or the vehicles,allocating the wireless channels of the BS,and allocating the computing resource of the edge server and the vehicles.A generalized benders decomposition and reformulation linearization-based iterative algorithm is designed to obtain the optimal solution to the optimization problem,and a two-stage heuristic algorithm is also given to provide near-optimal solutions with low computational complexity.The simulation results demonstrate the superiority of our scheme in seven different scenarios by comparing it with three other schemes.(3)For the IoV scenario where the terminal and the vehicle coexist,considering that the terminal is in the same scene and may generate the same task at the same time,we build a cache-enhanced terminal task offloading model architecture,taking into account the computing and cache resources of the vehicle and base station For the scheme of assisting terminal task offloading,an optimization problem is proposed to minimize the overall delay of the terminal.We propose a heuristic algorithm for decoupling task caching and task offloading,and design six simulation environments to compare with three schemes to verify the feasibility of the proposed scheme.The joint resource management technology of mobile edge computing in a heterogeneous wireless access environment in this thesis has a significant improvement in MTs’ and vehicles’ task processing.The research results can provide theoretical references for related research in the field of mobile edge computing.