Internet Of Vehicles Resource Allocation And Task Offloading Method Based On Mobile Edge Computing

With the gradual popularity of mobile edge computing,various applications with demanding delay requirements have emerged one after another.Although the cloud can provide powerful computing services for end users,it cannot provide real-time services due to the long distance.Edge computing is a good solution.Mobile edge computing offloads tasks generated by end users to edge servers,and uses technologies such as virtualization and software-defined networking to enable end users to flexibly obtain computing resources and other services.Applying mobile edge computing to the Internet of Vehicles architecture can effectively solve many problems.In order to be able to use the computing resources of edge devices to provide people with high-quality services close to the user side,the text researches and analyzes the computing offloading methods in the Internet of Vehicles environment.The task requirements generated by in-vehicle applications can be offloaded to the roadside unit equipped with edge servers.The processing by the side unit can also be offloaded to a vehicle close to the end user for processing,thereby maximizing the service efficiency of the Internet of Vehicles.This paper optimizes task offloading based on the K-means clustering algorithm,and compares the effect of random selection algorithms on the average completion time of the system.This paper conducts simulation experiments on the Matlab R2018 b platform.The results show that the K-means clustering algorithm is better than the random selection algorithm,and the advantages of the clustering algorithm in this paper are verified.Finally,a joint computing offloading and resource allocation mechanism is designed for the mobile edge computing architecture in the Internet of Vehicles environment.In this mechanism,the original multi-objective problem is solved into two problems,the less complex proportional resource allocation algorithm and the improved particle swarm algorithm.The simulation results prove that this mechanism greatly reduces the complexity of the algorithm compared to other algorithms,and user utility is always high.