Research On Task Offloading Strategy In Mobile Edge Computing

As an expansion from cloud computing to edge networks,MEC has become one of the force of the network change.Most mobile devices are computing intensive and delay sensitive applications.However,limited by physical design,the need of the limited resources and the not simple appliciation requirements is growing.Mobile edge computing,by sinking computing power to distributed base stations,provides computing power for applications in the vicinity of mobile terminals,and can meet different services.Task offloading is one of the methods to reduce energy consumption and delay in mobile edge computing.Through a certain task offloading strategy,tasks with a large amount of calculation on the mobile terminal are submitted to the MEC server with richer resources,with the help of cloud computing processing capabilities to support and improve its own computing performance,reduce energy costs,shorten task execution time,and obtain good results User experience.The Internet of Vehicles is a huge interactive network.It is a popular filed in MEC,and it is of great help to the realization of smart transportation.The mode of internet of vechines and the accuracy of numbers and informate have a high demand,also need timely safety protection.The realization of these applications requires a lot of energy consumption and puts forward high requirements for time delay.Therefore,research and improvement of an offloading strategy suitable for the Internet of Vehicles,to ensure the real-time requirements of the Internet of Vehicles,while reducing computing energy consumption,is meaningful for the application of mobile edge computing in the Internet of Vehicles.This paper is based on the character of the internet of vehicle,considerd the wireless and computing question of offloading decision.At the same time,according to the difference of MEC servers price decision,his chapter first proposes a unified pricing strategy(UPM),uses reverse induction to analyze the characteristics of the game structure,also design a best price to resolve and coordinated offloading strategies of computing tasks.A cooperative offloading strategy of computing tasks based on improved particle swarm algorithm is proposed.It is expected to effectively reduce user overhead costs and increase MEC server profits.In the end,the results has showd that the improved of the algorithm is effetive.