Research On Computing Offloading Strategy Based On Energy Cost Minimization

In recent years,the computing offloading technology in Mobile Edge Computing(MEC)can effectively solve the situation that terminal devices often face insufficient resources and computing power when processing computing-intensive and delay-sensitive applications.Optimizing the computing offloading strategy can maximize the utilization of computing resources.However,with the development of science and technology,the application of IoT devices has become more and more popular,and the energy consumption of terminal devices has also increased.Therefore,it is of great significance to reduce the energy consumption cost of the terminal device when completing the computing offloading task.This paper starts with the problem of minimizing energy consumption and studies the offloading strategy of optimal position calculation for UAV in parallel task allocation,and then further studies the economic cost of the offloading strategy of electric vehicle-assisted computing based on the minimization of energy consumption cost.The combination of electric vehicles provides a feasible idea for the implementation of mobile edge computing applications.(1)The computing offloading strategy for minimizing the total energy consumption in the scenario of a single unmanned aerial vehicle(UAV)with multiple user equipment is studied.Within a certain area,the user equipment requests computing resources from the UAV for task offloading.In order to make full use of the limited computing resources of UAV,this paper proposes an optimal location computing offloading strategy for UAV based on parallel task allocation.Aiming at the total energy consumption of UAV and user equipment,the optimization problem is a mixed-integer non-linear programming(MINLP)problem,solved by using block coordinate descent(BCD)and convex optimization techniques.Finally,the calculation offloading strategy EMUM(Energy consumption Minimization strategy for UAV-assisted Mobile edge computing)is obtained,which is approximate to the optimal location,which achieves the goal of minimizing the total energy consumption of UAV and all user equipment.The computing offloading strategy for minimizing the energy consumption cost of all user equipment in the scenario of multiple user equipment at multiple electric vehicle edges(install MEC server on electric vehicle)in an urban environment is studied.In this scenario,multiple electric vehicle edges(EV-edges)provide computing offloading services to multiple user equipment.All pricing strategies for energy consumption at the edge of electric vehicles are independent.Therefore,the user equipment selects different electric vehicle edges for task offloading according to the principle of minimizing the energy consumption cost of all user equipment.The proposed optimization problem is an MINLP problem,and the corresponding optimal value is solved jointly by using a block coordinate descent method and heuristic algorithm.Finally,an approximate optimal computing offloading strategy EMECM(Electric-vehicle assisted Mobile edge computing offloading strategy for Energy consumption Cost Minimization)is obtained,which achieves the goal of minimizing the sum of energy consumption costs of all user equipment.