Multi-objective Optimization of D2D Collaborative MEC Based on Improved NSGA-Ⅲ
In the current mobile edge computing(MEC),since tasks are directly uploaded to the MEC server for execution,there are problems such as high computing pressure on the edge server and insufficient utilization of resources on idle mobile devices.Using idle devices in the edge network for collaborative computing can realize rational utilization of user's idle resources and en-hance the computing capacity of MEC.Therefore,a device-to-device(D2D)collaborative MEC for partial offloading(DCM-PO)is proposed.In this model,in addition to local computing and MEC server computing,part of the tasks can be uploaded to idle D2D devices for auxiliary computing.First,a multi-objective optimization problem is established to minimize the delay,energy con-sumption and cost of the edge network.Then,the non-dominated sorting genetic algorithm Ⅲ(NSGA-Ⅲ)is improved in the as-pects of multi-chromosome mixed coding,adaptive crossover rate and mutation rate,so that it is suitable for solving the multi-ob-jective optimization problem in the DCM-PO.Finally,simulation results show that,compared with the baseline MEC,the DCM-PO has advantages in several performance indicators.
Mobile edge computingDevice-to-DeviceTask offloadingMulti-objective optimizationNSGA-Ⅲ
万方数据
维普
