面向5G的移动边缘计算中的激励机制

Incentive mechanism of mobile edge computing in 5G networks

现有的移动设备(Mobile Device,MD)受到自身物理体积的限制,其计算能力难以满足5G场景下的计算需求,因此需要借助移动边缘计算(Mobile Edge Computing,MEC)来高效地完成计算任务.为了激励边缘节点提供服务,在非竞争环境与竞争环境下分别提出了相应的激励机制算法.针对非竞争环境,利用经典的经济学规律进行建模,并采用凸优化方法对该利益最大化问题进行求解.对于竞争环境,基于拍卖理论中的威克瑞拍卖与维克瑞-克拉克-格罗夫斯(Vickery-Clarke-Groves,VCG)拍卖,分别设计了两种竞争环境下的激励机制算法.仿真结果表明:竞争环境下的激励机制在收益性能方面与非竞争环境下的理想情况十分接近,并且算法性能不会随用户数量上升而明显下降,可以较好地应对5G的海量运算数据场景.

Existing Mobile Devices(MD)are limited by their physical sizes,and their computing capacity is difficult to meet the computing requirements in 5G scenarios.Therefore,Mobile Edge Computing(MEC)is required to efficiently complete computing tasks.In order to stimulate edge nodes to provide services,this paper proposes corresponding incentive mechanisms respectively under non-competitive environment and competitive environment.For the non-competitive environment,the classic economic theories are used for modeling,and the convex optimization method is used to maximize the profit.For the competitive environment,this paper designs two kinds of incentive mechanisms respectively based on the Vickery auction and the Vickery-Clarke-Groves(VCG)auction in the auction theory.The simulation results show that the incentive mechanism in a competitive environment is very close to the ideal non-competitive environment in terms of revenue performance,and the algorithm performance will not significantly deteriorate with the increase in the number of users.It can better deal with 5G massive data scenarios.