【摘要】 移动边缘计算中本地设备可以将计算任务卸载到靠近网络的边缘节点上执行，只将计算结果回传至用户端，从而减小传输时延，降低移动设备的功耗，减轻客户端的负载压力，还可以减少核心网络的计算负载。针对复杂多类边缘节点的移动边缘计算环境，建立了基于线性规划松弛的三级计算任务卸载决策模型，称为CART-CRITIC-LR(CCLR)。首先通过分类回归决策树算法(CART)筛选出本地执行的计算任务；然后采用多属性决策的CRITIC算法确定3个性能指标的权值分配；最后，基于线性规划松弛(LR)对计算卸载问题建模，使计算任务卸载决策的总时延、总能耗和总成本最优。实验比较了其他计算卸载策略的能耗、成本、延迟时间等指标，结果表明CCLR卸载决策算法在保证多目标全局最优的同时，实现了总时延最短，说明了所提算法的有效性与适用性。更多还原

【Abstract】 In the mobile edge computing(MEC),the local device can offload tasks to the edge node near the network for computation processing, thereby reducing the delay, power consumption and overload of the client, also the computing loading core network.For the complex MEC environment of multi-type edge nodes, a three-stage computing offloading decision is modeled based on linear programming relaxation, that is CART-CRITIC-LR(CCLR) algorithm.First, the classification and regression decision tree algorithm(CART) is used to screen out the locally executed calculation tasks.Secondly, the multi-attribute decision-making algorithm(CRITIC) is used to determine the weight of the three performance indicators respectively.Then the calculation offloa-ding problem is modeled as a linear programming relaxation(LR) to optimize the equilibrium solutions among the total delay, total energy consumption and total cost.Each offloading strategy is analyzed by comprehensively comparing the energy consumption, cost, delay.experimental results show that the CCLR algorithm achieves the shortest total delay while ensuring the multi-objective global optimization, which illustrates the effectiveness and applicability of the algorithm.更多还原