基于能耗与延迟优化的移动边缘计算任务卸载模型及算法

随着移动边缘计算的兴起，如何处理边缘计算任务卸载成为研究热点问题之一。针对多任务-多边缘服务器的场景，本文首先提出一种基于能量延迟优化的移动边缘计算任务卸载模型，该模型考虑边缘设备的剩余电量,使用时延、能耗加权因子计算边缘设备的总开销，具有延长设备使用时间、减少任务卸载时延和能耗的优点。进一步提出一种基于改进遗传算法的移动边缘计算任务卸载算法，将求解最优卸载决策的问题转化为求解种群最优解的问题。对比仿真实验结果表明，本文提出的任务卸载模型和算法能够有效求解任务卸载问题，改进后的任务卸载算法求解更精确，能够避免局部最优解，利于寻找最优任务卸载决策。

Mobile Edge Computing Task Offloading Model and Algorithm Based on Energy Consumption and Delay Optimization

With the rise of mobile edge computing, how to handle the offloading of edge computing tasks has become one of the hot research issues. For the multi-task-multi-edge server scenario, this paper first proposes a mobile edge computing task offloading model based on energy and delay optimization. This model takes into account the remaining power of the device, and uses the delay and energy consumption weighting factors to calculate the total cost of edge devices.And it has the advantages of prolonging equipment use time, reducing task offloading delay and energy consumption. Then we propose a mobile edge computing task offloading algorithm based on an improved genetic algorithm, which converts the problem of solving the optimal offloading decision into a problem of solving the population optimal solution. Comparative simulation experiment results show that the task offloading model and algorithm proposed in this paper can effectively solve the task offloading problem. The improved task offloading algorithm has a more accurate solution, can avoid the local optimal solution, and is helpful to find the best task offloading decision.