Abstract: 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.

Key words: mobile edge computing, task offloading, genetic algorithm