Research on task offloading strategy of mobile edge computing in 5G environment

Abstract

With the wide application of 5G technology, more and more computing-intensive tasks and delay-sensitive tasks need to be calculated and processed on user equipment, but limited by the computing power and storage capacity of user equipment, these tasks cannot be efficient processing. The emergence of mobile edge computing (MEC) makes it possible. In this paper, we consider task offloading on Small Cell Network (SCN) structures unique to 5G. Under this network structure, a computational offloading strategy for joint optimization of forward and backward links is designed and implemented. Considering the front-end link and the backward link comprehensively, a computational offloading strategy model aiming at minimizing the total energy cost is established under the premise of delay limitation. Then, the objective function that needs to be optimized for energy is established according to the model, and the objective function is optimized by the improved artificial fish swarm algorithm. Finally, through the simulation of the algorithm, the performance of the improved algorithm is proved.