Research on Task Offloading Strategy Based on Genetic Algorithm

Existing mobile edge computing research focuses on optimizing latency and energy consumption while ignoring the impact of server load imbalance. In this paper, we consider the terminal device uploads the computing task to the nearest AP, and the AP offloads the task to the MEC server through multi-hop communication. Aiming at the edge server load problem of the system and the extra delay that may be caused by multi-hop communication, we established a multi-objective optimization problem with edge server load balancing and average offloading delay as the optimization goals. We propose an offloading decision based on a genetic algorithm, effectively optimizes the load balancing of task offloading. The effectiveness of the proposed algorithm is verified by a large number of simulations.