Multi Access Edge Computing Task Unloading Algorithm Based on Genetic Ant Colony Algorithm Fusion

Aiming at the problem that the computing resources of network terminals are insufficient and the task unloading time is too long, a multi access edge computing task unloading algorithm based on genetic ant colony algorithm is proposed. In the scenario of multi-user and multi MEC server, a edge computing task unloading model is established, and a multi access edge computing model is constructed. A computing task can be processed by the user equipment itself or transmitted to the MEC server for execution. In order to improve the quality of user experience, the task offloading problem is formulated to minimize the average delay of user tasks under the constraints of network and resource conditions, and the objective function and constraints are designed according to this. The task offloading algorithm is designed based on the fusion of genetic-ant colony algorithm, and the paths deviating from the optimal point are eliminated by calculating the fitness, which reduces the calculation amount of the algorithm and speeds up the algorithm iteration. The test results show that the performance of task offloading algorithm based on genetic-ant colony fusion is better than ant colony and particle swarm algorithm in terms of task offloading energy consumption and task average delay.