A joint optimization scheme for task offloading and resource allocation based on edge computing in 5G communication networks

With the development of 5G communication networks and the popularization of intelligent terminals, the computing resource intensive characteristic of various new applications poses a severe challenge to the task processing ability of intelligent terminals. In order to improve the efficiency of task processing, a joint optimization scheme for task offloading and resource allocation based on edge computing in 5G communication networks is proposed. Firstly, combining edge computing and Device-to-Device communication technologies, we propose three modes for processing computationally intensive tasks based on multi-user network system model for 5G edge networks, including local computing, fog node computing, edge node computing. Then, the corresponding time delay model, task execution model and offloading energy consumption computing model are constructed for these three computing modes. Finally, the problem of computing task offloading is transformed into a joint optimization problem of time delay and energy consumption, including optimization problems such as CPU frequency, offloading decision, transmission bandwidth allocation and power allocation of offloading users. Besides, the interior point method is utilized to solve this problem. Simulation platform is used to demonstrate the performance of our proposed scheme. The experimental results show that the scheme can effectively reduce the time delay and energy consumption of terminal tasks, which improves the efficiency of task processing and the experience quality of end users.