Joint Offloading Decision and Resource Allocation of 5G Edge Intelligent Computing for Complex Industrial Application

5G mobile edge computing (MEC) can be used in intelligent manufacturing. In complex industrial application scenarios, this paper tries to address the problem of energy consumption optimization of customer task unloading and resource rescheduling. Specifically, we employ 5G wireless private network and MEC computing resources between 5G private network and MEC. Then, we use game theory algorithm to optimize the user task unloading and resource rescheduling allocation, which is mainly measured by the minimum total time required to complete the task and energy consumption. The problem is a combinatorial nonlinear programming algorithm, involving joint optimization of task offloading decision, user side's uplink transmission energy consumption, MEC server's resource allocation. The solution includes the resource allocation of fixed task unloading decision, and the resource allocation optimization of task unloading. Results verify the proposed solution can improve the efficiency of task scheduling.