Service Cache-Based Offloading and Resource Optimization Algorithm for UAV-Assisted Computing

Edge computing minimizes data transmission delay and fulfills the requirements of real-time applications for the wireless network coverage problem in edge computing. This paper proposes a resource optimization algorithm based on service caching, and designs a hybrid computing model of UAV-assisted computation offloading and service programs, in which the UAV (Unmanned Aerial Vehicle) not only carries a server for computation offloading but also serves as a relay to assist in the downloading of service programs. The objective is to minimize the system’s total energy consumption while adhering to constraints such as delay, cache capacity, and other relevant factors. Because of the non-convex nature of the objective problem, it is divided into three sub-problems for more effective handling. Through analysis, the relationship between the resource allocation ratio and total system energy consumption is established, and the simulated annealing algorithm, greedy algorithm, and genetic algorithm are used to solve these sub-problems, respectively. Finally, the global optimal solutions for location deployment, service caching, resource allocation, and offloading decisions are obtained through iteration. The simulation results show that the proposed algorithm successfully reduces the total energy consumption of the system while maintaining task completion delay constraints.