Joint optimization algorithm for 6G network task offloading and fine-grained slice resource scheduling

In response to the diverse business needs of the future in all domains and scenarios, 6G networks need to provide scenario-based and personalized service capabilities. Aiming at the problem of quality assurance of fine-grained business services in the future, a joint optimization algorithm for 6G network task offloading and fine-grained slice resource scheduling was proposed, which jointly considered the calculation offloading of multiple MECs and the resource scheduling of network slices, and minimized the execution delay and energy consumption cost of the task within limited resources. Then the A3C reinforcement learning algorithm of asynchronous training was used to solve it. The simulation results show that, compared with the traditional algorithm, the proposed algorithm can reduce the computing cost while meeting the business needs of users. Additionally, the algorithm converges fast and can realize fast decision-making.