Abstract: An integrated architecture of communication, computing and caching (3C) is constructed to solve the joint optimization problem of task scheduling and resource allocation. In order to coordinate network functions and dynamically allocate limited 3C resources, the deep reinforcement learning algorithm is adopted to obtain the maximum profit function of mobile virtual network operators by jointly considering the diversity of user request services and dynamic wireless channel conditions. Simulation results show that the resource allocation scheme based on DRL is superior to the other two comparison strategies. The DRL algorithm combined with ensemble learning has a faster output speed and higher cost performance.

Key words: network slicing, multi-access edge computing, content caching, resource allocation, deep reinforcement learning, ensemble learning