LAUSR

With the continuous development and maturity of the fifth-generation mobile network (5G) technology, the demand for multimedia service access is increasing, which poses huge challenges to the connectivity, caching capabilities, and computing capabilities of the Internet of Things (IoT) devices. Therefore, edge computing, as the key to achieving efficient edge data preprocessing and improving data access and response, is considered to be a hot technology for the next generation of mobile networks and future development. However, the current imbalance of computing resources on the edge, the lack of collaboration between nodes, and the lack of adaptability of optimization methods in a dynamic environment pose challenges to the development of edge computing. To strengthen the collaboration of nodes in the edge environment, we designed a collaborative task offloading and resource scheduling framework, including macro base station collaborative space ( $$\xi $$ BSCS) and micro base station collaborative space ( $$\mu $$ BSCS) to balance computing and caching resources in heterogeneous wireless networks. In addition, we formulate the collaborative computing offloading problem as a Markov Decision Process (MDP), and deep reinforcement learning (DRL) agents are deployed to make task offloading and resource allocation decisions. The DRL agent is deployed in each base station (BS) in a decentralized manner, observing the available computing and caching resources on the edge side, and designing an optimal resource allocation strategy for task offloading to maximize the benefits of the long-term system. The data-driven simulation results verify that the proposed scheme is effective in reducing the overall consumption of the system, maximizing the long-term benefits of edge resource allocation, and improving the success rate of task completion.