Collaborative Edge Computing and Caching With Deep Reinforcement Learning Decision Agents

Large amounts of data will be generated due to the rapid development of the Internet of Things (IoT) technologies and 5th generation mobile networks (5G), the processing and analysis requirements of big data will challenge existing networks and processing platforms. As the most promising technology in 5G networks, edge computing will greatly ease the pressure on network and data processing analysis on the edge. In this paper, we considered the coordination between compute and cache resources between multi-level edge computing nodes (ENs), users under this system can offload computing tasks to ENs to improve quality of service (QoS). We aimed to maximize the long-term profit on the edge, while satisfying the low-latency computing of the users, and jointly optimize the edge-side node offloading strategy and resource allocation. However, it is challenging to obtain an optimal strategy in such a dynamic and complex system. To solve the complex resource allocation problem on the edge and make edge have certain adaptation and cooperation, we used double deep Q-learning (DDQN) to make decisions, ability to maximize long-term gains while making quick decisions. The simulation results prove the effectiveness of DDQN in maximizing revenue when allocation resources on the edge.