Deep Reinforcement Learning Cloud-Edge-Terminal Computation Resource Allocation Mechanism for IoT

With the development of Internet of Things (IoT), the types and the volume of IoT services have been increasing rapidly. Mobile edge computing (MEC) and cloud computing has recently emerged as a promising paradigm for meeting the increasing computational demands of IoT. More and more computation offloading algorithms of MEC and cloud computing have appeared. However, existing computation offloading algorithms cannot have particularly good performance in various scenarios. In this regard, we proposed a cloud-edge-terminal collaborative computation offloading algorithm based on Asynchronous Advantage Actor-Critic. It uses Asynchronous Advantage Actor-Critic to make the task choose one of the two algorithms that has better performance in their respective scenarios, and achieves the complementarity of the advantages and disadvantages of the two algorithms. Finally, the characteristics of the algorithm are investigated by simulation and compared with other algorithms to verify the algorithm’s performance.