Deep Reinforcement Learning-Based Joint Task Offloading in Cloud-Edge-End Cooperation Environments

To meet the service requirements for delay-sensitive tasks and improve content delivery, in this paper, a deep reinforcement learning (DRL)-based joint offloading scheme of computing and content tasks in cloud-edge-end cooperation networks is proposed. We use a minimum delay model to describe the task offloading problem, where the same requests can be aggregated to lighten the server load and the in-network caching is considered. We design a new DRL algorithm to achieve intelligent task offloading by making cooperative caching and routing decisions. The simulation results show that the proposed model has obvious advantage significantly better than the existing models in the cloudedge-end cooperation environments.