AI-Driven Energy-Efficient Content Task Offloading in Cloud-Edge-End Cooperation Networks
Chao Fang, Xiangheng Meng, Zhaoming Hu, Fangmin Xu, Deze Zeng, Mianxiong Dong, Wei Ni
Abstract
To tackle a challenging energy efficiency problem caused by the growing mobile Internet traffic, this paper proposes a deep reinforcement learning (DRL)-based green content task offloading scheme in cloud-edge-end cooperation networks. Specifically, we formulate the problem as a power minimization model, where requests arriving at a node for the same content can be aggregated in its queue and in-network caching is widely deployed in heterogeneous environments. A novel DRL algorithm is designed to minimize the power consumption by making collaborative caching and task offloading decisions in each slot on the basis of content request information in previous slots and current network state. Numerical results show that our proposed content task offloading model achieves better power efficiency than the existing popular counterparts in cloud-edge-end collaboration networks, and fast converges to the stable state.