Deep Reinforcement Learning-Based Cloud-Edge Collaborative Mobile Computation Offloading in Industrial Networks

作者：Siguang Chen, Jiamin Chen, Yifeng Miao, Qian Wang, Chuanxin Zhao · 发表于：IEEE Transactions on Signal and Information Processing over Networks · 年份：2022 · DOI：10.1109/tsipn.2022.3171336 · 被引用次数：63 · 研究领域：IoT and Edge/Fog Computing、Blockchain Technology Applications and Security、Digital Transformation in Industry

With the rapid development of mobile industrial applications and due to the limited coverage of static edge servers, traditional edge computing technology has great limitations in dynamic environmental applications. This paper proposes a deep reinforcement learning-based cloud-edge collaborative mobile computation offloading mechanism for satisfying the dynamic service requirements in industrial networks. Specifically, a three-layer network model of digital twins and a decentralized network of task resources are first constructed to handle the mobility of user terminals and the relevance of tasks. Then, based on the comprehensive consideration of mobility, associated tasks, computing resources and offloading decisions, an optimization problem is formulated to minimize the weighted sum of the execution delay and energy consumption of all tasks for all users. Additionally, a deep reinforcement learning-based cloud-edge collaborative mobile computation offloading (DRL-CCMCO) algorithm is proposed to solve this optimization problem. Based on the differences in each edge cloud, this algorithm sets the priority of the shared experience pool and selects the most effective experience samples to complete better learning and training. It also utilizes a distributed learning method to learn the probability of an approximate reward distribution and optimizes network parameters through cloud-edge collaboration to achieve faster optimal offloading decision. Finally, a large number of simul...