Edge Computing Empowered Digital Twin: An End-to-End Computing Task Scheduling Approach
Ruirui Zhong, Yixiong Feng, Xiuju Song, Bingtao Hu, Yong Wang, Puyan Li, Jianrong Tan · 2024
The emerging of Digital Twin (DT) technology facilitates the further development of industrial automation. However, real-time and accurate DTs modeling and updating require massive communication and computing resources, which poses a challenge to limited resources. Edge computing as a distributed computing architecture offers the possibility of high-efficient resource scheduling in DTs. Motivated by this gap, this paper aim to solve the problem of real-time and high fidelity DTs modeling and updating. First, we represent the computing tasks of DTs in the form of Heterogeneous Computing Task Graph (HCTG). Then, a Hierarchical Attention Mechanism (HAT) is proposed to obtain the latent representation vectors of the HCTG. Finally, we design Markov Decision Process (MDP), and propose Deep Reinforcement Learning (DRL)-based computing task scheduling approach (HAT-DRL) to satisfy the minimum total completion time requirement of different DTs. Experimental results demonstrate that the proposed algorithm has promising scheduling performance and outperforms other task scheduling algorithms.