In the era of Internet of Things (IoT), Digital Twin (DT) is envisioned to empower various areas as a bridge between physical objects and the digital world. Through virtualization and simulation techniques, multiple functions can be achieved by leveraging computing resources. In this process, Mobile Cloud Computing (MCC) and Mobile Edge Computing (MEC) have become two of the key factors to achieve real-time feedback. However, current works only considered edge servers or cloud servers in the DT system models. Besides, The models ignore the DT with not only one data resource. In this paper, we propose a new DT system model considering a heterogeneous MEC/MCC environment. Each DT in the model is maintained in one of the servers via multiple data collection devices. The offloading decision-making problem is also considered and a new offloading scheme is proposed based on Distributed Deep Learning (DDL). Simulation results demonstrate that our proposed algorithm can effectively and efficiently decrease the system's average latency and energy consumption. Significant improvement is achieved compared with the baselines under the dynamic environment of DTs.

翻译：在物联网时代，数字孪生（DT）被设想为连接物理对象与数字世界的桥梁，赋能多个领域。通过虚拟化和仿真技术，利用计算资源可以实现多种功能。在此过程中，移动云计算（MCC）和移动边缘计算（MEC）已成为实现实时反馈的两个关键因素。然而，现有工作仅考虑了DT系统模型中的边缘服务器或云服务器。此外，这些模型忽略了DT可能拥有多个数据源的情况。在本文中，我们提出了一种新的DT系统模型，该模型考虑了异构的MEC/MCC环境。模型中的每个DT通过多个数据采集设备在其中一个服务器上维护。本文还考虑了卸载决策问题，并提出了一种基于分布式深度学习（DDL）的卸载方案。仿真结果表明，我们提出的算法能够有效且高效地降低系统的平均延迟和能耗。在DT的动态环境下，与基线相比取得了显著的改进。