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