Efficient Task Offloading Algorithm for Digital Twin in Edge/Cloud Computing Environment
This work addresses real-time feedback challenges in IoT and Digital Twin applications, though it appears incremental by extending existing models to include multiple data sources and heterogeneous servers.
The paper tackles the problem of task offloading in Digital Twin systems by proposing a new algorithm based on Distributed Deep Learning for a heterogeneous edge/cloud environment, which reduces average latency and energy consumption compared to baselines.
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.