Efficient Task Offloading Algorithm for Digital Twin in Edge/Cloud Computing Environment

TL;DR

This paper introduces a novel task offloading algorithm for Digital Twin systems in heterogeneous edge and cloud environments, leveraging distributed deep learning to optimize latency and energy efficiency.

Contribution

It proposes a new Digital Twin system model with multiple data resources and a distributed deep learning-based offloading scheme for improved performance.

Findings

Reduces system latency and energy consumption

Outperforms baseline algorithms in dynamic environments

Effective in heterogeneous MEC/MCC settings

Abstract

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.