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

arXiv: https://arxiv.org/abs/2307.05888 · HTML: https://mlsanity.com/paper/2307.05888/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 42 references that are in the map.
- First posted on arXiv 12 Jul 2023.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Digital Twin of Wireless Systems: Overview, Taxonomy, Challenges, and Opportunities](https://mlsanity.com/paper/2202.02559/) (2022)
- [Adaptive Federated Learning and Digital Twin for Industrial Internet of Things](https://mlsanity.com/paper/2010.13058/) (2020)
- [Optimized Computation Offloading Performance in Virtual Edge Computing Systems via Deep Reinforcement Learning](https://mlsanity.com/paper/1805.06146/) (2018)
- [Fast Adaptive Task Offloading in Edge Computing based on Meta Reinforcement Learning](https://mlsanity.com/paper/2008.02033/) (2020)
- [DMRO:A Deep Meta Reinforcement Learning-based Task Offloading Framework for Edge-Cloud Computing](https://mlsanity.com/paper/2008.09930/) (2020)
- [6G Mobile-Edge Empowered Metaverse: Requirements, Technologies, Challenges and Research Directions](https://mlsanity.com/paper/2211.04854/) (2022)
- [Distributed Intelligence in Wireless Networks](https://mlsanity.com/paper/2208.00545/) (2022)
- [Re-shaping Post-COVID-19 Teaching and Learning: A Blueprint of Virtual-Physical Blended Classrooms in the Metaverse Era](https://mlsanity.com/paper/2203.09228/) (2022)

## Abstract (arXiv)

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.