首页|Digital twin driven and intelligence enabled content delivery in end-edge-cloud collaborative 5G networks

Digital twin driven and intelligence enabled content delivery in end-edge-cloud collaborative 5G networks

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The rapid development of 5G/6G and AI enables an environment of Internet of Everything(IoE)which can support millions of connected mobile devices and applications to operate smoothly at high speed and low delay.However,these massive devices will lead to explosive traffic growth,which in turn cause great burden for the data transmission and content delivery.This challenge can be eased by sinking some critical content from cloud to edge.In this case,how to determine the critical content,where to sink and how to access the content correctly and efficiently become new challenges.This work focuses on establishing a highly efficient content delivery frame-work in the IoE environment.In particular,the IoE environment is re-constructed as an end-edge-cloud collab-orative system,in which the concept of digital twin is applied to promote the collaboration.Based on the digital asset obtained by digital twin from end users,a content popularity prediction scheme is firstly proposed to decide the critical content by using the Temporal Pattern Attention(TPA)enabled Long Short-Term Memory(LSTM)model.Then,the prediction results are input for the proposed caching scheme to decide where to sink the critical content by using the Reinforce Learning(RL)technology.Finally,a collaborative routing scheme is proposed to determine the way to access the content with the objective of minimizing overhead.The experimental results indicate that the proposed schemes outperform the state-of-the-art benchmarks in terms of the caching hit rate,the average throughput,the successful content delivery rate and the average routing overhead.

Digital twinIoEContent deliveryCachingRouting

Bo Yi、Jianhui Lv、Xingwei Wang、Lianbo Ma、Min Huang

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College of Computer Science and Engineering Northeastern University,Shenyang 110169,China

Pengcheng Lab.,Shenzhen,518055,China

College of Software,Northeastern University,Shenyang 110819,China

College of Information Science and Engineering,Northeastern University,Shenyang,110819,China

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National Key Research and Development Program of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of China

2019YFB180280062002055620320136187207362202247

2024

10.1016/j.dcan.2022.09.014

数字通信与网络(英文)

数字通信与网络(英文)

ISSN：
年,卷(期)：2024.10(2)
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