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

Abstract

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 framework in the IoE environment. In particular, the IoE environment is re-constructed as an end-edge-cloud collaborative 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.