Toward A Task Offloading Framework Based on Cyber Digital Twins in Mobile Edge Computing

Bin Tan

doi:10.1109/MWC.020.2200533

Toward A Task Offloading Framework Based on Cyber Digital Twins in Mobile Edge Computing

2023-06-01

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DOI：10.1109/MWC.020.2200533

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Abstract

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In the metaverse, the concept of the digital twin has been expanded from modeling industrial manufacturing to the counterpart of physical objects in cyberspace. The cyber digital twin is updated using real-time data and reasoning to improve decision-making, which imposes a high computational demand on the mobile edge. Mobile edge computing (MEC) provides computing resources for mobile devices to handle complex tasks, addressing the shortcomings of mobile devices in performance. Cyber digital twins with artificial intelligence (AI) capability have great advantages in addressing complex and changing environments. In this article, we propose a cyber digital twin-based mobile edge computing framework, which integrates artificial intelligence into mobile edge networks to enable intelligent resource management. We address the edge computation offloading task through formulating an optimization problem that minimizes the latency of a mobile user via MEC server selection and power allocation. Our solution employs a reinforcement learning-based algorithm, which we demonstrate to be effective. The experimental results show that the cyber digital twin based framework with artificial intelligence capability can further reduce task processing latency and improve the quality of service provided to users.

Keywords:

Performance evaluation

Multi-access edge computing

Quality of service

Mobile handsets

Real-time systems

Digital twins

Resource management

Toward A Task Offloading Framework Based on Cyber Digital Twins in Mobile Edge Computing

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