Human-Centric Resource Allocation for the Metaverse With Multiaccess Edge Computing

Multiaccess edge computing (MEC) is a promising solution to the computation-intensive, low-latency rendering tasks of the metaverse. However, how to optimally allocate limited communication and computation resources at the edge to a large number of users in the metaverse is quite challenging. In thi...

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Bibliographic Details
Published in:IEEE internet of things journal 2023-11, Vol.10 (22), p.19993-20005
Main Authors: Long, Zijian, Dong, Haiwei, Saddik, Abdulmotaleb El
Format: Article
Language:English
Subjects:
Artificial neural networks
Edge computing
Environment models
Mobile computing
Multiagent systems
Resource allocation
Resource utilization
User experience
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Summary:Multiaccess edge computing (MEC) is a promising solution to the computation-intensive, low-latency rendering tasks of the metaverse. However, how to optimally allocate limited communication and computation resources at the edge to a large number of users in the metaverse is quite challenging. In this article, we propose an adaptive edge resource allocation method based on multiagent soft actor–critic with graph convolutional networks (SAC-GCN). Specifically, SAC-GCN models the multiuser metaverse environment as a graph where each agent is denoted by a node. Each agent learns the interplay between agents by graph convolutional networks with a self-attention mechanism to further determine the resource usage for one user in the metaverse. The effectiveness of SAC-GCN is demonstrated through the analysis of user experience, balance of resource allocation, and resource utilization rate by taking a virtual city park metaverse as an example. Experimental results indicate that SAC-GCN outperforms other resource allocation methods in improving overall user experience, balancing resource allocation, and increasing resource utilization rate by at least 27%, 11%, and 8%, respectively.
ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2023.3283335