Human-Centric Service Offloading with CNN Partitioning in Cloud-Edge Computing-Empowered Metaverse Networks

Tang, Sizhe and Xia, Xiaoyu and Bilal, Muhammad and Dou, Wanchun and Xu, Xiaolong (2025) Human-Centric Service Offloading with CNN Partitioning in Cloud-Edge Computing-Empowered Metaverse Networks. IEEE Transactions on Consumer Electronics. ISSN 0098-3063

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Abstract

Metaverse is an emerging paradigm of modern consumer entertainment that aims to create a fully immersive, hyper-spatial, and extremely interoperable virtual space for smart applications, emphasizing a human-centric approach. Benefiting from edge computing and 5G networks, there has been a surge in the development of massive intelligent services (e.g., real-time motion sensing games) based on Convolutional Neural Networks (CNNs) deployed at the edge, which require plenty of computational and communication resources. However, the huge volume of service requests can overload edge servers, thus decreasing the quality of service (QoS). Given the limited resources available at the edge, service offloading at the cloud can be employed to ensure the QoS. In this paper, we design MP-DOB, a load balance-aware offloading strategy with CNN model partitioning, which jointly optimizes the inference delay and energy consumption in the service process. Specifically, we partition a CNN model into sub-models in sequential and spatial manners. Then, part of these sub-models is considered to be offloaded at the cloud according to the game theory-based policy. Parallelly, the other part of sub-models is re-scheduled within the edge to further balance the load of edge servers. Comparative experiments are conducted to indicate the effectiveness of our proposed MP-DOB.