This study addresses the challenge of real-time metaverse applications by proposing an in-network placement and task-offloading solution for delay-constrained computing tasks in next-generation networks. The metaverse, envisioned as a parallel virtual world, requires seamless real-time experiences across diverse applications. The study introduces a software-defined networking (SDN)-based architecture and employs graph neural network (GNN) techniques for intelligent and adaptive task allocation in in-network computing (INC). Considering time constraints and computing capabilities, the proposed model optimally decides whether to offload rendering tasks to INC nodes or edge server. Extensive experiments demonstrate the superior performance of the proposed GNN model, achieving 97% accuracy compared to 72% for multilayer perceptron (MLP) and 70% for decision trees (DTs). The study fills the research gap in in-network placement for real-time metaverse applications, offering insights into efficient rendering task handling.

翻译：本研究针对实时元宇宙应用的挑战，提出了一种面向下一代网络中延迟约束计算任务的内网络部署与任务卸载解决方案。元宇宙被构想为平行虚拟世界，需要在多样化应用中实现无缝的实时体验。研究引入了一种基于软件定义网络（SDN）的架构，并利用图神经网络（GNN）技术实现内网络计算（INC）中智能自适应的任务分配。在考虑时间约束与计算能力的前提下，所提出的模型能够最优决策将渲染任务卸载至INC节点或边缘服务器。大量实验表明，所提出的GNN模型性能优越，准确率达到97%，而多层感知机（MLP）为72%、决策树（DTs）为70%。本研究填补了实时元宇宙应用内网络部署领域的研究空白，为高效处理渲染任务提供了重要见解。