Graph neural network for in-network placement of real-time metaverse tasks in next-generation network

2024-03-04

arXiv.org

AI Summary

To address the millisecond-level latency challenge in real-time metaverse rendering, this paper proposes an in-network computing task placement and offloading method based on Software-Defined Networking (SDN). The core contribution is the first application of Graph Neural Networks (GNNs) to dynamic scheduling of metaverse tasks, jointly modeling heterogeneous network topology and time-varying resource states to enable adaptive deployment of rendering tasks across in-network nodes and edge servers. Compared to baseline models—Multilayer Perceptron (72% accuracy) and Decision Tree (70% accuracy)—the proposed GNN achieves 97% task placement accuracy. Moreover, end-to-end rendering latency remains consistently within the millisecond range, significantly enhancing interactivity and fluidity in virtual environments. This approach bridges the gap between network-aware orchestration and compute-intensive metaverse workloads, enabling scalable, low-latency immersive experiences.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Hardware-aware ML
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• Graph Neural Networks
• Metaverse Tasks
• Real-time Processing

Innovation

• Graph Neural Networks
• Software Defined Networking
• Real-time Task Allocation