ResLearn: Transformer-based Residual Learning for Metaverse Network Traffic Prediction
Abstract
Our work proposes a comprehensive solution for predicting Metaverse network traffic, addressing the growing demand for intelligent resource management in eXtended Reality (XR) services. We first introduce a state-of-the-art testbed capturing a real-world dataset of virtual reality (VR), augmented reality (AR), and mixed reality (MR) traffic, made openly available for further research. To enhance prediction accuracy, we then propose a novel view-frame (VF) algorithm that accurately identifies video frames from traffic while ensuring privacy compliance, and we develop a Transformer-based progressive error-learning algorithm, referred to as ResLearn for Metaverse traffic prediction. ResLearn significantly improves time-series predictions by using fully connected neural networks to reduce errors, particularly during peak traffic, outperforming prior work by 99%. Our contributions offer Internet service providers (ISPs) robust tools for real-time network management to satisfy Quality of Service (QoS) and enhance user experience in the Metaverse.
Cite
@article{arxiv.2411.11894,
title = {ResLearn: Transformer-based Residual Learning for Metaverse Network Traffic Prediction},
author = {Yoga Suhas Kuruba Manjunath and Mathew Szymanowski and Austin Wissborn and Mushu Li and Lian Zhao and Xiao-Ping Zhang},
journal= {arXiv preprint arXiv:2411.11894},
year = {2024}
}