ResLearn: Transformer-based Residual Learning for Metaverse Network Traffic Prediction

TL;DR

This paper introduces ResLearn, a Transformer-based residual learning model, along with a new dataset and privacy-preserving view-frame algorithm, to improve Metaverse network traffic prediction accuracy for better resource management.

Contribution

It presents a novel Transformer-based residual learning approach and a view-frame algorithm, along with a real-world dataset, advancing traffic prediction in Metaverse networks.

Findings

ResLearn reduces prediction errors by 99% during peak traffic.

The new dataset captures VR, AR, and MR traffic for research.

The view-frame algorithm ensures privacy while identifying video frames.

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.