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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2411.11894 |
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| _version_ | 1866910704161783808 |
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| author | Manjunath, Yoga Suhas Kuruba Szymanowski, Mathew Wissborn, Austin Li, Mushu Zhao, Lian Zhang, Xiao-Ping |
| author_facet | Manjunath, Yoga Suhas Kuruba Szymanowski, Mathew Wissborn, Austin Li, Mushu Zhao, Lian Zhang, Xiao-Ping |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_11894 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ResLearn: Transformer-based Residual Learning for Metaverse Network Traffic Prediction Manjunath, Yoga Suhas Kuruba Szymanowski, Mathew Wissborn, Austin Li, Mushu Zhao, Lian Zhang, Xiao-Ping Artificial Intelligence Signal Processing 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. |
| title | ResLearn: Transformer-based Residual Learning for Metaverse Network Traffic Prediction |
| topic | Artificial Intelligence Signal Processing |
| url | https://arxiv.org/abs/2411.11894 |