Discern-XR: An Online Classifier for Metaverse Network Traffic
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866912110821244928 |
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| author | Manjunath, Yoga Suhas Kuruba Wissborn, Austin Szymanowski, Mathew Li, Mushu Zhao, Lian Zhang, Xiao-Ping |
| author_facet | Manjunath, Yoga Suhas Kuruba Wissborn, Austin Szymanowski, Mathew Li, Mushu Zhao, Lian Zhang, Xiao-Ping |
| contents | In this paper, we design an exclusive Metaverse network traffic classifier, named Discern-XR, to help Internet service providers (ISP) and router manufacturers enhance the quality of Metaverse services. Leveraging segmented learning, the Frame Vector Representation (FVR) algorithm and Frame Identification Algorithm (FIA) are proposed to extract critical frame-related statistics from raw network data having only four application-level features. A novel Augmentation, Aggregation, and Retention Online Training (A2R-OT) algorithm is proposed to find an accurate classification model through online training methodology. In addition, we contribute to the real-world Metaverse dataset comprising virtual reality (VR) games, VR video, VR chat, augmented reality (AR), and mixed reality (MR) traffic, providing a comprehensive benchmark. Discern-XR outperforms state-of-the-art classifiers by 7% while improving training efficiency and reducing false-negative rates. Our work advances Metaverse network traffic classification by standing as the state-of-the-art solution. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_05184 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Discern-XR: An Online Classifier for Metaverse Network Traffic Manjunath, Yoga Suhas Kuruba Wissborn, Austin Szymanowski, Mathew Li, Mushu Zhao, Lian Zhang, Xiao-Ping Artificial Intelligence Signal Processing In this paper, we design an exclusive Metaverse network traffic classifier, named Discern-XR, to help Internet service providers (ISP) and router manufacturers enhance the quality of Metaverse services. Leveraging segmented learning, the Frame Vector Representation (FVR) algorithm and Frame Identification Algorithm (FIA) are proposed to extract critical frame-related statistics from raw network data having only four application-level features. A novel Augmentation, Aggregation, and Retention Online Training (A2R-OT) algorithm is proposed to find an accurate classification model through online training methodology. In addition, we contribute to the real-world Metaverse dataset comprising virtual reality (VR) games, VR video, VR chat, augmented reality (AR), and mixed reality (MR) traffic, providing a comprehensive benchmark. Discern-XR outperforms state-of-the-art classifiers by 7% while improving training efficiency and reducing false-negative rates. Our work advances Metaverse network traffic classification by standing as the state-of-the-art solution. |
| title | Discern-XR: An Online Classifier for Metaverse Network Traffic |
| topic | Artificial Intelligence Signal Processing |
| url | https://arxiv.org/abs/2411.05184 |