Summary of Reslearn: Transformer-based Residual Learning For Metaverse Network Traffic Prediction, by Yoga Suhas Kuruba Manjunath et al.
ResLearn: Transformer-based Residual Learning for Metaverse Network Traffic Prediction
by Yoga Suhas Kuruba Manjunath, Mathew Szymanowski, Austin Wissborn, Mushu Li, Lian Zhao, Xiao-Ping Zhang
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a comprehensive solution for predicting Metaverse network traffic, which is crucial for intelligent resource management in eXtended Reality (XR) services. The authors introduce a state-of-the-art testbed capturing real-world datasets of virtual reality (VR), augmented reality (AR), and mixed reality (MR) traffic, making it openly available for further research. To enhance prediction accuracy, the paper proposes a novel view-frame (VF) algorithm that identifies video frames from traffic while ensuring privacy compliance, and develops a Transformer-based progressive error-learning algorithm called ResLearn for Metaverse traffic prediction. ResLearn significantly improves time-series predictions by reducing errors during peak traffic, outperforming prior work by 99%. The authors’ contributions offer Internet service providers (ISPs) robust tools for real-time network management to ensure Quality of Service (QoS) and enhance user experience in the Metaverse. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps predict how much data is used when people are online in virtual worlds. It’s important because it can help make sure that these experiences work well and don’t slow down or stop working. The authors created a special tool to collect real-world data about how people use different types of virtual reality, like games or educational programs. They also developed an algorithm that can predict how much data will be used in the future. This algorithm is better at making predictions than previous methods, which means it can help make sure that people have a good experience when they’re online in virtual worlds. |
Keywords
» Artificial intelligence » Time series » Transformer
