Summary of Chestnut: a Qos Dataset For Mobile Edge Environments, by Guobing Zou et al.
CHESTNUT: A QoS Dataset for Mobile Edge Environments
by Guobing Zou, Fei Zhao, Shengxiang Hu
First submitted to arxiv on: 25 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposes a novel Quality of Service (QoS) dataset that incorporates dynamic attributes such as time and geographic location. Existing datasets like WS-Dream primarily focus on static QoS metrics, neglecting these crucial factors that impact QoS performance. The proposed dataset aims to fill this gap by accurately recording temporal and geographic information during the collection process. This innovation is essential for improving QoS prediction in mobile edge environments, where QoS performance fluctuates over time and location. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes a new dataset for measuring how well network services work. It’s important because it helps us understand why some services are better than others when you’re on the move. Right now, most datasets only show static information like bandwidth and latency, but they don’t tell us about time or where you are. This is a problem because QoS changes depending on when and where you ask for a service. To fix this, the authors created a new dataset that shows how well services work at different times and locations. |
