构建首个含时空信息的移动边缘服务质量数据集
CHESTNUT: A QoS Dataset for Mobile Edge Environments
- 采集时同步记录请求时间与设备位置，捕捉动态变化
- 包含带宽、延迟、抖动、丢包率等多维指标，支持精准预测
- 适合研究移动边缘网络性能建模与优化的学者使用
服务质量（QoS）是衡量网络服务性能的关键指标，广泛应用于移动边缘环境，用于评估移动设备向边缘服务器请求服务时的服务质量。QoS通常包含带宽、延迟、抖动和数据包丢失率等多个维度。然而，现有大多数QoS数据集（如常见的WS-Dream数据集）主要关注静态的网络服务指标，忽略了时间与地理位置等动态属性。这些数据未记录服务请求时的设备位置或请求的时间顺序，而这些动态属性对理解和服务质量的真实表现预测至关重要，因为QoS性能通常随时间和地理位置波动。为此，我们提出一个新型数据集，在采集过程中精确记录服务质量的时间和地理定位信息，旨在为未来移动边缘环境中的QoS预测提供更准确、可靠的支撑数据。
原文摘要 · Abstract (English)
Quality of Service (QoS) is an important metric to measure the performance of network services. Nowadays, it is widely used in mobile edge environments to evaluate the quality of service when mobile devices request services from edge servers. QoS usually involves multiple dimensions, such as bandwidth, latency, jitter, and data packet loss rate. However, most existing QoS datasets, such as the common WS-Dream dataset, focus mainly on static QoS metrics of network services and ignore dynamic attributes such as time and geographic location. This means they should have detailed the mobile device's location at the time of the service request or the chronological order in which the request was made. However, these dynamic attributes are crucial for understanding and predicting the actual performance of network services, as QoS performance typically fluctuates with time and geographic location. To this end, we propose a novel dataset that accurately records temporal and geographic location information on quality of service during the collection process, aiming to provide more accurate and reliable data to support future QoS prediction in mobile edge environments.
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