CHESTNUT: A QoS Dataset for Mobile Edge Environments

TL;DR

This paper introduces CHESTNUT, a new QoS dataset for mobile edge environments that includes dynamic attributes like time and location, addressing limitations of existing static datasets.

Contribution

The paper presents a novel dataset capturing temporal and geographic QoS data, enabling better prediction and analysis of network performance in mobile edge settings.

Findings

Dataset includes detailed temporal and geographic QoS data.

Supports improved QoS prediction models.

Addresses gaps in existing static QoS datasets.

Abstract

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.