Cloud Gaming Study
Several datasets are hosted on this website. They all have been used in scientific publications.Firstable, there are network captures (PCAP files), made under normal and disturbed network conditions. Then, we find CG captures (PCAP files) made via an emulated cellular network. The cellular traces used to generate these captures are also available. Finally, we find representative QoS and QoE data for CG.
Normal conditions:As for the normal network conditions, we find network captures of CG traffic, as well as other application types, running under UDP.
- Online Gaming
- Remote Desktop
- Visio Conference
- Video Streaming
- Live Video
- Facebook navigation
Disturbed conditions:Here, we only have captures of CG traffic. We differentiate between two scenarios;
- "Permanent": constraints start before the game session
- "Temporary": 2' normal conditions ; 2' disturbed conditions ; 2' normal conditions.
Cellular conditions:Here, we also only have captures of CG traffic. There are 2 types of captures:
- "In": means the captures are done before the MahiMahi Linkshell queue
- "Out": means the captures are done after the MahiMahi Linkshell queue
QoS and QoE:Here, we capture data related to QoS/QoE. These captures were made via WebRTC, under 6 different emulated 4G network conditions. There are 10 .csv files of 14 QoS/QoE features collected while playing “Dirt5”, a racing game on the Google Stadia cloud gaming platform.
- An Analysis of Cloud Gaming Platforms Behavior under Different Network Constraints .
- Efficient Identification of Cloud Gaming Traffic at the Edge.
- Assessing Unsupervised Machine Learning solutions for Anomaly Detection in Cloud Gaming Sessions.
This dataset is part of a project that has received funding from the French organization ANR. No ANR-19-CE25-0012.
For more information, you can contact the authors of this work: