Gergely Dobreff

According to our database, Gergely Dobreff authored at least 9 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Empowering ISPs with Cloud Gaming User Experience Modeling: A NVIDIA GeForce NOW Use-Case.

Gergely Dobreff, Dániel Frey, Attila Báder, Alija Pasic

Proceedings of the 27th Conference on Innovation in Clouds, Internet and Networks, 2024

2023

Data Collection Framework for End-to-End Radio and Transport Network Quality Monitoring.

Gergely Dobreff, Márk Szalay, Bence Ladóczki, Marton Molnar, László Varga, Attila Báder, Alija Pasic

Proceedings of the 15th International Conference on Quality of Multimedia Experience, 2023

Predicting QoE for Delay-critical services in Mobile Networks: A video conferencing case study.

Gergely Dobreff, Márk Szalay, Marton Molnar, László Varga, Bence Ladóczki, Attila Báder, Alija Pasic

Proceedings of the 30th International Conference on Systems, Signals and Image Processing, 2023

2022

Optimizing and dimensioning a data intensive cloud application for soccer player tracking.

Gergely Dobreff, Marton Molnar, László Toka

Int. J. Comput. Sci. Sport, 2022

2021

Machine Learning-Based Scaling Management for Kubernetes Edge Clusters.

László Toka, Gergely Dobreff, Balázs Fodor, Balázs Sonkoly

IEEE Trans. Netw. Serv. Manag., 2021

Predicting cloud-native application failures based on monitoring data of cloud infrastructure.

László Toka, Gergely Dobreff, Dávid Haja, Mark Szalay

Proceedings of the 17th IFIP/IEEE International Symposium on Integrated Network Management, 2021

2020

Physical Performance Optimization in Football.

Gergely Dobreff, Péter Revisnyei, Gábor Schuth, György Szigeti, László Toka, Alija Pasic

Proceedings of the Machine Learning and Data Mining for Sports Analytics, 2020

Low-Cost Optical Tracking of Soccer Players.

Gabor Csanalosi, Gergely Dobreff, Alija Pasic, Marton Molnar, László Toka

Proceedings of the Machine Learning and Data Mining for Sports Analytics, 2020

Adaptive AI-based auto-scaling for Kubernetes.

László Toka, Gergely Dobreff, Balázs Fodor, Balázs Sonkoly

Proceedings of the 20th IEEE/ACM International Symposium on Cluster, 2020