AI methods for reliable big data transport
AI-RMDT addresses the transport of large data volumes over IP networks. It builds on RMDT, the data transfer protocol developed in the FILA laboratory. The research objective is to extend this protocol with artificial intelligence methods and improve the control of data flows at high transmission rates. Efficiency, reliability and stability across different network environments are central concerns.
The approach combines machine learning, multi-parameter optimization and synchronization algorithms. It considers both transmission control and secure transmission models for IP-based networks, rather than treating data rate as the only research question.
These improvements are development objectives. The central question is how the individual methods can be combined into a more efficient and reliable transport solution.
Research topics
- Analysis and further development of RMDT for large-volume data transfer.
- AI-based optimization of data flow and stability, alongside new congestion control approaches.
- Theoretical and experimental investigation of application-layer multicast and file synchronization.
- Analysis of secure transmission models and the role of multi-parameter optimization.
Acknowledgments
This project is funded by the European Regional Development Fund (ERDF) and the state of Saxony-Anhalt as part of the Saxony-Anhalt SCIENCE - Research and Innovation (ERDF) 2021-2027 program (funding reference: ZS/2023/12/182323).
