Research Terms
The University of Central Florida invention is a novel Speculative Software-Defined Networking (SDN) framework that incorporates reinforcement learning (RL) to predict the arrival of flows that may not have been seen before. The invention shows that the RL agents can learn and speculatively install unseen flow rules to avoid additional control latency due to the reactive installation of flow rules. The UCF design helps respond to application dynamics. It makes reactive SDN a strong candidate for responding to the needs of emerging low-latency applications, such as augmented and virtual reality.
Partnering Opportunity
UCF is seeking licensing partners for further development and commercialization of this technology.
RL-Based Speculative Installation of Unseen Flows in SDNs for Low-Latency Applications, Interactive Session 5: Network Optimization II, IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN), Stockholm, Sweden, May 5-8, 2024.
