The proliferation of Internet-connected devices and applications, which are heavily reliant on high-speed, robust networks, have made the management of networks difficult and complicated. While the incorporation of programmable switches and machine-learning tools in data-centers and wide-area networks has contributed to improvements in Quality of Experience (QoE), access networks, which heavily rely on human operators for most network management tasks, remain the final frontier for providing a high quality of experience to end-users. The goal of this project is to design, implement, and evaluate RELIEF, a REinforcement-LearnIng (RL)-based sElF-driving wireless network-management system that can dynamically update network configurations to detect, diagnose, and resolve network events that contribute to QoE degradation.
This project will develop new data-collection tools to capture fine-grained network data, new metrics to quantify QoE for the network, a scalable and explainable RL-based learning model to dynamically configure the underlying network and data-processing pipelines for QoE optimization. This project pursues fundamental contributions to the reinforcement learning area by developing novel solutions to inherent statistical and computational challenges. Finally, this project will design new programming abstractions and data structures to build an end-to-end system that optimizes the use of limited network resources to execute the trained RL models at scale.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
