49 citations · 52 across the 3 of their papers we have counts for
3 papers
Atom: Neural Traffic Compression with Spatio-Temporal Graph Neural Networks
Paul Almasan, Krzysztof Rusek, Shihan Xiao +4
Storing network traffic data is key to efficient network management; however, it is becoming more challenging and costly due to the ever-increasing data transmission rates, traffic…
GraphCC: A Practical Graph Learning-based Approach to Congestion Control in Datacenters
Guillermo Bernárdez, José Suárez-Varela, Xiang Shi +4
Congestion Control (CC) plays a fundamental role in optimizing traffic in Data Center Networks (DCN). Currently, DCNs mainly implement two main CC protocols: DCTCP and DCQCN. Both…
MAGNNETO: A Graph Neural Network-based Multi-Agent system for Traffic Engineering
Guillermo Bernárdez, José Suárez-Varela, Albert López +5
Current trends in networking propose the use of Machine Learning (ML) for a wide variety of network optimization tasks. As such, many efforts have been made to produce ML-based sol…