DAPHNE: Deep learning and Artificial Intelligence for High-Performance Networks
With advances in computing, data is being produced at exponential rates requiring highly flexible mobility across HPC computing and distributed facilities. Networks are the essential bloodline to science collaborations across the globe such as in high-energy physics, earth sciences, and genomics. However, upgrading network hardware with high-end routers and optic fibers to cope with this data revolution can cost millions of dollars. In this project, we are exploring artificial intelligence to design and efficiently manage distributed network architectures to improve data transfers, guarantee high-throughput and improve traffic engineering.
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