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Integrating OpenIreland and COSMOS testbeds for delivering a cross-Atlantic Open Networking Solution

Through the use of data from the testbeds we develop machine learning algorithms capable of predicting physical layer network behaviou. These algorithms have the potential to provide more reliable and sustainable internet networks.

ProjectID

NGI-ATLANTIC:OC2-202

acronym

IOICT

additional Info

The purpose of our experiments is, on one side to evaluate the ability to transfer a machine learning model from simulation to a testbed environment and between two different testbeds. Here we investigate the trade-off between data training requirements and performance. More in depth, our investigation covers the effect that collecting data point measurements across different wavelength channels, data rates, modulation formats, topologies and optical devices has on the amount of data required to train the algorithms.
On the other side we show an application running in the COSMOS testbed, that can control resources in the OpenIreland testbed, reserving capacity in the optical transmission networks, and spectrum in the wireless nodes. The initial concept was developed through the FUTEBOL European project and will be brought here to a large-scale use case.

Enduser Relevance

The real challenge of our experiments is to understand how well can ML algorithms approximate real device and systems behaviour, taking into consideration issues of unpredictable variability, which are typically neglected in simulation.

contact

https://www.linkedin.com/in/marco-ruffini-9848a83/

endorsements
https://ngiatlantic.eu/funded-experiments/integrating-openireland-and-cosmos-testbeds-delivering-cross-atlantic-open
disclaimer

This experiment has not completed.

Country:  IE IT US

Keywords: COSMOSEPIOpenIrelandWireless

Status: Early research demo

Category: Network infrastructure (including routing, peer-to-peer and virtual private networking)

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