Deep data plane programmability is exploited at different future 6G network technological segments to realize end-to-end application delay telemetry. For the first time, data analytics obtained by RAN controllers and metro network collectors are processed by a Multi Agent System running AI algorithms with the aim of detecting latency anomalies and their location in the network, suggesting the most appropriate recovery countermeasure. The demo is shown applied to Augmented Reality application with extreme low latency requirements.

Augmented Reality App with AI-based Pervasive Latency Monitoring of RAN and Programmable Metro Packet-Optical Networks

Alhamed, F.;Guaitolini, M.;Ismail, L.;Olmos Vegas, J. J.;Sgambelluri, A.;Paolucci, F.
2024-01-01

Abstract

Deep data plane programmability is exploited at different future 6G network technological segments to realize end-to-end application delay telemetry. For the first time, data analytics obtained by RAN controllers and metro network collectors are processed by a Multi Agent System running AI algorithms with the aim of detecting latency anomalies and their location in the network, suggesting the most appropriate recovery countermeasure. The demo is shown applied to Augmented Reality application with extreme low latency requirements.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11382/572093
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