Exploració per autor "Chen, Xiaoliang"
Ara es mostren els items 1-5 de 5
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Cooperative learning for disaggregated delay modeling in multidomain networks
(2021-05-05)
Article
Accés obertAccurate delay estimation is one of the enablers of future network connectivity services, as it facilitates the application layer to anticipate network performance. If such connectivity services require isolation (slicing), ... -
Experimental demonstration of cognitive provisioning and alien wavelength monitoring in multi-domain EON
(Institute of Electrical and Electronics Engineers (IEEE), 2018)
Text en actes de congrés
Accés obertThis paper proposes a cognitive multi-domain EON architecture with machine-learning aided RMSA and alien wavelength monitoring. Testbed experiments show modulation format recognition, QoT monitoring and cognitive routing ... -
Experimental demonstration of heterogeneous cross stratum broker for scientific applications
(Institute of Electrical and Electronics Engineers (IEEE), 2016)
Text en actes de congrés
Accés restringit per política de l'editorialWe propose and demonstrate cross-stratum Broker orchestration for scientific applications and heterogeneous resources reservation in DCs, HPC facilities and networks belonging to different operators. Experiments were ... -
Experimental demonstration of machine-learning-aided QoT estimation in multi-domain elastic optical networks with alien wavelengths
(Institute of Electrical and Electronics Engineers (IEEE), 2019)
Article
Accés obertIn multi-domain elastic optical networks with alien wavelengths, each domain needs to consider intradomain and interdomain alien traffic to estimate and guarantee the required quality of transmission (QoT) for each lightpath ... -
On real-time optical subcarrier management in P2MP networks with mixed-strategy gaming
(Institute of Electrical and Electronics Engineers (IEEE), 2023)
Text en actes de congrés
Accés obertWe developed a mixed-strategy gaming approach for distributed and real-time optical subscarrier management in point-to-multipoint networks, achieving traffic loss rates close to those by ILP-based centralized optimization.
