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dc.contributor.authorKanwal Janjua, Hafiza
dc.contributor.authorMiguel Jiménez, Ignacio de 
dc.contributor.authorDurán Barroso, Ramón José 
dc.contributor.authorMasoumi, Maryam
dc.contributor.authorHosseini, Soheil
dc.contributor.authorAguado Manzano, Juan Carlos 
dc.contributor.authorMerayo Álvarez, Noemí 
dc.contributor.authorAbril Domingo, Evaristo José 
dc.date.accessioned2023-10-26T11:07:30Z
dc.date.available2023-10-26T11:07:30Z
dc.date.issued2023
dc.identifier.citationIn: Mehmood, R., et al. Distributed Computing and Artificial Intelligence, Special Sessions I, 20th International Conference. DCAI 2023. Lecture Notes in Networks and Systems vol. 741, pp. 399-407, Springer, Chames
dc.identifier.citationIn: Mehmood, R., et al. Distributed Computing and Artificial Intelligence, Special Sessions I, 20th International Conference. DCAI 2023. Lecture Notes in Networks and Systems, vol. 741, pp. 399-407, Springer, Cham
dc.identifier.urihttps://uvadoc.uva.es/handle/10324/62380
dc.descriptionProducción Científicaes
dc.description.abstractSDN (Software Define Networking) and NFV (Network Function Virtualization) are the key enablers for 5G systems and also open many doors in the cloud-native application. Besides, it invites new challenges to the efficiency and scalability of resource management. This work aims to provide a cognitive framework for 5G resource and service orchestration in a cloud-native SDN environment. The proposed NG2CRO framework resource orchestrator is designed to adapt the network’s self-learning capabilities and dynamicity while taken on to account the network’s Markovian properties and diverse service requirements. We consider incorporating AI (Artificial Intelligence) techniques specifically RL (Reinforcement Learning) methodologies because literature has shown that these techniques can efficiently address and comply with the current dynamic behaviors and heterogeneity of 5G services and applications. In conclusion, both benefits and liabilities are discussed of incorporating AI specifically RL into resource orchestration practices that provide us with future research challenges.es
dc.format.mimetypeapplication/pdfes
dc.language.isoenges
dc.publisherSpringer
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subject.classificationNext Generation 5G Network
dc.subject.classificationArtificial Intelligence
dc.subject.classificationNetwork Automation
dc.subject.classificationSoftware-Define Networking
dc.subject.classificationCloud-Native SDN
dc.subject.classificationResource Orchestration
dc.titleA Framework for Next Generation Cloud-Native SDN Cognitive Resource Orchestrator for IoTs (NG2CRO)es
dc.typeinfo:eu-repo/semantics/bookPartes
dc.identifier.doi10.1007/978-3-031-38318-2_39
dc.relation.publisherversionhttps://doi.org/10.1007/978-3-031-38318-2_39
dc.description.projectEU H2020 MSCA ITN-ETN IoTalentum (grant no. 953442)
dc.description.projectConsejería de Educación de la Junta de Castilla y León y FEDER (VA231P20)
dc.description.projectMinisterio de Ciencia e Innovación y Agencia Estatal de Investigación (Proyecto PID2020-112675RB-C42 financiado por MCIN/AEI/10.13039/501100011033)
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersiones


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