ML-driven provisioning and management of vertical services in automated cellular networks

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dc.contributor.author Casetti, Claudio
dc.contributor.author Chiasserini, Carla Fabiana
dc.contributor.author Marcato, Silvio
dc.contributor.author Puligheddu, Corrado
dc.contributor.author Mangues-Bafalluy, Josep
dc.contributor.author Baranda, Jorge
dc.contributor.author Brenes, Juan
dc.contributor.author Bocchi, Francesco
dc.contributor.author Landi, Giada
dc.contributor.author Bakhshi, Bahador
dc.date.accessioned 2022-03-03T13:50:40Z
dc.date.available 2022-03-03T13:50:40Z
dc.date.issued 2022-02-23
dc.identifier.bibliographicCitation IEEE Transactions on Network and Service Management, 19(3), Sept. 2022, Pp. 2017-2033
dc.identifier.issn 1932-4537
dc.identifier.uri http://hdl.handle.net/10016/34286
dc.description.abstract One of the main tasks of new-generation cellular networks is the support of the wide range of virtual services that may be requested by vertical industries, while fulfilling their diverse performance requirements. Such task is made even more challenging by the time-varying service and traffic demands, and the need for a fully-automated network orchestration and management to reduce the service operational costs incurred by the network provider. In this paper, we address these issues by proposing a softwarized 5G network architecture that realizes the concept of ML-as-a-Service (MLaaS) in a flexible and efficient manner. The designed MLaaS platform can provide the different entities of a MANO architecture with already-trained ML models, ready to be used for decision making. In particular, we show how our MLaaS platform enables the development of two ML-driven algorithms for, respectively, network slice subnet sharing and run-time service scaling. The proposed approach and solutions are implemented and validated through an experimental testbed in the case of three different services in the automotive domain, while their performance is assessed through simulation in a large-scale, real-world scenario. In-testbed validation shows that the use of the MLaaS platform within the designed architecture and the ML-driven decision-making processes entail a very limited time overhead, while simulation results highlight remarkable savings in operational costs, e.g., up to 40% reduction in CPU consumption and up to 30% reduction in the OPEX.
dc.description.sponsorship This work was supported by the EU Commission through the 5GROWTH project (Grant Agreement No. 856709), Spanish MINECO 5G-REFINE project (TEC2017-88373-R), and Generalitat de Catalunya 2017 SGR 1195.
dc.format.extent 17
dc.language.iso eng
dc.publisher IEEE
dc.rights © 2022 IEEE.
dc.subject.other 5G platform
dc.subject.other Vertical services
dc.subject.other SLA management
dc.subject.other ML-driven network management
dc.subject.other Service orchestration
dc.title ML-driven provisioning and management of vertical services in automated cellular networks
dc.type article
dc.description.status Publicado
dc.subject.eciencia Telecomunicaciones
dc.identifier.doi https://doi.org/10.1109/TNSM.2022.3153087
dc.rights.accessRights openAccess
dc.relation.projectID info:eu-repo/grantAgreement/EC/856709/5GROWTH
dc.relation.projectID Gobierno de España. TEC2017-88373-R/5G-REFINE
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 1
dc.identifier.publicationfirstpage 2017
dc.identifier.publicationissue 3
dc.identifier.publicationlastpage 17
dc.identifier.publicationlastpage 2033
dc.identifier.publicationtitle IEEE Transactions on Network and Service Management
dc.identifier.publicationvolume 19
dc.identifier.uxxi AR/0000030307
dc.contributor.funder European Commission
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