2026-04-27T12:55:11Zhttps://uvadoc.uva.es/oai/request
oai:uvadoc.uva.es:10324/335602025-03-26T19:10:17Zcom_10324_1191com_10324_931com_10324_894col_10324_1379
UVaDOC author Ruiz Pérez, Lidia author Durán Barroso, Ramón José author Miguel Jiménez, Ignacio de author Khodashenas, Pouria S. author Pedreno Manresa, José Juan author Merayo Álvarez, Noemí author Aguado Manzano, Juan Carlos author Pavón Marino, Pablo author Siddiqui, Shuaib author Mata, Javier author Fernández Reguero, Patricia author Lorenzo Toledo, Rubén Mateo author Abril Domingo, Evaristo José 2018-12-19T10:49:30Z 2018-12-19T10:49:30Z 2018 Applied Sciences, 2018, vol. 8, n. 12, 2614 2076-3417 http://uvadoc.uva.es/handle/10324/33560 10.3390/app8122614 5G technologies promise to bring new network and service capacities and are expected to introduce significant architectural and service deployment transformations. The Cloud-Radio Access Networks (C-RAN) architecture, enabled by the combination of Software Defined Networking (SDN), Network Function Virtualization (NFV) and Mobile Edge Computing (MEC) technologies, play a key role in the development of 5G. In this context, this paper addresses the problems of Virtual Network Functions (VNF) provisioning (VNF-placement and service chain allocation) in a 5G network. In order to solve that problem, we propose a genetic algorithm that, considering both computing resources and optical network capacity, minimizes both the service blocking rate and CPU usage. In addition, we present an algorithm extension that adds a learning stage and evaluate the algorithm performance benefits in those scenarios where VNF allocations can be reconfigured. Results reveal and quantify the advantages of reconfiguring the VNF mapping depending on the current demands. Our methods outperform previous proposals in the literature, reducing the service blocking ratio while saving energy by reducing the number of active core CPUs. eng Attribution 4.0 International A Genetic Algorithm for VNF Provisioning in NFV-Enabled Cloud/MEC RAN Architectures info:eu-repo/semantics/article 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URL https://uvadoc.uva.es/bitstream/10324/33560/1/applsci-08-02614.pdf File MD5 dc33d9b4ba7b4af34501b79ed60ecae5 1796305 application/pdf applsci-08-02614.pdf