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    Joint route selection and split level management for 5G C-RAN

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    JointRouteSelection.pdf (2.800Mb)
    Identificadores
    URI: http://hdl.handle.net/10902/23426
    DOI: 10.1109/TNSM.2021.3091543
    ISSN: 1932-4537
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    Autoría
    Erazo-Agredo, Cristian C.; Garza-Fabre, Mario; Agüero Calvo, RamónAutoridad Unican; Díez Fernández, Luis FranciscoAutoridad Unican; Serrat Fernández, Joan; Rubio-Loyola, Javier
    Fecha
    2021-12
    Derechos
    © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Publicado en
    IEEE Transactions on Network and Service Management, 2021, 18(4), 4616-4638
    Editorial
    Institute of Electrical and Electronics Engineeers
    Enlace a la publicación
    https://doi.org/10.1109/TNSM.2021.3091543
    Palabras clave
    Route selection
    Functional split
    5G
    C-RAN
    Resumen/Abstract
    This work tackles the problem faced by network/infrastructure providers of jointly selecting routing and functional split level to satisfy requests from virtual mobile network operators (vMNOs). We build a novel system model that brings together all the involved elements and features, embracing split levels defined by the 3GPP and packet switch fronthaul network. To our best knowledge, this is the first work that provides a solution for multiple vMNO requests considering the two aforementioned sub-problems (i.e. split selection and routing). We use the model defined to formulate an optimization problem, which is characterized by the exponential size of its search space. We propose two heuristic approaches to address this problem: (1) a greedy scheme, and (2) an evolutionary algorithm, which is also improved with a specialized initialization. We conduct extensive experiments to assess the performance and behavior of the proposed methods, over varying network instances. When possible, we also perform comparisons with respect to the optimal solution and a well-known commercial solver. Our results indicate that the proposed techniques represent appropriate trade-offs between solution quality and execution time, and can serve complementary goals: the quality of the results yielded by our evolutionary method are better, but at the cost of longer execution times; in contrast, our greedy algorithm offers a reasonably appropriate performance, with an execution time that is notably lower. Our experiments show that it is possible to produce near-optimal results to the above complex problem through computationally efficient algorithmic solutions.
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    UNIVERSIDAD DE CANTABRIA

    Repositorio realizado por la Biblioteca Universitaria utilizando DSpace software
    Contacto | Sugerencias
    Metadatos sujetos a:licencia de Creative Commons Reconocimiento 4.0 España