© 2017 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. ; The introduction of new services requiring large and dynamic bitrate connectivity can cause changes in the direction of the traffic in metro and even core network segments throughout the day. This leads to large overprovisioning in statically managed virtual network topologies (VNTs), which are designed to cope with the traffic forecast. To reduce expenses while ensuring the required grade of service, in this paper we propose a VNT reconfiguration approach based on data analytics for traffic prediction (VENTURE). It regularly reconfigures the VNT based on the predicted traffic, thus adapting the topology to both the current and the predicted traffic volume and direction. A machine learning algorithm based on an artificial neural network is used to provide robust and adaptive traffic models. The reconfiguration problem that takes as its input the traffic prediction is modeled mathematically, and a heuristic is proposed to solve it in practical times. To support VENTURE, we propose an architecture that allows collecting and storing data from monitoring at the routers and that is used to train predictive models for every origin-destination pair. Exhaustive simulation results of the algorithm, together with the experimental assessment of the proposed architecture, are finally presented. ; Peer Reviewed ; Postprint (published version)


    Access

    Download


    Export, share and cite



    Big data analytics for the virtual network topology reconfiguration use case

    Gifre Renom, Lluís / Morales Alcaide, Fernando / Velasco Esteban, Luis Domingo et al. | BASE | 2016

    Free access

    DeepTFP: Mobile Time Series Data Analytics based Traffic Flow Prediction

    Chen, Yuanfang / Chen, Falin / Ren, Yizhi et al. | ArXiv | 2017

    Free access

    Real-Time Traffic Flow Prediction Using Big Data Analytics

    Tran, Dang-Khoa / Hoang, Dinh-Quang / Le, Viet-Thang et al. | Springer Verlag | 2022


    When Traffic Flow Prediction Meets Wireless Big Data Analytics

    Chen, Yuanfang / Guizani, Mohsen / Zhang, Yan et al. | ArXiv | 2017

    Free access

    Big Data Analytics of Network Traffic and Attacks

    Wang, Lidong / Jones, Randy | IEEE | 2018