Software Defined Networking (SDN), characterized by a clear separation of the control and data planes, is being adopted as a novel paradigm for wired networking. With SDN, network operators can run their infrastructure more efficiently, supporting a faster deployment of new services while enabling key features such as virtualization. In this article, we adopt an SDN-like approach applied to wireless mobile networks that will not only benefit from the same features as in the wired case, but also will leverage on the distinct features of mobile deployments to push improvements even further. We illustrate with a number of representative use cases the benefits from the adoption of the proposed architecture, which is detailed in terms of modules, interfaces and high-level signaling. We also review the ongoing standardization efforts, and discuss the potential advantages, weaknesses and the need for a coordinated approach.

Abstract-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 along the day. This leads to large overprovisioning in statically managed virtual network topologies (VNT), designed to cope with the traffic forecast. To reduce expenses while ensuring the required grade of service, in this paper we propose the VNT reconfiguration approach based on data analytics for traffic prediction (VENTURE); it regularly reconfigures the VNT based on predicted traffic thus, adapting the topology to both, the current and the predicted traffic volume and direction. A machine learning algorithm based on artificial neural network (ANN) is used to provide robust and adaptive traffic models. The reconfiguration problem that takes as input the traffic prediction is modelled 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.

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