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CogNet: A network management architecture featuring cognitive capabilities

: Xu, Lei; Assem, Haytham; Yahia, Imen Griden Ben; Buda, Teodora Sandra; Martin, Angel; Gallico, Domenico; Biancani, Matteo; Pastor, Antonio; Aranda, Pedro A.; Smirnov, Mikhail; Raz, Danny; Uryupina, Olga; Mozo, Alberto; Ordozgoiti, Bruno; O'Sullivan, Pat; Mullins, Robert


Onopas, G.A. ; Institute of Electrical and Electronics Engineers -IEEE-; IEEE Communications Society:
The dawn of 5G. EuCNC 2016, European Conference on Networks and Communications : Athens, Greece, June 27-30, 2016
Piscataway, NJ: IEEE, 2016
ISBN: 978-1-5090-2893-1
ISBN: 978-1-5090-2894-8 (Print)
European Conference on Networks and Communications (EuCNC) <25, 2016, Athens>
European Commission EC
H2020; 671625; CogNet
Fraunhofer FOKUS ()

It is expected that the fifth generation mobile networks (5G) will support both human-to-human and machine-to-machine communications, connecting up to trillions of devices and reaching formidable levels of complexity and traffic volume. This brings a new set of challenges for managing the network due to the diversity and the sheer size of the network. It will be necessary for the network to largely manage itself and deal with organisation, configuration, security, and optimisation issues. This paper proposes an architecture of an autonomic self-managing network based on Network Function Virtualization, which is capable of achieving or balancing objectives such as high QoS, low energy usage and operational efficiency. The main novelty of the architecture is the Cognitive Smart Engine introduced to enable Machine Learning, particularly (near) real-time learning, in order to dynamically adapt resources to the immediate requirements of the virtual network functions, while minimizing performance degradations to fulfill SLA requirements. This architecture is built within the CogNet European Horizon 2020 project, which refers to Cognitive Networks.