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.

CogNet: A Network Management Architecture Featuring Cognitive Capabilities / Xu, L; Assem, H; Ben Yahia, Ig; Buda, Ts; Martin, A; Gallico, D; Biancani, M; Pastor, A; Aranda, Pa; Smirnov, M; Raz, D; Uryupina, O; Mozo, A; Ordozgoiti, B; Corici, Mi; O'Sullivan, P; Mullins, R. - (2016), pp. 325-329. (Intervento presentato al convegno EuCnC tenutosi a Athens nel June 27-30, 2016).

CogNet: A Network Management Architecture Featuring Cognitive Capabilities

Uryupina, O;
2016-01-01

Abstract

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.
2016
Proceedings of the 2016 European Conference on Networks and Communications
345 E 47TH ST, NEW YORK, NY 10017 USA
IEEE
Xu, L; Assem, H; Ben Yahia, Ig; Buda, Ts; Martin, A; Gallico, D; Biancani, M; Pastor, A; Aranda, Pa; Smirnov, M; Raz, D; Uryupina, O; Mozo, A; Ordozgoiti, B; Corici, Mi; O'Sullivan, P; Mullins, R
CogNet: A Network Management Architecture Featuring Cognitive Capabilities / Xu, L; Assem, H; Ben Yahia, Ig; Buda, Ts; Martin, A; Gallico, D; Biancani, M; Pastor, A; Aranda, Pa; Smirnov, M; Raz, D; Uryupina, O; Mozo, A; Ordozgoiti, B; Corici, Mi; O'Sullivan, P; Mullins, R. - (2016), pp. 325-329. (Intervento presentato al convegno EuCnC tenutosi a Athens nel June 27-30, 2016).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/295819
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