TY - GEN
T1 - CogNet
AU - Xu, Lei
AU - Assem, Haytham
AU - Yahia, Imen Grida Ben
AU - Buda, Teodora Sandra
AU - Martin, Angel
AU - Gallico, Domenico
AU - Biancani, Matteo
AU - Pastor, Antonio
AU - Aranda, Pedro A.
AU - Smirnov, Mikhail
AU - Raz, Danny
AU - Uryupina, Olga
AU - Mozo, Alberto
AU - Ordozgoiti, Bruno
AU - Corici, Marius Iulian
AU - O'Sullivan, Pat
AU - Mullins, Robert
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/9/6
Y1 - 2016/9/6
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84988984644&partnerID=8YFLogxK
U2 - 10.1109/EuCNC.2016.7561056
DO - 10.1109/EuCNC.2016.7561056
M3 - Conference contribution
AN - SCOPUS:84988984644
T3 - EUCNC 2016 - European Conference on Networks and Communications
SP - 325
EP - 329
BT - EUCNC 2016 - European Conference on Networks and Communications
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 27 June 2016 through 30 June 2016
ER -