TY - GEN
T1 - Self-modeling based diagnosis of Software-Defined Networks
AU - Sanchez, Jose Manuel
AU - Grida Ben Yahia, Imen
AU - Crespi, Noel
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/6/1
Y1 - 2015/6/1
N2 - Networks built using SDN (Software-Defined Networks) and NFV (Network Functions Virtualization) approaches are expected to face several challenges such as scalability, robustness and resiliency. In this paper, we propose a self-modeling based diagnosis to enable resilient networks in the context of SDN and NFV. We focus on solving two major problems: On the one hand, we lack today of a model or template that describes the managed elements in the context of SDN and NFV. On the other hand, the highly dynamic networks enabled by the softwarisation require the generation at runtime of a diagnosis model from which the root causes can be identified. In this paper, we propose finer granular templates that do not only model network nodes but also their sub-components for a more detailed diagnosis suitable in the SDN and NFV context. In addition, we specify and validate a self-modeling based diagnosis using Bayesian Networks. This approach differs from the state of the art in the discovery of network and service dependencies at run-Time and the building of the diagnosis model of any SDN infrastructure using our templates.
AB - Networks built using SDN (Software-Defined Networks) and NFV (Network Functions Virtualization) approaches are expected to face several challenges such as scalability, robustness and resiliency. In this paper, we propose a self-modeling based diagnosis to enable resilient networks in the context of SDN and NFV. We focus on solving two major problems: On the one hand, we lack today of a model or template that describes the managed elements in the context of SDN and NFV. On the other hand, the highly dynamic networks enabled by the softwarisation require the generation at runtime of a diagnosis model from which the root causes can be identified. In this paper, we propose finer granular templates that do not only model network nodes but also their sub-components for a more detailed diagnosis suitable in the SDN and NFV context. In addition, we specify and validate a self-modeling based diagnosis using Bayesian Networks. This approach differs from the state of the art in the discovery of network and service dependencies at run-Time and the building of the diagnosis model of any SDN infrastructure using our templates.
KW - Bayesian networks
KW - NFV
KW - SDN
KW - self-diagnosis
KW - self-modeling
U2 - 10.1109/NETSOFT.2015.7116174
DO - 10.1109/NETSOFT.2015.7116174
M3 - Conference contribution
AN - SCOPUS:84945315137
T3 - 1st IEEE Conference on Network Softwarization: Software-Defined Infrastructures for Networks, Clouds, IoT and Services, NETSOFT 2015
BT - 1st IEEE Conference on Network Softwarization
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 1st IEEE Conference on Network Softwarization, NETSOFT 2015
Y2 - 13 April 2015 through 17 April 2015
ER -