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Monitoring and Discovery for Self-Organized Network Management in Virtualized and Software Defined Networks

This paper presents the Monitoring and Discovery Framework of the Self-Organized Network Management in Virtualized and Software Defined Networks SELFNET project. This design takes into account the scalability and flexibility requirements needed by 5G infrastructures. In this context, the present fra...

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Detalles Bibliográficos
Autores principales: Valdivieso Caraguay, Ángel Leonardo, García Villalba, Luis Javier
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5421691/
https://www.ncbi.nlm.nih.gov/pubmed/28362346
http://dx.doi.org/10.3390/s17040731
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author Valdivieso Caraguay, Ángel Leonardo
García Villalba, Luis Javier
author_facet Valdivieso Caraguay, Ángel Leonardo
García Villalba, Luis Javier
author_sort Valdivieso Caraguay, Ángel Leonardo
collection PubMed
description This paper presents the Monitoring and Discovery Framework of the Self-Organized Network Management in Virtualized and Software Defined Networks SELFNET project. This design takes into account the scalability and flexibility requirements needed by 5G infrastructures. In this context, the present framework focuses on gathering and storing the information (low-level metrics) related to physical and virtual devices, cloud environments, flow metrics, SDN traffic and sensors. Similarly, it provides the monitoring data as a generic information source in order to allow the correlation and aggregation tasks. Our design enables the collection and storing of information provided by all the underlying SELFNET sublayers, including the dynamically onboarded and instantiated SDN/NFV Apps, also known as SELFNET sensors.
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spelling pubmed-54216912017-05-12 Monitoring and Discovery for Self-Organized Network Management in Virtualized and Software Defined Networks Valdivieso Caraguay, Ángel Leonardo García Villalba, Luis Javier Sensors (Basel) Article This paper presents the Monitoring and Discovery Framework of the Self-Organized Network Management in Virtualized and Software Defined Networks SELFNET project. This design takes into account the scalability and flexibility requirements needed by 5G infrastructures. In this context, the present framework focuses on gathering and storing the information (low-level metrics) related to physical and virtual devices, cloud environments, flow metrics, SDN traffic and sensors. Similarly, it provides the monitoring data as a generic information source in order to allow the correlation and aggregation tasks. Our design enables the collection and storing of information provided by all the underlying SELFNET sublayers, including the dynamically onboarded and instantiated SDN/NFV Apps, also known as SELFNET sensors. MDPI 2017-03-31 /pmc/articles/PMC5421691/ /pubmed/28362346 http://dx.doi.org/10.3390/s17040731 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Valdivieso Caraguay, Ángel Leonardo
García Villalba, Luis Javier
Monitoring and Discovery for Self-Organized Network Management in Virtualized and Software Defined Networks
title Monitoring and Discovery for Self-Organized Network Management in Virtualized and Software Defined Networks
title_full Monitoring and Discovery for Self-Organized Network Management in Virtualized and Software Defined Networks
title_fullStr Monitoring and Discovery for Self-Organized Network Management in Virtualized and Software Defined Networks
title_full_unstemmed Monitoring and Discovery for Self-Organized Network Management in Virtualized and Software Defined Networks
title_short Monitoring and Discovery for Self-Organized Network Management in Virtualized and Software Defined Networks
title_sort monitoring and discovery for self-organized network management in virtualized and software defined networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5421691/
https://www.ncbi.nlm.nih.gov/pubmed/28362346
http://dx.doi.org/10.3390/s17040731
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