Autonomic Classification of IP Traffic in an NFV-based Network: Using Supervised Machine Learning Algorithms - Couverture souple

Vergara, Juliana ; Martinez, Maria Camila ; Caicedo, Oscar

9786202128902: Autonomic Classification of IP Traffic in an NFV-based Network: Using Supervised Machine Learning Algorithms

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ISBN 10 : 6202128909 ISBN 13 : 9786202128902

Editeur : Editorial Academica Espanola, 2018

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Network Function Virtualization (NFV) is an emerging solution that improves the flexibility, efficiency, and manageability of networks by leveraging virtualization and cloud computing technologies to run networked devices in software. The implementation of NFV presents issues such as the introduction of new software components, bottleneck performance and monitoring of hidden traffic. A considerable amount of NFV traffic is invisible using traditional monitoring strategies because it does not hit a physical link. The implementation of autonomous management and supervised algorithms of Machine Learning (ML) become a key strategy to manage this hidden traffic. In this research, we focus on analyzing NFV traffic features in two test environments with different components and traffic generation. We perform a benchmarking of the performance of supervised ML algorithms concerning its efficiency; considering that the efficiency of the algorithms depends on the trade-off between the time-response and the precision achieved in the classication. The results show that the NaiveBayes and C4.5 algorithms reach values greater than 90.68 % in a response time range between 0.37 sec and 3 sec.

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�diteur: Editorial Academica Espanola
Date d'�dition: 2018
Langue: anglais
ISBN 10: 6202128909
ISBN 13: 9786202128902
Reliure: Broch�
Nombre de pages: 64
Coordonn�es du fabricant: non disponible
Personne responsable: non disponible

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Autonomic Classification of IP Traffic in an NFV-based Network

Juliana Vergara

Edit� par Editorial Acad�mica Espa�ola Jul 2018, 2018

ISBN 10 : 6202128909 ISBN 13 : 9786202128902

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Network Function Virtualization (NFV) is an emerging solution that improves the flexibility, efficiency, and manageability of networks by leveraging virtualization and cloud computing technologies to run networked devices in software. The implementation of NFV presents issues such as the introduction of new software components, bottleneck performance and monitoring of hidden traffic. A considerable amount of NFV traffic is invisible using traditional monitoring strategies because it does not hit a physical link. The implementation of autonomous management and supervised algorithms of Machine Learning (ML) become a key strategy to manage this hidden traffic. In this research, we focus on analyzing NFV traffic features in two test environments with different components and traffic generation. We perform a benchmarking of the performance of supervised ML algorithms concerning its efficiency; considering that the efficiency of the algorithms depends on the trade-off between the time-response and the precision achieved in the classication. The results show that the NaiveBayes and C4.5 algorithms reach values greater than 90.68 % in a response time range between 0.37 sec and 3 sec. 64 pp. Englisch. N� de r�f. du vendeur 9786202128902

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Autonomic Classification of IP Traffic in an NFV-based Network

Juliana Vergara|Maria Camila Martinez|Oscar Caicedo

Edit� par Editorial Acad�mica Espa�ola, 2018

ISBN 10 : 6202128909 ISBN 13 : 9786202128902

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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Vergara JulianaJuliana Alejandra Vergara Reyes and Maria Camila Martinez Ordonez are Electronics and Telecommunications Engineers from the Universidad del Cauca, Colombia. They are ISOC and IEEE ComSoc members. Their main interests a. N� de r�f. du vendeur 385929163

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Autonomic Classification of IP Traffic in an NFV-based Network

Juliana Vergara

Edit� par Editorial Acad�mica Espa�ola Jul 2018, 2018

ISBN 10 : 6202128909 ISBN 13 : 9786202128902

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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Network Function Virtualization (NFV) is an emerging solution that improves the flexibility, efficiency, and manageability of networks by leveraging virtualization and cloud computing technologies to run networked devices in software. The implementation of NFV presents issues such as the introduction of new software components, bottleneck performance and monitoring of hidden traffic. A considerable amount of NFV traffic is invisible using traditional monitoring strategies because it does not hit a physical link. The implementation of autonomous management and supervised algorithms of Machine Learning (ML) become a key strategy to manage this hidden traffic. In this research, we focus on analyzing NFV traffic features in two test environments with different components and traffic generation. We perform a benchmarking of the performance of supervised ML algorithms concerning its efficiency; considering that the efficiency of the algorithms depends on the trade-off between the time-response and the precision achieved in the classication. The results show that the NaiveBayes and C4.5 algorithms reach values greater than 90.68 % in a response time range between 0.37 sec and 3 sec.VDM Verlag, Dudweiler Landstra�e 99, 66123 Saarbr�cken 64 pp. Englisch. N� de r�f. du vendeur 9786202128902

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Autonomic Classification of IP Traffic in an NFV-based Network : Using Supervised Machine Learning Algorithms

Juliana Vergara

Edit� par Editorial Acad�mica Espa�ola, 2018

ISBN 10 : 6202128909 ISBN 13 : 9786202128902

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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Network Function Virtualization (NFV) is an emerging solution that improves the flexibility, efficiency, and manageability of networks by leveraging virtualization and cloud computing technologies to run networked devices in software. The implementation of NFV presents issues such as the introduction of new software components, bottleneck performance and monitoring of hidden traffic. A considerable amount of NFV traffic is invisible using traditional monitoring strategies because it does not hit a physical link. The implementation of autonomous management and supervised algorithms of Machine Learning (ML) become a key strategy to manage this hidden traffic. In this research, we focus on analyzing NFV traffic features in two test environments with different components and traffic generation. We perform a benchmarking of the performance of supervised ML algorithms concerning its efficiency; considering that the efficiency of the algorithms depends on the trade-off between the time-response and the precision achieved in the classication. The results show that the NaiveBayes and C4.5 algorithms reach values greater than 90.68 % in a response time range between 0.37 sec and 3 sec. N� de r�f. du vendeur 9786202128902

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