9786202128902 - Autonomic Classification of IP Traffic in an NFV-based Network: Using Supervised Machine Learning Algorithms de Vergara, Juliana; Martinez, Maria Camila; Caicedo, O... (6 resultados)

Taschenbuch. Condición: Neu. Autonomic Classification of IP Traffic in an NFV-based Network | Using Supervised Machine Learning Algorithms | Juliana Vergara (u. a.) | Taschenbuch | 64 S. | Englisch | 2018 | Editorial Académica Española | EAN 9786202128902 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

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Taschenbuch. Condición: 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.

Condición: 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.

Taschenbuch. Condición: 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 Landstraße 99, 66123 Saarbrücken 64 pp. Englisch.

Taschenbuch. Condición: 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.