Quality of service assesment using machine learning techniques for the NETCONF protocol

Abstract: Study of an unsupervised machine learning approach for the testing results defined by the RFC2544 - ITU.Y1564 standard methodologies and the use of NETCONF protocol to automatically assess traffic parameters required to comply with quality of service level agreements. By doing disrupt...

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Detalles Bibliográficos
Autores principales: Ouret, Javier A., Parravicini, Ignacio
Formato: Documento de conferencia
Lenguaje:Inglés
Publicado: IEE Explore 2022
Materias:
PROTOCOLOS
APRENDIZAJE AUTOMÁTICO
MODELO DE DATOS
REDES
Acceso en línea:https://repositorio.uca.edu.ar/handle/123456789/14751
Aporte de:
Repositorio Institucional de la Universidad Católica Argentina (UCA) de Universidad Católica Argentina
id I33-R139-123456789-14751
record_format dspace
institution Universidad Católica Argentina
institution_str I-33
repository_str R-139
collection Repositorio Institucional de la Universidad Católica Argentina (UCA)
language Inglés
topic PROTOCOLOS
APRENDIZAJE AUTOMÁTICO
MODELO DE DATOS
REDES
spellingShingle PROTOCOLOS
APRENDIZAJE AUTOMÁTICO
MODELO DE DATOS
REDES
Ouret, Javier A.
Parravicini, Ignacio
Quality of service assesment using machine learning techniques for the NETCONF protocol
topic_facet PROTOCOLOS
APRENDIZAJE AUTOMÁTICO
MODELO DE DATOS
REDES
description Abstract: Study of an unsupervised machine learning approach for the testing results defined by the RFC2544 - ITU.Y1564 standard methodologies and the use of NETCONF protocol to automatically assess traffic parameters required to comply with quality of service level agreements. By doing disruptive and non-disruptive tests for service integrity, a service provider can certify that the working parameters of a delivered Ethernet circuit complies with the end user expectations, to avoid poor application performance. This work focus in an unsupervised learning approach using Expectation Maximization based clustering algorithm. We find that the unsupervised technique used is an excellent tool for exploring and classify service parameters like frame delay, frame delay variation, packet high loss intervals, availability and throughput. A correlation of parameters with the type of service required for the network flows (real time IP for data, video and voice applications) can be applied to automatically set bandwidth profiles. The bandwidth profiles can be configured per port, VLAN and CoS based, in one or multiple EVCs (Ethernet Virtual Circuits) per UNI device port. For the setup we adopt the Yang data modeling language and XML NETCONF message encoding protocol, followed by a delayed or an optional non-delayed orchestrated activation in the network devices via multiple NETCONF transactions.
format Documento de conferencia
author Ouret, Javier A.
Parravicini, Ignacio
author_facet Ouret, Javier A.
Parravicini, Ignacio
author_sort Ouret, Javier A.
title Quality of service assesment using machine learning techniques for the NETCONF protocol
title_short Quality of service assesment using machine learning techniques for the NETCONF protocol
title_full Quality of service assesment using machine learning techniques for the NETCONF protocol
title_fullStr Quality of service assesment using machine learning techniques for the NETCONF protocol
title_full_unstemmed Quality of service assesment using machine learning techniques for the NETCONF protocol
title_sort quality of service assesment using machine learning techniques for the netconf protocol
publisher IEE Explore
publishDate 2022
url https://repositorio.uca.edu.ar/handle/123456789/14751
work_keys_str_mv AT ouretjaviera qualityofserviceassesmentusingmachinelearningtechniquesforthenetconfprotocol
AT parraviciniignacio qualityofserviceassesmentusingmachinelearningtechniquesforthenetconfprotocol
bdutipo_str Repositorios
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