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 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.
| Main Authors: | , |
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| Format: | Documento de conferencia biblioteca |
| Language: | eng |
| Published: |
IEE Explore
2018
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| Subjects: | PROTOCOLOS, APRENDIZAJE AUTOMÁTICO, MODELO DE DATOS, REDES, |
| Online Access: | https://repositorio.uca.edu.ar/handle/123456789/14751 |
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| Summary: | 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. |
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