Observatorio de I+D+i UPM

Descripción
Abstract: Predicting failures on networks and systems is critical in order to maintain high uptime rates. Online failure prediction (OFP) techniques use machine learning and predictive analytics to generate failure models that can be applied to computer network data. These techniques can be provisioned on state-of-the-art stream processing systems, such as Spark Streaming, in order to cope with the scalability challenges from the base data. A big challenge with OFP is selecting the right information to process, as well as the appropriate features in order to achieve high accuracy in predicting failures on complex, interconnected systems. In this article we describe an OFP system built over Apache Spark that takes a repository of network management events, trains a Random Forest model, and uses this model to predict the appearance of future events in near real time. We show through our experiments the usefulness of network management events for accurate predictions, and the advantages of the proposed system in terms of predictive quality, cost, and ease of deployment.
Internacional	Si
JCR del ISI	Si
Título de la revista	IEEE Communications Magazine
ISSN	0163-6804
Factor de impacto JCR	10,435
Información de impacto
Volumen
DOI
Número de revista
Desde la página	166
Hasta la página	170
Mes	ENERO
Ranking	1

Esta actividad pertenece a memorias de investigación

• Autor: Juan Carlos Dueñas Lopez (UPM)
• Autor: Jose Manuel Navarro Gonzalez (UPM)
• Autor: Hugo Alexer Parada Gelvez (UPM)
• Autor: Javier Andion Jimenez (UPM)
• Autor: Felix Cuadrado Latasa (UPM)

• Creador: Centro o Instituto I+D+i: Centro de I+d+i en Procesado de la Información y Telecomunicaciones
• Departamento: Ingeniería de Sistemas Telemáticos
• Departamento: Ingeniería Telemática y Electrónica