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Memorias de investigación
Artículos en revistas:
Forecasting short-term data center network traffic load with convolutional neural networks
Año:2018
Áreas de investigación
  • Ciencias de la computación y tecnología informática
Datos
Descripción
Efficient resource management in data centers is of central importance to content service providers as 90 percent of the network traffic is expected to go through them in the coming years. In this context we propose the use of convolutional neural networks (CNNs) to forecast short-term changes in the amount of traffic crossing a data center network. This value is an indicator of virtual machine activity and can be utilized to shape the data center infrastructure accordingly. The behaviour of network traffic at the seconds scale is highly chaotic and therefore traditional time-series-analysis approaches such as ARIMA fail to obtain accurate forecasts. We show that our convolutional neural network approach can exploit the non-linear regularities of network traffic, providing significant improvements with respect to the mean absolute and standard deviation of the data, and outperforming ARIMA by an increasingly significant margin as the forecasting granularity is above the 16-second resolution. In order to increase the accuracy of the forecasting model, we exploit the architecture of the CNNs using multiresolution input distributed among separate channels of the first convolutional layer. We validate our approach with an extensive set of experiments using a data set collected at the core network of an Internet Service Provider over a period of 5 months, totalling 70 days of traffic at the one-second resolution.
Internacional
Si
JCR del ISI
Si
Título de la revista
PLOS One
ISSN
1932-6203
Factor de impacto JCR
Información de impacto
Volumen
DOI
Número de revista
Desde la página
e0191939
Hasta la página
e0191939
Mes
SIN MES
Ranking
Q1
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: Bonifacio Alberto Mozo Velasco (UPM)
  • Autor: Bruno Ordozgoiti Rubio (UPM)
  • Autor: Sandra Maria Gomez Canaval (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Grupo de Modelización Matemática y Biocomputación
  • Departamento: Sistemas Informáticos
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