Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/7634

TitleTopology aware Internet traffic forecasting using neural networks
Author(s)Cortez, Paulo
Rio, Miguel
Sousa, Pedro
Rocha, Miguel
KeywordsLink mining
Multilayer perceptrons
Multivariate time series
Network monitoring
Traffic engineering
Issue dateSep-2007
PublisherSpringer
JournalLecture Notes in Computer Science
CitationSÁ, Joaquim Marques de [et. al.], eds. – “Artificial Neural Networks : ICANN 2007 : proceedings of the 17th International Conference On Artificial Neural Networks, Porto, Portugal, 2007”. Heidelberg : Springer Berlin, 2007. Part. II. ISBN 978-3-540-74693-5.
Abstract(s)Forecasting Internet traffic is receiving an increasing attention from the computer networks domain. Indeed, by improving this task efficient traffic engineering and anomaly detection tools can be developed, leading to economic gains due to better resource management. This paper presents a Neural Network (NN) approach to predict TCP/IP traffic for all links of a backbone network, using both univariate and multivariate strategies. The former uses only past values of the forecasted link, while the latter is based on the neighbor links of the backbone topology. Several experiments were held by considering real-world data from the UK education and research network. Also, different time scales (e.g. every ten minutes and hourly) were analyzed. Overall, the proposed NN approach outperformed other forecasting methods (e.g. Holt-Winters).
TypeconferencePaper
URIhttp://hdl.handle.net/1822/7634
ISBN978-3-540-74693-5
ISSN0302-9743
Publisher versionhttp://springerlink.com/content/g25073613398/?p=3564e034865f469d8d07b1dc29446ed8&pi=209
Peer-Reviewedyes
AccessopenAccess
Appears in Collections:DSI - Engenharia da Programação e dos Sistemas Informáticos
DI/CCTC - Artigos (papers)
CAlg - Artigos em livros de atas/Papers in proceedings

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