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Digital signature to help network management using flow analysis

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  • Mario Lemes Proença
  • Gilberto Fernandes
  • Luiz F. Carvalho
  • Marcos V. O. de Assis
  • Joel J. P. C. Rodrigues

Abstract

Because of constant growth in proportion and complexity of networks, flow analysis has become an indispensable tool for network management mechanisms. Through this resource, a traffic characterization, called digital signature of network segment using flow analysis (DSNSF), is accomplished. The models used for this purpose are the ant colony optimization metaheuristic, the Holt–Winters forecasting method and the statistical procedure, principal component analysis. The obtained DSNSF by each model is compared with the actual traffic of packets and bits and then subjected to specific evaluations in order to measure its accuracy. The experimental results show that the three methods could achieve good correlation indices and low normalized mean square error values between the DSNSF curve and the real traffic movement, indicating a good adaptability and efficiency in characterizing a network traffic segment. Copyright © 2015 John Wiley & Sons, Ltd.

Suggested Citation

  • Mario Lemes Proença & Gilberto Fernandes & Luiz F. Carvalho & Marcos V. O. de Assis & Joel J. P. C. Rodrigues, 2016. "Digital signature to help network management using flow analysis," International Journal of Network Management, John Wiley & Sons, vol. 26(2), pages 76-94, March.
    • Handle: RePEc:wly:intnem:v:26:y:2016:i:2:p:76-94
    DOI: 10.1002/nem.1892
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    References listed on IDEAS

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    1. Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
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    Cited by:

    1. Bruno L. Dalmazo & João P. Vilela & Marilia Curado, 2016. "Online traffic prediction in the cloud," International Journal of Network Management, John Wiley & Sons, vol. 26(4), pages 269-285, July.

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