Improving Network Measurement Efficiency through Multiadaptive Sampling

  • João Marco C. Silva
  • Solange Rito Lima
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7189)


Sampling techniques play a key role in achieving efficient network measurements by reducing the amount of traffic processed while trying to maintain the accuracy of network statistical behavior estimation.

Despite the evolution of current techniques regarding the correctness of network parameters estimation, the overhead associated with the volume of data involved in the sampling process is still considerable. In this context, this paper proposes a new technique for multiadaptive traffic sampling based on linear prediction, which allows to reduce significantly the traffic under analysis, keeping the representativeness of samples in capturing network behavior.

A proof-of-concept, evaluating this technique for real traffic traces representing distinct traffic profiles, demonstrates the effectiveness of the proposal, outperforming classic techniques both in accuracy and data volumes processed.


Network Activity Linear Prediction Network Measurement Adaptive Sampling Network Behavior 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Dogman, A., Saatchi, R., Al-Khayatt, S.: An adaptive statistical sampling technique for computer network traffic. In: 2010 7th International Symposium on Communication Systems Networks and Digital Signal Processing (CSNDSP), pp. 479–483 (July 2010)Google Scholar
  2. 2.
    Giertl, J., Baca, J., Jakab, F., Andoga, R.: Adaptive sampling in measuring traffic parameters in a computer network using a fuzzy regulator and a neural network. Cybernetics and Systems Analysis 44, 348–356 (2008), CrossRefzbMATHGoogle Scholar
  3. 3.
    Hernandez, E.A., Chidester, M.C., George, A.D.: Adaptive sampling for network management. Journal of Network and Systems Management 9, 409–434 (2001), CrossRefGoogle Scholar
  4. 4.
    Lu, Y., He, C.: Resource allocation using adaptive linear prediction in wdm/tdm epons. AEU - International Journal of Electronics and Communications 64(2), 173–176 (2010)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Wei, Y., Wang, J., Wang, C.: A traffic prediction based bandwidth management algorithm of a future internet architecture. In: International Workshop on Intelligent Networks and Intelligent Systems, pp. 560–563 (2010)Google Scholar
  6. 6.
    Xin, Q., Hong, L., Fang, L.: A modified flc adaptive sampling method. In: WRI International Conference on Communications and Mobile Computing, CMC 2009, vol. 2, pp. 515–520 (January 2009)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2012

Authors and Affiliations

  • João Marco C. Silva
    • 1
  • Solange Rito Lima
    • 1
  1. 1.Departamento de Informática, Centro AlgoritmiUniversidade do MinhoPortugal

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