Skip to main content

Improving Network Measurement Efficiency through Multiadaptive Sampling

  • Conference paper
Traffic Monitoring and Analysis (TMA 2012)

Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 7189))

Included in the following conference series:

  • 1153 Accesses

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 54.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 69.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  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. 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), http://dx.doi.org/10.1007/s10559-008-9005-0

    Article  MATH  Google Scholar 

  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), http://dx.doi.org/10.1023/A:1012980307500

    Article  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  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. 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 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 IFIP International Federation for Information Processing

About this paper

Cite this paper

Silva, J.M.C., Lima, S.R. (2012). Improving Network Measurement Efficiency through Multiadaptive Sampling. In: Pescapè, A., Salgarelli, L., Dimitropoulos, X. (eds) Traffic Monitoring and Analysis. TMA 2012. Lecture Notes in Computer Science, vol 7189. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28534-9_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-28534-9_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28533-2

  • Online ISBN: 978-3-642-28534-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics