Skip to main content
Log in

A novel network performance evaluation method based on maximizing deviations

  • Published:
Telecommunication Systems Aims and scope Submit manuscript

Abstract

Analyzing and evaluating the performance of an entire network is vital for the Internet development and also a great challenge to the Internet community. This paper proposes a network performance evaluation method based on maximizing deviations (NPEMD for short) to evaluate the performance of an entire network in a quantitative way. This method is based on the external properties of the network performance matrix model and applies the uncertain multiple attribute decision making theory. Two sets of experiments are designed, which are based on typical network topologies and stochastic topologies respectively. The results demonstrate that from various aspects this novel method is feasible and effective in evaluating the overall network performance.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Bu, T., & Towsley, D. (2002). On distinguishing between Internet power law topology generators. In Proc. of IEEE INFOCOM’02 (pp. 638–647). New York: IEEE Press.

    Google Scholar 

  2. Chen, C.-T., & Hung, W.-Z. (2009). Applying ELECTRE and maximizing deviation method for stock portfolio selection under fuzzy environment. Studies in Computational Intelligence, 214, 85–91.

    Article  Google Scholar 

  3. Grochla, K. (2008). Simulation comparison of active queue management algorithms in TCP/IP networks. Telecommunications Systems, 39(2), 131–136.

    Article  Google Scholar 

  4. Kalidindi, S., & Zekauskas, M. J. (1999). Surveyor: an infrastructure for Internet performance measurements. In Proc. of INET’99, Stockholm, Sweden.

    Google Scholar 

  5. Li, C., & Zhao, W. (2009). Stochastic performance analysis of non-feedforward networks. Telecommunications Systems, 39(3–4), 237–252.

    Google Scholar 

  6. Li, L., Alderson, D., Willinger, W., & Doyle, J. (2004). A first-principles approach to understanding the internets router-level topology. In Proc. of ACM SIGCOMM’04, Portland, Oregon, USA (pp. 3–14). New York: ACM.

    Google Scholar 

  7. Ishibashi, K., Kimura, T., & Ozawa, T. (2000). A measurement-based performance evaluation method for IP networks and its implementation. Telecommunications Systems, 15(1–2), 203–215.

    Article  Google Scholar 

  8. Medina, A., Lakhina, A., Matta, I., & Byers, J. (2001). BRITE: an approach to universal topology generation. In Proc. of the MASCOTS’01 (pp. 346–353). Washington: IEEE Comp. Soc..

    Google Scholar 

  9. Paxson, V., Mahdavi, J., Adams, A., & Mathis, M. (1998). An architecture for large-scale Internet measurement. IEEE Communications Letters, 36(8), 48–54.

    Article  Google Scholar 

  10. Ribeiro, V. J., Riedi, R. H., Baraniuk, R. G., Navratil, J., & Cottrell, L. (2003). PathChirp: efficient available bandwidth estimation for network paths. In Proc. of passive and active measurement workshop (PAM’03), San Diego, California, USA (pp. 359–386).

    Google Scholar 

  11. Ritke, R., Hong, X., & Gerla, M. (2001). Contradictory relationship between Hurst parameter and queueing performance (extended version). Telecommunications Systems, 16(1–2), 159–175.

    Article  Google Scholar 

  12. Schwartz, M. (1996). Broadband integrated networks. Englewood Cliffs: Prentice-Hall.

    Google Scholar 

  13. Stephan, E. (2005). IP performance metrics (IPPM) metrics registry. IETF RFC 4148.

  14. Tangmunarunkit, H., Govindan, R., Jamin, S., Shenker, S., & Willinger, W. (2002). Network topology generators: degree-based vs. structural. In Proc. of ACM SIGCOMM’02, Pittsburgh (pp. 147–159). New York: ACM.

    Google Scholar 

  15. Taqqu, M. S., & Levy, J. (1986). Using renewal processes to generate long-range dependence and high variability. In E. Eberlein & M. S. Taqqu (Eds.), Dependence in probability and statistics (pp. 73–89). Boston: Birkhauser.

    Chapter  Google Scholar 

  16. Xiong, H., & Chen, M. (2006). Towards an AS level network performance mode. World Academy of Science, Engineering and Technology, 18, 74–79.

    Google Scholar 

  17. Yoon, K. P., & Hwang, C. L. (1995). Multiple attribute decision making: an introduction. Thousand Oaks, CA: Sage Publications.

    Google Scholar 

  18. Zhang, D., Hu, M., & Zhang, H. (2006). Study on network performance evaluation method based on measurement. Journal of Communications, 27(10), 74–79. .

    Google Scholar 

Download references

Acknowledgements

This work was supported by the Major State Development Basic Research Program of China (973 Program) (Grant No. 2012CB315806), the National Natural Science Foundation of China (Grant Nos. 61070173 and 61103225), and Jiangsu Province Natural Science Foundation of China (Grant No. BK2010133).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Huali Bai.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chen, M., Bai, H., Zhou, Y. et al. A novel network performance evaluation method based on maximizing deviations. Telecommun Syst 55, 149–158 (2014). https://doi.org/10.1007/s11235-013-9759-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11235-013-9759-1

Keywords

Navigation