Long-term forecasting of Internet backbone traffic

Konstantina Papagiannaki, Nina Taft, Zhi Li Zhang, Christophe Diot

Research output: Contribution to journalArticlepeer-review

95 Scopus citations

Abstract

We introduce a methodology to predict when and where link additions/ upgrades have to take place in an Internet protocol (IP) backbone network. Using simple network management protocol (SNMP) statistics, collected continuously since 1999, we compute aggregate demand between any two adjacent points of presence (PoPs) and look at its evolution at time scales larger than 1 h. We show that IP backbone traffic exhibits visible long term trends, strong periodicities, and variability at multiple time scales. Our methodology relies on the wavelet multiresolution analysis (MRA) and linear time series models. Using wavelet MRA, we smooth the collected measurements until we identify the overall long-term trend. The fluctuations around the obtained trend are further analyzed at multiple time scales. We show that the largest amount of variability in the original signal is due to its fluctuations at the 12-h time scale. We model inter-PoP aggregate demand as a multiple linear regression model, consisting of the two identified components. We show that this model accounts for 98% of the total energy in the original signal, while explaining 90% of its variance. Weekly approximations of those components can be accurately modeled with low-order autoregressive integrated moving average (ARIMA) models. We show that forecasting the long term trend and the fluctuations of the traffic at the 12-h time scale yields accurate estimates for at least 6 months in the future.

Original languageEnglish (US)
Pages (from-to)1110-1124
Number of pages15
JournalIEEE Transactions on Neural Networks
Volume16
Issue number5
DOIs
StatePublished - Sep 2005

Bibliographical note

Funding Information:
Manuscript received January 19, 2004; revised March 13, 2005. The work of Z.-L. Zhang was supported in part by the National Science Foundation under Grants ANI-0073819, ITR-0085824, and CAREER Award NCR-9734428. K. Papagiannaki and C. Diot are with Sprint ATL, Intel Research, Cambridge CB3 0FD, U.K. (e-mail: dina.papagiannaki@intel.com; christophe.diot@intel.com). N. Taft is with Sprint ATL, Intel Research, Berkeley, CA 94704 USA (e-mail: nina.taft@intel.com). Z.-L. Zhang is with the University of Minnesota, Minneapolis, MN 55455 USA (e-mail: zhzhang@cs.umn.edu). Digital Object Identifier 10.1109/TNN.2005.853437

Keywords

  • Autoregressive integrated moving average (ARIMA)
  • Capacity planning
  • Network provisioning
  • Time series models
  • Traffic forecasting

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