N2 - We introduce a methodology to predict when and where link additions/ upgrades have to take place in an Internet protocol (IP) backbone network. Zhang is with the University of Minnesota, Minneapolis, MN 55455 USA (e-mail: Digital Object Identifier 10.1109/TNN.2005.853437 ![]() ![]() Taft is with Sprint ATL, Intel Research, Berkeley, CA 94704 USA (e-mail: Z.-L. Diot are with Sprint ATL, Intel Research, Cambridge CB3 0FD, U.K. Zhang was supported in part by the National Science Foundation under Grants ANI-0073819, ITR-0085824, and CAREER Award NCR-9734428. Manuscript received Janurevised March 13, 2005. T1 - Long-term forecasting of Internet backbone traffic 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.", Weekly approximations of those components can be accurately modeled with low-order autoregressive integrated moving average (ARIMA) models. We show that this model accounts for 98% of the total energy in the original signal, while explaining 90% of its variance. We model inter-PoP aggregate demand as a multiple linear regression model, consisting of the two identified components. We show that the largest amount of variability in the original signal is due to its fluctuations at the 12-h time scale. The fluctuations around the obtained trend are further analyzed at multiple time scales. Using wavelet MRA, we smooth the collected measurements until we identify the overall long-term trend. Our methodology relies on the wavelet multiresolution analysis (MRA) and linear time series models. We show that IP backbone traffic exhibits visible long term trends, strong periodicities, and variability at multiple time scales. 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 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.Ībstract = "We introduce a methodology to predict when and where link additions/ upgrades have to take place in an Internet protocol (IP) backbone network. ![]() We introduce a methodology to predict when and where link additions/ upgrades have to take place in an Internet protocol (IP) backbone network.
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