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Long-Term Forecasting of Internet Backbone Traffic.

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Název: Long-Term Forecasting of Internet Backbone Traffic.
Autoři: Papagiannaki, Konstantina1 dina.papagiannaki@intel.com, Taft, Nina2 nina.taft@intel.com, Zhi-Li Zhang3 zhzhang@cs.umn.edu, Diot, Christophe1 christophe.diot@intel.com
Zdroj: IEEE Transactions on Neural Networks. Sep2005, Vol. 16 Issue 5, p1110-1124. 15p.
Témata: *SIMPLE Network Management Protocol (Computer network protocol), *INTERNET protocols, *COMPUTER network protocols, *COMPUTER network architectures, *DATA transmission systems, *BOX-Jenkins forecasting, WAVELETS (Mathematics)
Abstrakt: 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 IF 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 MBA, 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. [ABSTRACT FROM AUTHOR]
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Databáze: Business Source Index
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Abstrakt: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 IF 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 MBA, 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. [ABSTRACT FROM AUTHOR]
ISSN:10459227
DOI:10.1109/TNN.2005.853437