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  • 标题:Network traffic forecasting based on the canonical expansion of a random process
  • 本地全文:下载
  • 作者:Vitalii Savchenko ; Oleksander Matsko ; Oleh Vorobiov
  • 期刊名称:Eastern-European Journal of Enterprise Technologies
  • 印刷版ISSN:1729-3774
  • 电子版ISSN:1729-4061
  • 出版年度:2018
  • 卷号:3
  • 期号:2
  • 页码:33-41
  • DOI:10.15587/1729-4061.2018.131471
  • 语种:English
  • 出版社:PC Technology Center
  • 摘要:We studied the problem of forecasting network traffic in TCP/IP networks based on statistical observational data. We determined that existing protocols (SNMP, RMON) do not provide long-term forecasting, which is necessary for network upgrades. Regression methods (AR, ARMA, ARIMA, SARIMA), which are the basis of protocols, use only a sequence of values of forecasted series, which makes long-term forecasting impossible. We made a conclusion that there is no universal effective method for forecasting time sequences that describe traffic of a computer network.We developed the model of a forecast of network traffic taking into account features of accumulation of statistical data: presence of a priori trajectories, a posteriori character of forecasting, finiteness of variance. We applied the apparatus of the canonical expansion of a random process, taking into account heterogeneity of traffic. We developed a mathematical apparatus to solve the problem of extrapolation of implementation; we obtained expressions for the estimation of an extrapolation error, and expressions for the reconstruction of a posteriori random process based on modeling. We took into account accuracy of a priori measurements, which makes it possible to use this model in networks with a minimum of diagnostic data. It provides accurate determination of parameters of a random process at control points and the minimum standard approximation error in the intervals between these points.Application of the proposed method based on the canonical decomposition of random processes provides a solution to the problem of long-term forecasting of network traffic. A comparative analysis of forecasting methods indicates that the method of canonical decomposition of a random process comes close to intelligent forecasting methods.
  • 关键词:network traffic;forecasting control;random process;canonical decomposition of a random process
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