Time series models for internet traffic

Authors:
Sabyasachi Basu;Amarnath Mukherjee;Steve Klivansky
Affiliations:
Department of Statistical Science, Southern Methodist University, Dallas, TX;College of Computing, Georgia Institute of Technology, Atlanta, GA;College of Computing, Georgia Institute of Technology, Atlanta, GA
Venue:
INFOCOM'96 Proceedings of the Fifteenth annual joint conference of the IEEE computer and communications societies conference on The conference on computer communications - Volume 2
Year:
1996

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Abstract

Data traffic sequences from two campus FDDI rings, an Ethernet, two entry/exit points of the NSFNET, and sub-sequences belonging to popular TCP port numbers on one of the FDDI rings indicate that appropriately differenced time-series generated from these traces can be modeled as Auto-Regressive-Moving-Average (ARMA) processes. The variates of the ARMA filter are, however, non-Gaussian. A sequence of steps leading through (i) parameter estimation, (ii) generating the distribution of the variates, (iii) forecasting tail percentiles, and (iv) synthetic generation of non-negative integer sequences is presented. The data indicates that parameter estimates drift slowly with time and may need to be recomputed periodically for accurate forecasts. The forecasting algorithm has potential application in dynamic resource allocation. The synthetic traffic generation algorithm may be used in simulation studies of resource management algorithms.