Long-range dependence in a changing Internet traffic mix

Cited 42 time in webofscience Cited 0 time in scopus
  • Hit : 37
  • Download : 0
This paper provides a deep analysis of long-range dependence in a continually evolving Internet traffic mix by employing a number of recently developed statistical methods. Our study considers time-of-day, day-of-week, and cross-year variations in the traffic on an Internet link. Surprisingly large and consistent differences in the packet-count time series were observed between data from 2002 and 2003. A careful examination, based on stratifying the data according to protocol, revealed that the large difference was driven by a single UDP application that was not present in 2002. Another result was that the observed large differences between the two years showed up only in packet-count time series, and not in byte counts (while conventional wisdom suggests that these should be similar). We also found and analyzed several of the time series that exhibited more "bursty" characteristics than could be modeled as fractional Gaussian noise. The paper also shows how modern statistical tools can be used to study long-range dependence and non-stationarity in Internet traffic data. (c) 2005 Elsevier B.V. All rights reserved.
Issue Date
Article Type

COMPUTER NETWORKS, v.48, no.3, pp.401 - 422

Appears in Collection
MA-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 42 items in WoS Click to see citing articles in records_button


  • mendeley


rss_1.0 rss_2.0 atom_1.0