Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2001 · 18 citations · 0 references
ComputingComputer ScienceInformation TechnologyComputer NetworksData MiningWeb AnalyticsCommunication NetworksNetwork Capacity PlanningNetwork Performance AnalysisNetwork Traffic AnalysisQueueing TheoryNetwork CapacityNetwork Traffic ModelingSelf-similar TrafficStatistical Traffic AnalysisTraffic DistributionWeb Traffic Measurement
In recent years, several studies have shown that network traffic exhibits the property of self-similarity. Traditional (Poissonian) modelling approaches have been shown not to be able to describe this property and generally lead to the underestimation of interesting performance measures. Crovella and Bestavros have shown that network traffic that is due to World Wide Web transfers shows characteristics of self-similarity and they argue that this can be explained by the heavy-tailedness of many of the involved distributions. Considering these facts, developing methods which are able to handle self-similarity and heavy-tailedness is of great importance for network capacity planing purposes. In this paper we discuss two methods to fit hyper-exponential distributions to data sets which exhibit heavy-tails. One method is taken from the literature and shown to fall short. The other, new method, is shown to perform well in a number of case studies.