IEEE/ACM Transactions on Networking · 1994 · 5.3K citations · 43 references
ComputingComputer EngineeringComputer ScienceComputer NetworksDigital CommunicationStochastic ProcessesCommunication NetworksNetwork Performance AnalysisNetwork Traffic AnalysisQueueing TheoryStochastic NetworksTraffic Flow TheoryEthernetFractal DimensionNetwork Traffic ModelingSelf-similar ProcessesSelf-similar StructuresSelf-similar TrafficTraffic ModelingTraffic Pattern Analysis
Demonstrates that Ethernet LAN traffic is statistically self-similar, that none of the commonly used traffic models is able to capture this fractal-like behavior, that such behavior has serious implications for the design, control, and analysis of high-speed, cell-based networks, and that aggregating streams of such traffic typically intensifies the self-similarity ("burstiness") instead of smoothing it. These conclusions are supported by a rigorous statistical analysis of hundreds of millions of high quality Ethernet traffic measurements collected between 1989 and 1992, coupled with a discussion of the underlying mathematical and statistical properties of self-similarity and their relationship with actual network behavior. The authors also present traffic models based on self-similar stochastic processes that provide simple, accurate, and realistic descriptions of traffic scenarios expected during B-ISDN deployment.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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Time Series Analysis: Forecasting and Control
Michael D. Geurts, George E. P. Box, Gwilym M. Jenkins · Journal of Marketing Research · 1977 · 19.3K citations
Computing, Data Science, Forecasting +11
Robust Statistics: The Approach Based on Influence Functions
David Ruppert, Frank R. Hampel, Elvezio Ronchetti et al. · Technometrics · 1987 · 3.8K citations