Internet teletraffic modeling and estimation / / Alexandre Barbosa de Lima and José Roberto de Almeida Amazonas ; cover design by Fernando Freitas

• Record Nr.: UNINA9910464482503321
• Autore: de Lima Alexandre Barbosa
• Pubbl/distr/stampa: Aalborg, Denmark : , : River Publishers, , 2013; ©2013
• ISBN: 87-92982-94-8
• Descrizione fisica: 1 online resource (186 p.)
• Collana: River Publishers Series in Information Science and Technology
• Disciplina: 621.3851
• Soggetti: Telecommunication - Traffic; Electronic books.
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: ""Cover""; ""Contents""; ""List of Tables""; ""List of Figures""; ""Preface""; ""List of acronyms and symbols""; ""1 Introduction""; ""1.1 Objectives of telecommunications carriers""; ""1.2 Traffic characteristics""; ""1.3 Questions and contributions""; ""1.4 Time series basic concepts""; ""1.4.1 Time series examples""; ""1.4.2 Operators notation""; ""1.4.3 Stochastic processes""; ""1.4.4 Time seriesmodeling""; ""2 The fractal nature of network traffic""; ""2.1 Fractals and self-similarity examples""; ""2.1.1 The Hurst exponent""; ""2.1.2 Samplemean variance""; ""2.2 Long range dependence""; ""2.2.1 Aggregate process""""2.3 Self-similarity""; ""2.3.1 Exact second order self-similarity""; ""2.3.2 Impulsiveness""; ""2.4 Final remarks: why is the data networks traffic fractal?""; ""3 Modeling of long-range dependent teletraffic""; ""3.1 Classes of modeling""; ""3.1.1 Non-parametric modeling""; ""3.2 Wavelet transform""; ""3.2.1 Multiresolution analysis and the discrete wavelet transform""; ""3.3 ModelMWM""; ""3.4 Parametric modeling""; ""3.4.1 ARFIMAmodel""; ""3.4.2 ARFIMA models prediction - optimum estimation""; ""3.4.3 Formsof prediction""; ""3.4.4 Confidence interval""; ""3.4.5 ARFIMAprediction""""3.5 Longmemorystatistical tests""; ""3.5.1 R/Sstatistics""; ""3.5.2 GPHtest""; ""3.6 Some H and d estimation methods""; ""3.6.1 R/Sstatistics""; ""3.6.2 Variance plot""; ""3.6.3 ; Periodogram method""; ""3.6.4 Whittleâ€?s method""; ""3.6.5 Haslett and Rafteryâ€?s MV approximate estimator""; ""3.6.6 Abry andVeitchâ€?swavelet estimator""; ""3.7 Bi-spectrum and linearity test""; ""3.8 KPSS stationarity test""; ""4 State-space modeling""; ""4.1 Introduction""; ""4.2 TARFIMAmodel""; ""4.2.1 Multistep prediction with the Kalman filter""; ""4.2.2 The prediction power of the TARFIMA model""""4.3 Series exploratory analysis""; ""4.3.1 ARFIMA(0; 0.4; 0) series""; ""4.3.2 MWM series with H = 0.9""; ""4.3.3 Nile river series""; ""4.4 Prediction empirical studywith theTARFIMAmodel""; ""4.4.1 ARFIMA(0, d, 0) series""; ""4.4.2 MWMseries""; ""4.4.3 Nile river series between years 1007 and 1206""; ""4.4.4 Conclusions""; ""5 Modeling of Internet traffic""; ""5.1 Introduction""; ""5.2 Modeling of the UNC02 trace""; ""5.2.1 Exploratory analysis""; ""5.2.2 Long memory local analysis of the UNC02 trace""; ""5.2.3 Empirical prediction with the TARFIMA model""""6 Conclusions""; ""Bibliography""; ""Index""; ""About the Authors""