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Self-similar processes in telecommunications / / Oleg I. Sheluhin, Sergey M. Smolskiy, Andrey V. Osin
Self-similar processes in telecommunications / / Oleg I. Sheluhin, Sergey M. Smolskiy, Andrey V. Osin
Autore Sheluhin Oleg I.
Pubbl/distr/stampa Chichester, England ; , : Wiley, , c2007
Descrizione fisica 1 online resource (336 p.)
Disciplina 621.382
621.382150151922
Altri autori (Persone) SmolskiySergey M
OsinAndrey V
Soggetto topico Telecommunication systems - Mathematical models
Internetworking (Telecommunication)
Self-similar processes
ISBN 1-282-34615-6
9786612346156
0-470-06209-6
0-470-06210-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Foreword -- About the authors -- Acknowledgements -- 1 Principal Concepts of Fractal Theory and Self-Similar Processes -- 1.1 Fractals and Multifractals -- 1.1.1 Fractal Dimension of a Set -- 1.1.2 Multifractals -- 1.1.3 Fractal Dimension D0 and Informational Dimension D1 -- 1.1.4 Legendre Transform -- 1.2 Self-Similar Processes -- 1.2.1 Definitions and Properties of Self-Similar Processes -- 1.2.2 Multifractal Processes -- 1.2.3 Long-Range and Short-Range Dependence -- 1.2.4 Slowly Decaying Variance -- 1.3 'Heavy Tails' -- 1.3.1 Distribution with 'Heavy Tails' (DHT) -- 1.3.2 'Heavy Tails' Estimation -- 1.4 Hurst Exponent Estimation -- 1.4.1 Time Domain Methods of Hurst Exponent Estimation -- 1.4.2 Frequency Domain Methods of Hurst Exponent -- Estimation -- 1.5 Hurst Exponent Estimation Problems -- 1.5.1 Estimation Problems -- 1.5.2 Nonstationarity Problems -- 1.5.3 Computational Problems -- 1.6 Self-Similarity Origins in Telecommunication Traffic -- 1.6.1 User's Behaviour -- 1.6.2 Data Generation Data Structure and Its Search -- 1.6.3 Traffic Aggregation -- 1.6.4 Means of Network Control -- 1.6.5 Control Mechanisms based on Feedback -- 1.6.6 Network Development -- References -- 2 Simulation Methods for Fractal Processes -- 2.1 Fractional Brownian Motion -- 2.1.1 RMD Algorithm for FBM Generation -- 2.1.2 SRA Algorithm for FBM Generation -- 2.2 Fractional Gaussian Noise -- 2.2.1 FFT Algorithm for FGN Synthesis -- 2.2.2 Advantages and Shortcomings of FBM/FGN Models -- in Network Applications -- 2.3 Regression Models of Traffic -- 2.3.1 Linear Autoregressive (AR) Processes -- 2.3.2 Processes of Moving Average (MA) -- 2.3.3 Autoregressive Models of Moving Average, ARMA#p; q -- 2.3.4 Fractional Autoregressive Integrated Moving Average -- (FARIMA) Process -- 2.3.5 Parametric Estimation Methods -- 2.3.6 FARIMA#p,d,q Process Synthesis -- 2.4 Fractal Point Process -- 2.4.1 Statistical Characteristics of the Point Process -- 2.4.2 Fractal Structure of FPP -- 2.4.3 Methods of FPP Formation.
2.5 Fractional Levy Motion and its Application to Network -- Traffic Modelling -- 2.5.1 Fractional Levy Motion and Its Properties -- 2.5.2 Algorithm of Fractional Levy Motion Modelling -- 2.5.3 Fractal Traffic Formation Based on FLM -- 2.6 Models of Multifractal Network Traffic -- 2.6.1 Multiplicative Cascades -- 2.6.2 Modified Estimation Method of Multifractal Functions -- 2.6.3 Generation of Traffic the Multifractal Model -- 2.7 LRD Traffic Modelling with the Help of Wavelets -- 2.8 M/G/1Model -- 2.8.1 M/G/1Model and Pareto Distribution -- 2.8.2 M/G/1Model and Log-Normal Distribution -- References -- 3 Self-Similarity of Real Time Traffic -- 3.1 Self-Similarity of Real Time Traffic Preliminaries -- 3.2 Statistical Characteristics of Telecommunication Real Time Traffic -- 3.2.1 Measurement Organization -- 3.2.2 Pattern of TN Traffic -- 3.3 Voice Traffic Characteristics -- 3.3.1 Voice Traffic Characteristics at the Call Layer -- 3.3.2 Voice Traffic Characteristics at the Packet Layer -- 3.4 Multifractal Analysis of Voice Traffic -- 3.4.1 Basics -- 3.4.2 Algorithm for the Partition Function Sm#q Calculation -- 3.4.3 Multifractal Properties of Multiplexed Voice Traffic -- 3.4.4 Multifractal Properties of Two-Component Voice Traffic -- 3.5 Mathematical Models of VoIP Traffic -- 3.5.1 Problem Statement -- 3.5.2 Voice Traffic Models at the Call Layer -- 3.5.3 Estimation of Semi-Markovian Model Parameters and the Modelling -- Results of the Voice Traffic at the Call Layer -- 3.5.4 Mathematical Models of Voice Traffic at the Packets Layer -- 3.6 Simulation of the Voice Traffic -- 3.6.1 Simulation Structure -- 3.6.2 Parameters Choice of Pareto Distributions for Voice -- Traffic Source in ns2 -- 3.6.3 Results of Separate Sources Modelling -- 3.6.4 Results of Traffic Multiplexing for the Separate -- ON/OFF Sources -- 3.7 Long-Range Dependence for the VBR-Video -- 3.7.1 Distinguished Characteristics of Video Traffic -- 3.7.2 Video Conferences -- 3.7.3 Video Broadcasting -- 3.7.4 MPEG Video Traffic.
3.7.5 Nonstationarity of VBR Video Traffic -- 3.8 Self-Similarity Analysis of Video Traffic -- 3.8.1 Video Broadcasting Wavelet Analysis -- 3.8.2 Numerical Results -- 3.8.3 Multifractal Analysis -- 3.9 Models and Modelling of Video Sequences -- 3.9.1 Nonstationarity Types for VBR Video Traffic -- 3.9.2 Model of the Video Traffic Scene Changing Based on the -- Shifting Level Process -- 3.9.3 Video Traffic Models in the Limits of the Separate Scene -- 3.9.4 Fractal Autoregressive Models of p-Order -- 3.9.5 MPEG Data Modelling Using I, P and B Frames Statistics -- 3.9.6 ON/OFF Model of the Video Sequences -- 3.9.7 Self-Similar Norros Model -- 3.9.8 Hurst Exponent Dependence on N -- References -- 4 Self-Similarity of Telecommunication Networks Traffic -- 4.1 Problem Statement -- 4.2 Self-Similarity and 'Heavy Tails' in Lan Traffic -- 4.2.1 Experimental Investigations of Ethernet Traffic Self-Similar -- Structure -- 4.2.2 Estimation of Testing Results -- 4.3 Self-Similarity of WAN Traffic -- 4.3.1 WAN Traffic at the Application Level -- 4.3.2 Some Limiting Results for Aggregated WAN Traffic -- 4.3.3 The Statistical Analysis of WAN Traffic at the -- Application Level -- 4.3.4 Multifractal Analysis of WAN Traffic -- 4.4 Self-Similarity of Internet Traffic -- 4.4.1 Results of Experimental Studies -- 4.4.2 Stationarity Analysis of IP Traffic -- 4.4.3 Nonstationarity of Internet Traffic -- 4.4.4 Scaling Analysis -- 4.5 Multilevel ON/OFF Model of Internet Traffic -- 4.5.1 Problem Statement -- 4.5.2 Estimation of Parameters and Model Parameterization -- 4.5.3 Parallel Buffer Structure for Active Queue Control -- References -- 5 Queuing and Performance Evaluation of Telecommunication -- Networks under Traffic Self-Similarity Conditions -- 5.1 Traffic Fractality Influence Estimate on Telecommunication -- Networks Queuing -- 5.1.1 Monofractal Traffic -- 5.1.2 Communication System Model and the Packet Loss Probability -- Estimate for the Asymptotic Self-Similar Traffic Described by.
Pareto Distribution -- 5.1.3 Queuing Model with Fractal Levy Motion -- 5.1.4 Estimate of the Effect of Traffic Multifractality Effect on Queuing -- 5.2 Estimate of Voice Traffic Self-Similarity Effects on the iP Networks -- Input Parameter Optimization -- 5.2.1 Problem Statement -- 5.2.2 Simulation Structure -- 5.2.3 Estimate of the Traffic Self-Similarity Influence on QoS -- 5.2.4 TN input Parameter Optimization for Given QoS Characteristics -- 5.2.5 Conclusions -- 5.3 Telecomminication Network Parameters Optimization Using the Tikhonov -- Regularization Approach -- 5.3.1 Problem Statement -- 5.3.2 Telecommunication Network Parameter Optimization on the Basis of -- the Minimization of the Discrepancy Functional of QoS Parameters -- 5.3.3 Optimization Results -- 5.3.4 TN Parameter Optimization on the Basis of Tikhonov -- Functional Minimization -- 5.3.5 Regularization Results -- 5.3.6 Conclusions -- 5.4 Estimation of the Voice Traffic Self-Similarity Influence on QoS -- with Frame Relay Networks -- 5.4.1 Pocket Delay at Transmission through the Frame Relay Network -- 5.4.2 Frame Relay Router Modelling -- 5.4.3 Simulation Results -- 5.5 Bandwidth Prediction in Telecommunication Networks -- 5.6 Congestion Control of Self-Similar Traffic -- 5.6.1 Unimodal Ratio Loading/Productivity -- 5.6.2 Selecting Aggressiveness Control (SAC) Scheme -- References -- Appendix A List of Symbols -- Appendix B List of Acronyms -- Index.
Record Nr. UNINA-9910143701603321
Sheluhin Oleg I.  
Chichester, England ; , : Wiley, , c2007
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Self-similar processes in telecommunications / / Oleg I. Sheluhin, Sergey M. Smolskiy, Andrey V. Osin
Self-similar processes in telecommunications / / Oleg I. Sheluhin, Sergey M. Smolskiy, Andrey V. Osin
Autore Sheluhin Oleg I.
Pubbl/distr/stampa Chichester, England ; , : Wiley, , c2007
Descrizione fisica 1 online resource (336 p.)
Disciplina 621.382
621.382150151922
Altri autori (Persone) SmolskiySergey M
OsinAndrey V
Soggetto topico Telecommunication systems - Mathematical models
Internetworking (Telecommunication)
Self-similar processes
ISBN 1-282-34615-6
9786612346156
0-470-06209-6
0-470-06210-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Foreword -- About the authors -- Acknowledgements -- 1 Principal Concepts of Fractal Theory and Self-Similar Processes -- 1.1 Fractals and Multifractals -- 1.1.1 Fractal Dimension of a Set -- 1.1.2 Multifractals -- 1.1.3 Fractal Dimension D0 and Informational Dimension D1 -- 1.1.4 Legendre Transform -- 1.2 Self-Similar Processes -- 1.2.1 Definitions and Properties of Self-Similar Processes -- 1.2.2 Multifractal Processes -- 1.2.3 Long-Range and Short-Range Dependence -- 1.2.4 Slowly Decaying Variance -- 1.3 'Heavy Tails' -- 1.3.1 Distribution with 'Heavy Tails' (DHT) -- 1.3.2 'Heavy Tails' Estimation -- 1.4 Hurst Exponent Estimation -- 1.4.1 Time Domain Methods of Hurst Exponent Estimation -- 1.4.2 Frequency Domain Methods of Hurst Exponent -- Estimation -- 1.5 Hurst Exponent Estimation Problems -- 1.5.1 Estimation Problems -- 1.5.2 Nonstationarity Problems -- 1.5.3 Computational Problems -- 1.6 Self-Similarity Origins in Telecommunication Traffic -- 1.6.1 User's Behaviour -- 1.6.2 Data Generation Data Structure and Its Search -- 1.6.3 Traffic Aggregation -- 1.6.4 Means of Network Control -- 1.6.5 Control Mechanisms based on Feedback -- 1.6.6 Network Development -- References -- 2 Simulation Methods for Fractal Processes -- 2.1 Fractional Brownian Motion -- 2.1.1 RMD Algorithm for FBM Generation -- 2.1.2 SRA Algorithm for FBM Generation -- 2.2 Fractional Gaussian Noise -- 2.2.1 FFT Algorithm for FGN Synthesis -- 2.2.2 Advantages and Shortcomings of FBM/FGN Models -- in Network Applications -- 2.3 Regression Models of Traffic -- 2.3.1 Linear Autoregressive (AR) Processes -- 2.3.2 Processes of Moving Average (MA) -- 2.3.3 Autoregressive Models of Moving Average, ARMA#p; q -- 2.3.4 Fractional Autoregressive Integrated Moving Average -- (FARIMA) Process -- 2.3.5 Parametric Estimation Methods -- 2.3.6 FARIMA#p,d,q Process Synthesis -- 2.4 Fractal Point Process -- 2.4.1 Statistical Characteristics of the Point Process -- 2.4.2 Fractal Structure of FPP -- 2.4.3 Methods of FPP Formation.
2.5 Fractional Levy Motion and its Application to Network -- Traffic Modelling -- 2.5.1 Fractional Levy Motion and Its Properties -- 2.5.2 Algorithm of Fractional Levy Motion Modelling -- 2.5.3 Fractal Traffic Formation Based on FLM -- 2.6 Models of Multifractal Network Traffic -- 2.6.1 Multiplicative Cascades -- 2.6.2 Modified Estimation Method of Multifractal Functions -- 2.6.3 Generation of Traffic the Multifractal Model -- 2.7 LRD Traffic Modelling with the Help of Wavelets -- 2.8 M/G/1Model -- 2.8.1 M/G/1Model and Pareto Distribution -- 2.8.2 M/G/1Model and Log-Normal Distribution -- References -- 3 Self-Similarity of Real Time Traffic -- 3.1 Self-Similarity of Real Time Traffic Preliminaries -- 3.2 Statistical Characteristics of Telecommunication Real Time Traffic -- 3.2.1 Measurement Organization -- 3.2.2 Pattern of TN Traffic -- 3.3 Voice Traffic Characteristics -- 3.3.1 Voice Traffic Characteristics at the Call Layer -- 3.3.2 Voice Traffic Characteristics at the Packet Layer -- 3.4 Multifractal Analysis of Voice Traffic -- 3.4.1 Basics -- 3.4.2 Algorithm for the Partition Function Sm#q Calculation -- 3.4.3 Multifractal Properties of Multiplexed Voice Traffic -- 3.4.4 Multifractal Properties of Two-Component Voice Traffic -- 3.5 Mathematical Models of VoIP Traffic -- 3.5.1 Problem Statement -- 3.5.2 Voice Traffic Models at the Call Layer -- 3.5.3 Estimation of Semi-Markovian Model Parameters and the Modelling -- Results of the Voice Traffic at the Call Layer -- 3.5.4 Mathematical Models of Voice Traffic at the Packets Layer -- 3.6 Simulation of the Voice Traffic -- 3.6.1 Simulation Structure -- 3.6.2 Parameters Choice of Pareto Distributions for Voice -- Traffic Source in ns2 -- 3.6.3 Results of Separate Sources Modelling -- 3.6.4 Results of Traffic Multiplexing for the Separate -- ON/OFF Sources -- 3.7 Long-Range Dependence for the VBR-Video -- 3.7.1 Distinguished Characteristics of Video Traffic -- 3.7.2 Video Conferences -- 3.7.3 Video Broadcasting -- 3.7.4 MPEG Video Traffic.
3.7.5 Nonstationarity of VBR Video Traffic -- 3.8 Self-Similarity Analysis of Video Traffic -- 3.8.1 Video Broadcasting Wavelet Analysis -- 3.8.2 Numerical Results -- 3.8.3 Multifractal Analysis -- 3.9 Models and Modelling of Video Sequences -- 3.9.1 Nonstationarity Types for VBR Video Traffic -- 3.9.2 Model of the Video Traffic Scene Changing Based on the -- Shifting Level Process -- 3.9.3 Video Traffic Models in the Limits of the Separate Scene -- 3.9.4 Fractal Autoregressive Models of p-Order -- 3.9.5 MPEG Data Modelling Using I, P and B Frames Statistics -- 3.9.6 ON/OFF Model of the Video Sequences -- 3.9.7 Self-Similar Norros Model -- 3.9.8 Hurst Exponent Dependence on N -- References -- 4 Self-Similarity of Telecommunication Networks Traffic -- 4.1 Problem Statement -- 4.2 Self-Similarity and 'Heavy Tails' in Lan Traffic -- 4.2.1 Experimental Investigations of Ethernet Traffic Self-Similar -- Structure -- 4.2.2 Estimation of Testing Results -- 4.3 Self-Similarity of WAN Traffic -- 4.3.1 WAN Traffic at the Application Level -- 4.3.2 Some Limiting Results for Aggregated WAN Traffic -- 4.3.3 The Statistical Analysis of WAN Traffic at the -- Application Level -- 4.3.4 Multifractal Analysis of WAN Traffic -- 4.4 Self-Similarity of Internet Traffic -- 4.4.1 Results of Experimental Studies -- 4.4.2 Stationarity Analysis of IP Traffic -- 4.4.3 Nonstationarity of Internet Traffic -- 4.4.4 Scaling Analysis -- 4.5 Multilevel ON/OFF Model of Internet Traffic -- 4.5.1 Problem Statement -- 4.5.2 Estimation of Parameters and Model Parameterization -- 4.5.3 Parallel Buffer Structure for Active Queue Control -- References -- 5 Queuing and Performance Evaluation of Telecommunication -- Networks under Traffic Self-Similarity Conditions -- 5.1 Traffic Fractality Influence Estimate on Telecommunication -- Networks Queuing -- 5.1.1 Monofractal Traffic -- 5.1.2 Communication System Model and the Packet Loss Probability -- Estimate for the Asymptotic Self-Similar Traffic Described by.
Pareto Distribution -- 5.1.3 Queuing Model with Fractal Levy Motion -- 5.1.4 Estimate of the Effect of Traffic Multifractality Effect on Queuing -- 5.2 Estimate of Voice Traffic Self-Similarity Effects on the iP Networks -- Input Parameter Optimization -- 5.2.1 Problem Statement -- 5.2.2 Simulation Structure -- 5.2.3 Estimate of the Traffic Self-Similarity Influence on QoS -- 5.2.4 TN input Parameter Optimization for Given QoS Characteristics -- 5.2.5 Conclusions -- 5.3 Telecomminication Network Parameters Optimization Using the Tikhonov -- Regularization Approach -- 5.3.1 Problem Statement -- 5.3.2 Telecommunication Network Parameter Optimization on the Basis of -- the Minimization of the Discrepancy Functional of QoS Parameters -- 5.3.3 Optimization Results -- 5.3.4 TN Parameter Optimization on the Basis of Tikhonov -- Functional Minimization -- 5.3.5 Regularization Results -- 5.3.6 Conclusions -- 5.4 Estimation of the Voice Traffic Self-Similarity Influence on QoS -- with Frame Relay Networks -- 5.4.1 Pocket Delay at Transmission through the Frame Relay Network -- 5.4.2 Frame Relay Router Modelling -- 5.4.3 Simulation Results -- 5.5 Bandwidth Prediction in Telecommunication Networks -- 5.6 Congestion Control of Self-Similar Traffic -- 5.6.1 Unimodal Ratio Loading/Productivity -- 5.6.2 Selecting Aggressiveness Control (SAC) Scheme -- References -- Appendix A List of Symbols -- Appendix B List of Acronyms -- Index.
Record Nr. UNINA-9910829875703321
Sheluhin Oleg I.  
Chichester, England ; , : Wiley, , c2007
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Self-similar processes in telecommunications / / Oleg I. Sheluhin, Sergey M. Smolskiy, Andrey V. Osin
Self-similar processes in telecommunications / / Oleg I. Sheluhin, Sergey M. Smolskiy, Andrey V. Osin
Autore Sheluhin O. I (Oleg Ivanovich)
Pubbl/distr/stampa Chichester, England ; ; Hoboken, NJ, : Wiley, c2007
Descrizione fisica 1 online resource (336 p.)
Disciplina 621.382
621.382150151922
Altri autori (Persone) SmolskiySergey M
OsinAndrey V
Soggetto topico Telecommunication systems - Mathematical models
Internetworking (Telecommunication)
Self-similar processes
ISBN 9786612346156
9781282346154
1282346156
9780470062098
0470062096
9780470062104
047006210X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Foreword -- About the authors -- Acknowledgements -- 1 Principal Concepts of Fractal Theory and Self-Similar Processes -- 1.1 Fractals and Multifractals -- 1.1.1 Fractal Dimension of a Set -- 1.1.2 Multifractals -- 1.1.3 Fractal Dimension D0 and Informational Dimension D1 -- 1.1.4 Legendre Transform -- 1.2 Self-Similar Processes -- 1.2.1 Definitions and Properties of Self-Similar Processes -- 1.2.2 Multifractal Processes -- 1.2.3 Long-Range and Short-Range Dependence -- 1.2.4 Slowly Decaying Variance -- 1.3 'Heavy Tails' -- 1.3.1 Distribution with 'Heavy Tails' (DHT) -- 1.3.2 'Heavy Tails' Estimation -- 1.4 Hurst Exponent Estimation -- 1.4.1 Time Domain Methods of Hurst Exponent Estimation -- 1.4.2 Frequency Domain Methods of Hurst Exponent -- Estimation -- 1.5 Hurst Exponent Estimation Problems -- 1.5.1 Estimation Problems -- 1.5.2 Nonstationarity Problems -- 1.5.3 Computational Problems -- 1.6 Self-Similarity Origins in Telecommunication Traffic -- 1.6.1 User's Behaviour -- 1.6.2 Data Generation Data Structure and Its Search -- 1.6.3 Traffic Aggregation -- 1.6.4 Means of Network Control -- 1.6.5 Control Mechanisms based on Feedback -- 1.6.6 Network Development -- References -- 2 Simulation Methods for Fractal Processes -- 2.1 Fractional Brownian Motion -- 2.1.1 RMD Algorithm for FBM Generation -- 2.1.2 SRA Algorithm for FBM Generation -- 2.2 Fractional Gaussian Noise -- 2.2.1 FFT Algorithm for FGN Synthesis -- 2.2.2 Advantages and Shortcomings of FBM/FGN Models -- in Network Applications -- 2.3 Regression Models of Traffic -- 2.3.1 Linear Autoregressive (AR) Processes -- 2.3.2 Processes of Moving Average (MA) -- 2.3.3 Autoregressive Models of Moving Average, ARMA#p; q -- 2.3.4 Fractional Autoregressive Integrated Moving Average -- (FARIMA) Process -- 2.3.5 Parametric Estimation Methods -- 2.3.6 FARIMA#p,d,q Process Synthesis -- 2.4 Fractal Point Process -- 2.4.1 Statistical Characteristics of the Point Process -- 2.4.2 Fractal Structure of FPP -- 2.4.3 Methods of FPP Formation.
2.5 Fractional Levy Motion and its Application to Network -- Traffic Modelling -- 2.5.1 Fractional Levy Motion and Its Properties -- 2.5.2 Algorithm of Fractional Levy Motion Modelling -- 2.5.3 Fractal Traffic Formation Based on FLM -- 2.6 Models of Multifractal Network Traffic -- 2.6.1 Multiplicative Cascades -- 2.6.2 Modified Estimation Method of Multifractal Functions -- 2.6.3 Generation of Traffic the Multifractal Model -- 2.7 LRD Traffic Modelling with the Help of Wavelets -- 2.8 M/G/1Model -- 2.8.1 M/G/1Model and Pareto Distribution -- 2.8.2 M/G/1Model and Log-Normal Distribution -- References -- 3 Self-Similarity of Real Time Traffic -- 3.1 Self-Similarity of Real Time Traffic Preliminaries -- 3.2 Statistical Characteristics of Telecommunication Real Time Traffic -- 3.2.1 Measurement Organization -- 3.2.2 Pattern of TN Traffic -- 3.3 Voice Traffic Characteristics -- 3.3.1 Voice Traffic Characteristics at the Call Layer -- 3.3.2 Voice Traffic Characteristics at the Packet Layer -- 3.4 Multifractal Analysis of Voice Traffic -- 3.4.1 Basics -- 3.4.2 Algorithm for the Partition Function Sm#q Calculation -- 3.4.3 Multifractal Properties of Multiplexed Voice Traffic -- 3.4.4 Multifractal Properties of Two-Component Voice Traffic -- 3.5 Mathematical Models of VoIP Traffic -- 3.5.1 Problem Statement -- 3.5.2 Voice Traffic Models at the Call Layer -- 3.5.3 Estimation of Semi-Markovian Model Parameters and the Modelling -- Results of the Voice Traffic at the Call Layer -- 3.5.4 Mathematical Models of Voice Traffic at the Packets Layer -- 3.6 Simulation of the Voice Traffic -- 3.6.1 Simulation Structure -- 3.6.2 Parameters Choice of Pareto Distributions for Voice -- Traffic Source in ns2 -- 3.6.3 Results of Separate Sources Modelling -- 3.6.4 Results of Traffic Multiplexing for the Separate -- ON/OFF Sources -- 3.7 Long-Range Dependence for the VBR-Video -- 3.7.1 Distinguished Characteristics of Video Traffic -- 3.7.2 Video Conferences -- 3.7.3 Video Broadcasting -- 3.7.4 MPEG Video Traffic.
3.7.5 Nonstationarity of VBR Video Traffic -- 3.8 Self-Similarity Analysis of Video Traffic -- 3.8.1 Video Broadcasting Wavelet Analysis -- 3.8.2 Numerical Results -- 3.8.3 Multifractal Analysis -- 3.9 Models and Modelling of Video Sequences -- 3.9.1 Nonstationarity Types for VBR Video Traffic -- 3.9.2 Model of the Video Traffic Scene Changing Based on the -- Shifting Level Process -- 3.9.3 Video Traffic Models in the Limits of the Separate Scene -- 3.9.4 Fractal Autoregressive Models of p-Order -- 3.9.5 MPEG Data Modelling Using I, P and B Frames Statistics -- 3.9.6 ON/OFF Model of the Video Sequences -- 3.9.7 Self-Similar Norros Model -- 3.9.8 Hurst Exponent Dependence on N -- References -- 4 Self-Similarity of Telecommunication Networks Traffic -- 4.1 Problem Statement -- 4.2 Self-Similarity and 'Heavy Tails' in Lan Traffic -- 4.2.1 Experimental Investigations of Ethernet Traffic Self-Similar -- Structure -- 4.2.2 Estimation of Testing Results -- 4.3 Self-Similarity of WAN Traffic -- 4.3.1 WAN Traffic at the Application Level -- 4.3.2 Some Limiting Results for Aggregated WAN Traffic -- 4.3.3 The Statistical Analysis of WAN Traffic at the -- Application Level -- 4.3.4 Multifractal Analysis of WAN Traffic -- 4.4 Self-Similarity of Internet Traffic -- 4.4.1 Results of Experimental Studies -- 4.4.2 Stationarity Analysis of IP Traffic -- 4.4.3 Nonstationarity of Internet Traffic -- 4.4.4 Scaling Analysis -- 4.5 Multilevel ON/OFF Model of Internet Traffic -- 4.5.1 Problem Statement -- 4.5.2 Estimation of Parameters and Model Parameterization -- 4.5.3 Parallel Buffer Structure for Active Queue Control -- References -- 5 Queuing and Performance Evaluation of Telecommunication -- Networks under Traffic Self-Similarity Conditions -- 5.1 Traffic Fractality Influence Estimate on Telecommunication -- Networks Queuing -- 5.1.1 Monofractal Traffic -- 5.1.2 Communication System Model and the Packet Loss Probability -- Estimate for the Asymptotic Self-Similar Traffic Described by.
Pareto Distribution -- 5.1.3 Queuing Model with Fractal Levy Motion -- 5.1.4 Estimate of the Effect of Traffic Multifractality Effect on Queuing -- 5.2 Estimate of Voice Traffic Self-Similarity Effects on the iP Networks -- Input Parameter Optimization -- 5.2.1 Problem Statement -- 5.2.2 Simulation Structure -- 5.2.3 Estimate of the Traffic Self-Similarity Influence on QoS -- 5.2.4 TN input Parameter Optimization for Given QoS Characteristics -- 5.2.5 Conclusions -- 5.3 Telecomminication Network Parameters Optimization Using the Tikhonov -- Regularization Approach -- 5.3.1 Problem Statement -- 5.3.2 Telecommunication Network Parameter Optimization on the Basis of -- the Minimization of the Discrepancy Functional of QoS Parameters -- 5.3.3 Optimization Results -- 5.3.4 TN Parameter Optimization on the Basis of Tikhonov -- Functional Minimization -- 5.3.5 Regularization Results -- 5.3.6 Conclusions -- 5.4 Estimation of the Voice Traffic Self-Similarity Influence on QoS -- with Frame Relay Networks -- 5.4.1 Pocket Delay at Transmission through the Frame Relay Network -- 5.4.2 Frame Relay Router Modelling -- 5.4.3 Simulation Results -- 5.5 Bandwidth Prediction in Telecommunication Networks -- 5.6 Congestion Control of Self-Similar Traffic -- 5.6.1 Unimodal Ratio Loading/Productivity -- 5.6.2 Selecting Aggressiveness Control (SAC) Scheme -- References -- Appendix A List of Symbols -- Appendix B List of Acronyms -- Index.
Record Nr. UNINA-9911019242903321
Sheluhin O. I (Oleg Ivanovich)  
Chichester, England ; ; Hoboken, NJ, : Wiley, c2007
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui