scholarly journals Advanced Models and Algorithms for Self-Similar IP Network Traffic Simulation and Performance Analysis

2010 ◽  
Vol 61 (6) ◽  
pp. 341-349 ◽  
Author(s):  
Dimitar Radev ◽  
Izabella Lokshina
Keyword(s):  
Performance Analysis ◽  
Network Traffic ◽  
Traffic Simulation ◽  
Traffic Data ◽  
Fractal Models ◽  
Wide Range ◽  
Ip Network ◽  
Self Similar ◽  
Markov Modulated ◽  
And Performance

Advanced Models and Algorithms for Self-Similar IP Network Traffic Simulation and Performance Analysis The paper examines self-similar (or fractal) properties of real communication network traffic data over a wide range of time scales. These self-similar properties are very different from the properties of traditional models based on Poisson and Markov-modulated Poisson processes. Advanced fractal models of sequentional generators and fixed-length sequence generators, and efficient algorithms that are used to simulate self-similar behavior of IP network traffic data are developed and applied. Numerical examples are provided; and simulation results are obtained and analyzed.

Modeling and Simulation of Self-Similar Traffic in Wireless IP Networks

2011 ◽  
pp. 249-265
Author(s):  
Dimitar Radev ◽  
Izabella Lokshina ◽  
Svetla Radeva
Keyword(s):  
Network Traffic ◽  
Traffic Load ◽  
Single Server ◽  
Finite Buffer ◽  
Wide Range ◽  
Telecommunications Network ◽  
Source Models ◽  
Wireless Ip ◽  
Self Similar ◽  
Markov Modulated

The paper examines self-similar properties of real telecommunications network traffic data over a wide range of time scales. These self-similar properties are very different from the properties of traditional models based on Poisson and Markov-modulated Poisson processes. Simulation with stochastic and long range dependent traffic source models is performed, and the algorithms for buffer overflow simulation for finite buffer single server model under self-similar traffic load SSM/M/1/B are explained. The algorithms for modeling fixed-length sequence generators that are used to simulate self-similar behavior of wireless IP network traffic are developed and applied. Numerical examples are provided, and simulation results are analyzed.

Modeling and Simulation of Self-Similar Traffic in Wireless IP Networks

2010 ◽  
pp. 1631-1647
Author(s):  
Dimitar Radev ◽  
Izabella Lokshina ◽  
Svetla Radeva
Keyword(s):  
Network Traffic ◽  
Traffic Load ◽  
Single Server ◽  
Finite Buffer ◽  
Wide Range ◽  
Telecommunications Network ◽  
Source Models ◽  
Wireless Ip ◽  
Self Similar ◽  
Markov Modulated

The article examines self-similar properties of real telecommunications network traffic data over a wide range of time scales. These self-similar properties are very different from the properties of traditional models based on Poisson and Markov-modulated Poisson processes. Simulation with stochastic and long range dependent traffic source models is performed, and the algorithms for buffer overflow simulation for finite buffer single server model under self-similar traffic load SSM/M/1/B are explained. The algorithms for modeling fixedlength sequence generators that are used to simulate self-similar behavior of wireless IP network traffic are developed and applied. Numerical examples are provided, and simulation results are analyzed.

Modeling and Simulation of Self-Similar Traffic in Wireless IP Networks

2009 ◽  
Vol 1 (4) ◽  
pp. 71-87
Author(s):  
Dimitar Radev ◽  
Izabella Lokshina ◽  
Svetla Radeva
Keyword(s):  
Network Traffic ◽  
Traffic Load ◽  
Single Server ◽  
Finite Buffer ◽  
Wide Range ◽  
Telecommunications Network ◽  
Source Models ◽  
Wireless Ip ◽  
Self Similar ◽  
Markov Modulated

The article examines self-similar properties of real telecommunications network traffic data over a wide range of time scales. These self-similar properties are very different from the properties of traditional models based on Poisson and Markov-modulated Poisson processes. Simulation with stochastic and long range dependent traffic source models is performed, and the algorithms for buffer overflow simulation for finite buffer single server model under self-similar traffic load SSM/M/1/B are explained. The algorithms for modeling fixedlength sequence generators that are used to simulate self-similar behavior of wireless IP network traffic are developed and applied. Numerical examples are provided, and simulation results are analyzed.

Research on the Traffic Simulation Platform Based on the Real-Time Mobile Phone Data

2012 ◽  
Vol 253-255 ◽  
pp. 1365-1368
Author(s):  
Ge Qi Qi ◽  
Jian Ping Wu ◽  
Yi Man Du
Keyword(s):  
Mobile Phone ◽  
Real Time ◽  
Traffic Simulation ◽  
Rapid Development ◽  
Mobile Phone Data ◽  
Simulation Platform ◽  
Traffic Data ◽  
The Real ◽  
Real Time Traffic ◽  
Wide Range

With the rapid development of the society, the transportation system has become more complicated and vulnerable. For simulating the real-time traffic condition of the whole city, a wide range of OD matrix data are needed which are hard to collect in whole based on the present conventional methods. The paper raises a feasible design of the traffic simulation platform based on the real-time mobile phone data. The popularity and development of mobile phones make the vast amounts of real-time traffic data can be collected and usable. With the help of the GIS module, dynamic OD traffic generation module and other related modules, the real-time mobile phone data will be converted to the valuable traffic data and applied to the traffic simulation platform.

scholarly journals Fractal-based techniques for physiological time series: An updated approach

Open Physics ◽  
2018 ◽  
Vol 16 (1) ◽  
pp. 741-750 ◽  
Author(s):  
José Luis Roca ◽  
German Rodríguez-Bermúdez ◽  
Manuel Fernández-Martínez
Keyword(s):  
Time Series ◽  
State Of The Art ◽  
Self Similarity ◽  
High Quality ◽  
Detailed Review ◽  
Physiological Time ◽  
Wide Range ◽  
Fractal Patterns ◽  
Self Similar ◽  
And Performance

AbstractAlong this paper, we shall update the state-of-the-art concerning the application of fractal-based techniques to test for fractal patterns in physiological time series. As such, the first half of the present work deals with some selected approaches to deal with the calculation of the self-similarity exponent of time series. They include broadly-used procedures as well as recent advances improving their accuracy and performance for a wide range of self-similar processes. The second part of this paper consists of a detailed review of high-quality studies carried out in the context of electroencephalogram signals. Both medical and non-medical applications have been deeply reviewed. This work is especially recommended to all those researchers especially interested in fractal pattern recognition for physiological time series.

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