Characterization of class-based traffic flows in multiservice IP networks

2003 ◽  
Author(s):  
I. Atov ◽  
R.J. Harris
Keyword(s):  
Ip Networks ◽  
Traffic Flows

Representative Time Series Fractal Analysis of the Traffic Flows of a Dedicated High-Speed Link

2021 ◽  
Author(s):  
Ginno Millan ◽  
manuel vargas ◽  
Guillermo Fuertes
Keyword(s):  
Time Series ◽  
Traffic Flow ◽  
Long Range ◽  
Computer Networks ◽  
Fractal Analysis ◽  
High Speed ◽  
Long Range Dependence ◽  
Traffic Flows ◽  
Speed Computer

Fractal behavior and long-range dependence are widely observed in measurements and characterization of traffic flow in high-speed computer networks of different technologies and coverage levels. This paper presents the results obtained when applying fractal analysis techniques on a time series obtained from traffic captures coming from an application server connected to the internet through a high-speed link. The results obtained show that traffic flow in the dedicated high-speed network link exhibited fractal behavior since the Hurst exponent was in the range of 0.5, 1, the fractal dimension between 1, 1.5, and the correlation coefficient between -0.5, 0. Based on these results, it is ideal to characterize both the singularities of the fractal traffic and its impulsiveness during a fractal analysis of temporal scales. Finally, based on the results of the time series analyzes, the fact that the traffic flows of current computer networks exhibited fractal behavior with a long-range dependence was reaffirmed.

scholarly journals Modeling and Characterization of Traffic Flows in Urban Environments

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2020 ◽  
Author(s):  
Jorge Zambrano-Martinez ◽  
Carlos Calafate ◽  
David Soler ◽  
Juan-Carlos Cano ◽  
Pietro Manzoni
Keyword(s):  
Urban Environments ◽  
Traffic Flows

scholarly journals Systematic performance modeling and characterization of heterogeneous IP networks

2006 ◽  
Vol 72 (7) ◽  
pp. 1134-1143 ◽  
Author(s):  
Alessio Botta ◽  
Donato Emma ◽  
Antonio Pescapé ◽  
Giorgio Ventre
Keyword(s):  
Performance Modeling ◽  
Ip Networks

Trajectory Clustering and Classification for Characterization of Air Traffic Flows

2016 ◽  
Author(s):  
Mayara Conde Rocha Murca ◽  
Richard DeLaura ◽  
R John Hansman ◽  
Richard Jordan ◽  
Tom Reynolds ◽  
...  
Keyword(s):  
Air Traffic ◽  
Traffic Flows ◽  
Trajectory Clustering ◽  
Clustering And Classification

MLP Modeling and Prediction of IP Subnet Packets Forwarding Performance

2019 ◽  
Vol 18 (01) ◽  
pp. 1950006 ◽  
Author(s):  
Yaming Zhang ◽  
Yaya Hamadou Koura ◽  
Yanyuan Su
Keyword(s):  
Gradient Descent ◽  
Ip Networks ◽  
Resources Allocation ◽  
Traffic Flows ◽  
Traffic Shaping ◽  
Feed Forward Neural Network ◽  
Modeling And Prediction ◽  
Sigmoid Functions ◽  
Density Of Neurons ◽  
Hidden Nodes

In IP networks, packets forwarding performance can be improved by adding more nodes and dividing the network into smaller segments. Being able to measure and predict traffic flows to direct to a given segment can be crucial in respecting traffic shaping, scheduling and QoS. This paper proposes to model network packets forwarding performance for optimization and prediction purposes by using multi-layer feed-forward neural network model that uses sigmoid functions to activate the hidden nodes. Gradient descent technique has been considered to optimize and enhance the MLP accuracy. Simulations of MPL neurons training stages pointed out a relative improvement of the forwarding process when network posses a larger density of neurons. Numerical results validated our theoretical analysis and confirmed that to enhance the forwarding process, it is necessary to divide the network into small segments by optimizing resources allocation.

Characterization of the busy-hour traffic of IP networks based on their intrinsic features

2011 ◽  
Vol 55 (9) ◽  
pp. 2111-2125 ◽  
Author(s):  
José Luis García-Dorado ◽  
José Alberto Hernández ◽  
Javier Aracil ◽  
Jorge E. López de Vergara ◽  
Sergio Lopez-Buedo
Keyword(s):  
Ip Networks
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