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Internet congestion control: From stochastic to dynamical models


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Título :
Internet congestion control: From stochastic to dynamical models
Autor :
Giménez, Angel
AMIGO, JOSE M.  
Martinez Bonastre, Oscar  
Valero, José
Editor :
World Scientific Publishing
Departamento:
Departamentos de la UMH::Estadística, Matemáticas e Informática
Fecha de publicación:
2021
URI :
https://hdl.handle.net/11000/30982
Resumen :
Since its inception, control of data congestion on the Internet has been based on stochas tic models. One of the first such models was Random Early Detection. Later, this model was reformulated as a dynamical system, with the average queue sizes at a router’s buffer being the states. Recently, the dynamical model has been generalized to improve global stability. In this paper we review the original stochastic model and both nonlin ear models of Random Early Detection with a two-fold objective: (i) illustrate how a random model can be “smoothed out” to a deterministic one through data aggregation and (ii) how this translation can shed light into complex processes such as the Internet data traffic. Furthermore, this paper contains new materials concerning the occurrence of chaos, bifurcation diagrams, Lyapunov exponents and global stability robustness with respect to control parameters. The results reviewed and reported here are expected to help design an active queue management algorithm in real conditions, that is, when sys tem parameters such as the number of users and the round-trip time of the data packets change over time. The topic also illustrates the much-needed synergy of a theoretical approach, practical intuition and numerical simulations in engineering
Palabras clave/Materias:
Internet congestion control
adaptive queue management
random early detection
discrete-time dynamical systems
global stability
robust setting of control parameters
Área de conocimiento :
CDU: Ciencias puras y naturales
Tipo documento :
application/pdf
Derechos de acceso:
info:eu-repo/semantics/closedAccess
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
DOI :
https://doi.org/10.1142/S0219493721400098
Aparece en las colecciones:
Artículos Estadística, Matemáticas e Informática



Creative Commons La licencia se describe como: Atribución-NonComercial-NoDerivada 4.0 Internacional.