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dc.contributor.authorDuran, Guilem-
dc.contributor.authorValero Cuadra, José-
dc.contributor.authorAMIGO, JOSE M.-
dc.contributor.authorGiménez, Ángel-
dc.contributor.authorMartinez Bonastre, Oscar-
dc.contributor.otherDepartamentos de la UMH::Estadística, Matemáticas e Informáticaes_ES
dc.date.accessioned2024-02-16T11:26:24Z-
dc.date.available2024-02-16T11:26:24Z-
dc.date.created2018-11-08-
dc.identifier.isbn978-1-5386-6043-0-
dc.identifier.urihttps://hdl.handle.net/11000/31441-
dc.description.abstractThe Internet is a so complex nonlinear network that many results show how the data flow exhibits chaotic attributes and the fractal nature of aggregate TCP/IP traffic. In this work, we study a new model of Random Early Detection (RED) using beta distribution configured by tuning decisions of dropping or accepting packets so that the queue occupancy level is kept at a given target level, thereby eliminating aggressive fluctuations of buffer underflow and overflow. Our proposed model programmed with Python incorporates new parameters (a, B) that make it possible to stabilize oscillations of averaged router queue length and to be close to the stationary state. We present study and numerical analysis from the same perspective of former studies for congestion control.es_ES
dc.formatapplication/pdfes_ES
dc.format.extent2es_ES
dc.language.isoenges_ES
dc.publisherIEEEes_ES
dc.relation.ispartofIEEE 26th International Conference on Network Protocols (ICNP), 2018es_ES
dc.rightsinfo:eu-repo/semantics/closedAccesses_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectcongestion controles_ES
dc.subjectAQMes_ES
dc.subjectInternetes_ES
dc.subjectprotocolses_ES
dc.subject.classificationLeguajes y Sistemas Informáticoses_ES
dc.subject.otherCDU::0 - Generalidades.::04 - Ciencia y tecnología de los ordenadores. Informática.es_ES
dc.titleStabilizing Chaotic Behavior of REDes_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.relation.publisherversionhttps://doi.org/10.1109/ICNP.2018.00033es_ES
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