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Advanced Models and Algorithms for Self–Similar IP Network Traffic Simulation and Performance Analysis

dc.contributor.authorRadev, Dimitar
dc.contributor.authorLokshina, Izabella V.
dc.date.accessioned2022-04-01T19:18:53Z
dc.date.available2022-04-01T19:18:53Z
dc.date.issued2010
dc.identifier.citationRadev, D., and Lokshina, I. (2010). Advanced Models and Algorithms for Self-Similar Network Traffic Simulation and Performance Analysis. Journal of Electrical Engineering (JEE)/the Institute of Electrical and Electronics Engineers (IEEE); online: Wersita, Warsaw - Central European Science Publishers, 61(6), p. 341-349en_US
dc.identifier.doi10.2478/v10187-010-0053-0
dc.identifier.urihttp://hdl.handle.net/20.500.12648/7145
dc.description.abstractThe 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.en_US
dc.language.isoen_USen_US
dc.publisherFEI STU Bratislavaen_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectCommunication networksen_US
dc.subjectIP network trafficen_US
dc.subjectLong-range dependent self-similar processesen_US
dc.subjectAdvanced generators of self-similar teletrafficen_US
dc.titleAdvanced Models and Algorithms for Self–Similar IP Network Traffic Simulation and Performance Analysisen_US
dc.typeArticle/Reviewen_US
dc.source.journaltitleJournal of Electrical Engineeringen_US
dc.description.versionVoRen_US
refterms.dateFOA2022-04-01T19:18:54Z
dc.description.institutionSUNY Oneontaen_US
dc.description.departmentBusinessen_US
dc.description.degreelevelN/Aen_US


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Attribution-NonCommercial-NoDerivatives 4.0 International
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International