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Algorithm 972: JMarkov: An integrated framework for Markov chain modeling

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dc.creatorPérez, Juan F.spa
dc.creatorSilva D.F.spa
dc.creatorGóez J.C.spa
dc.creatorRiaño G.spa
dc.creatorSarmiento A.spa
dc.creatorSarmiento-Romero A.spa
dc.creatorAkhavan-Tabatabaei R.spa
dc.date.accessioned2020-05-25T23:59:51Z
dc.date.available2020-05-25T23:59:51Z
dc.date.created2017spa
dc.description.abstractMarkov chains (MC) are a powerful tool for modeling complex stochastic systems. Whereas a number of tools exist for solving different types ofMCmodels, the first step inMCmodeling is to define themodel parameters. This step is, however, error prone and far from trivial when modeling complex systems. In this article, we introduce jMarkov, a framework for MC modeling that provides the user with the ability to define MC models from the basic rules underlying the system dynamics. From these rules, jMarkov automatically obtains the MC parameters and solves the model to determine steady-state and transient performance measures. The jMarkov framework is composed of four modules: (i) the main module supports MC models with a finite state space; (ii) the jQBD module enables the modeling of Quasi-Birth-and-Death processes, a class of MCs with infinite state space; (iii) the jMDP module offers the capabilities to determine optimal decision rules based on Markov Decision Processes; and (iv) the jPhase module supports the manipulation and inclusion of phase-type variables to representmore general behaviors than that of the standard exponential distribution. In addition, jMarkov is highly extensible, allowing the users to introduce new modeling abstractions and solvers. © 2017 ACM.eng
dc.format.mimetypeapplication/pdf
dc.identifier.doihttps://doi.org/10.1145/3009968
dc.identifier.issn983500
dc.identifier.urihttps://repository.urosario.edu.co/handle/10336/23121
dc.language.isoengspa
dc.publisherAssociation for Computing Machineryspa
dc.relation.citationIssueNo. 3
dc.relation.citationTitleACM Transactions on Mathematical Software
dc.relation.citationVolumeVol. 43
dc.relation.ispartofACM Transactions on Mathematical Software, ISSN:983500, Vol.43, No.3 (2017)spa
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85011339690&doi=10.1145%2f3009968&partnerID=40&md5=64fd83b593cb72483141e59f1e1ba7dcspa
dc.rights.accesRightsinfo:eu-repo/semantics/openAccess
dc.rights.accesoAbierto (Texto Completo)spa
dc.source.instnameinstname:Universidad del Rosariospa
dc.source.reponamereponame:Repositorio Institucional EdocURspa
dc.subject.keywordChainsspa
dc.subject.keywordQueueing theoryspa
dc.subject.keywordStochastic modelsspa
dc.subject.keywordStochastic systemsspa
dc.subject.keywordExponential distributionsspa
dc.subject.keywordInfinite state spacespa
dc.subject.keywordIntegrated frameworksspa
dc.subject.keywordMarkov Decision Processesspa
dc.subject.keywordOptimal decision-rulespa
dc.subject.keywordPhase type distributionsspa
dc.subject.keywordQuasi-birth and death processspa
dc.subject.keywordSteady state and transientsspa
dc.subject.keywordMarkov processesspa
dc.subject.keywordMarkov chainsspa
dc.subject.keywordMarkov decision processesspa
dc.subject.keywordPhase-type distributionsspa
dc.subject.keywordQuasi-birth-and-death processesspa
dc.subject.keywordStochastic modelingspa
dc.titleAlgorithm 972: JMarkov: An integrated framework for Markov chain modelingspa
dc.typearticleeng
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersion
dc.type.spaArtículospa
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