Transient analysis of large Markov models with absorbing states using regenerative randomization

In this paper, we develop a new method, called regenerative randomization, for the transient analysis of continuous time Markov models with absorbing states. The method has the same good properties as standard randomization: numerical stability, well-controlled computation error, and ability to spec...

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Detalles Bibliográficos
Autor: Carrasco, Juan A.|||0000-0001-7757-1651
Tipo de recurso: informe técnico
Fecha de publicación:2005
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/7845
Acceso en línea:https://hdl.handle.net/2117/7845
Access Level:acceso abierto
Palabra clave:Markov processes
Fault-tolerant computing
Markov, Processos de
Tolerància als errors (Informàtica)
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
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spelling Transient analysis of large Markov models with absorbing states using regenerative randomizationCarrasco, Juan A.|||0000-0001-7757-1651Markov processesFault-tolerant computingMarkov, Processos deTolerància als errors (Informàtica)Àrees temàtiques de la UPC::Informàtica::Automàtica i controlIn this paper, we develop a new method, called regenerative randomization, for the transient analysis of continuous time Markov models with absorbing states. The method has the same good properties as standard randomization: numerical stability, well-controlled computation error, and ability to specify the computation error in advance. The method has a benign behavior for large t and is significantly less costly than standard randomization for large enough models and large enough t. For a class of models, class C, including typical failure/repair reliability models with exponential failure and repair time distributions and repair in every state with failed components, stronger theoretical results are available assessing the efficiency of the method in terms of “visible” model characteristics. A large example belonging to that class is used to illustrate the performance of the method and to show that it can indeed be much faster than standard randomization.20052005-04-3020102010-06-25reporthttp://purl.org/coar/resource_type/c_93fcAOhttp://purl.org/coar/version/c_b1a7d7d4d402bcceinfo:eu-repo/semantics/reportapplication/pdfhttps://hdl.handle.net/2117/7845reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/78452026-05-27T15:37:01Z
dc.title.none.fl_str_mv Transient analysis of large Markov models with absorbing states using regenerative randomization
title Transient analysis of large Markov models with absorbing states using regenerative randomization
spellingShingle Transient analysis of large Markov models with absorbing states using regenerative randomization
Carrasco, Juan A.|||0000-0001-7757-1651
Markov processes
Fault-tolerant computing
Markov, Processos de
Tolerància als errors (Informàtica)
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
title_short Transient analysis of large Markov models with absorbing states using regenerative randomization
title_full Transient analysis of large Markov models with absorbing states using regenerative randomization
title_fullStr Transient analysis of large Markov models with absorbing states using regenerative randomization
title_full_unstemmed Transient analysis of large Markov models with absorbing states using regenerative randomization
title_sort Transient analysis of large Markov models with absorbing states using regenerative randomization
dc.creator.none.fl_str_mv Carrasco, Juan A.|||0000-0001-7757-1651
author Carrasco, Juan A.|||0000-0001-7757-1651
author_facet Carrasco, Juan A.|||0000-0001-7757-1651
author_role author
dc.subject.none.fl_str_mv Markov processes
Fault-tolerant computing
Markov, Processos de
Tolerància als errors (Informàtica)
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
topic Markov processes
Fault-tolerant computing
Markov, Processos de
Tolerància als errors (Informàtica)
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
description In this paper, we develop a new method, called regenerative randomization, for the transient analysis of continuous time Markov models with absorbing states. The method has the same good properties as standard randomization: numerical stability, well-controlled computation error, and ability to specify the computation error in advance. The method has a benign behavior for large t and is significantly less costly than standard randomization for large enough models and large enough t. For a class of models, class C, including typical failure/repair reliability models with exponential failure and repair time distributions and repair in every state with failed components, stronger theoretical results are available assessing the efficiency of the method in terms of “visible” model characteristics. A large example belonging to that class is used to illustrate the performance of the method and to show that it can indeed be much faster than standard randomization.
publishDate 2005
dc.date.none.fl_str_mv 2005
2005-04-30
2010
2010-06-25
dc.type.none.fl_str_mv report
http://purl.org/coar/resource_type/c_93fc
AO
http://purl.org/coar/version/c_b1a7d7d4d402bcce
dc.type.openaire.fl_str_mv info:eu-repo/semantics/report
format report
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/7845
url https://hdl.handle.net/2117/7845
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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