Transient analysis of some rewarded Markov models using randomization with quasistationarity detection

Rewarded homogeneous continuous-time Markov chain (CTMC) models can be used to analyze performance, dependability and performability attributes of computer and telecommunication systems. In this paper, we consider rewarded CTMC models with a reward structure including reward rates associated with st...

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Detalles Bibliográficos
Autor: Carrasco, Juan A.|||0000-0001-7757-1651
Tipo de recurso: artículo
Fecha de publicación:2004
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:2117/19946
Acceso en línea:https://hdl.handle.net/2117/19946
Access Level:acceso abierto
Palabra clave:Fault-tolerant computing
Tolerància als errors (Informàtica)
Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica
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oai_identifier_str oai:upcommons.upc.edu:2117/19946
network_acronym_str ES
network_name_str España
repository_id_str
spelling Transient analysis of some rewarded Markov models using randomization with quasistationarity detectionCarrasco, Juan A.|||0000-0001-7757-1651Fault-tolerant computingTolerància als errors (Informàtica)Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàticaRewarded homogeneous continuous-time Markov chain (CTMC) models can be used to analyze performance, dependability and performability attributes of computer and telecommunication systems. In this paper, we consider rewarded CTMC models with a reward structure including reward rates associated with states and two measures summarizing the behavior in time of the resulting reward rate random variable: the expected transient reward rate at time t and the expected averaged reward rate in the time interval [0, t]. Computation of those measures can be performed using the randomization method, which is numerically stable and has good error control. However, for large stiff models, the method is very expensive. Exploiting the existence of a quasistationary distribution in the subset of transient states of discrete-time Markov chains with a certain structure, we develop a new variant of the (standard) randomization method, randomization with quasistationarity detection, covering finite CTMC models with state space S\cup {f_1, f_2, ..., f_A}, A\geq 1, where all states in S are transient and reachable among them and the states f_i are absorbing. The method has the same good properties as the standard randomization method and can be much more efficient. We also compare the performance of the method with that of regenerative randomization.20042004-09-0120132013-07-12journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/19946reponame: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:2117/199462026-05-27T15:37:01Z
dc.title.none.fl_str_mv Transient analysis of some rewarded Markov models using randomization with quasistationarity detection
title Transient analysis of some rewarded Markov models using randomization with quasistationarity detection
spellingShingle Transient analysis of some rewarded Markov models using randomization with quasistationarity detection
Carrasco, Juan A.|||0000-0001-7757-1651
Fault-tolerant computing
Tolerància als errors (Informàtica)
Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica
title_short Transient analysis of some rewarded Markov models using randomization with quasistationarity detection
title_full Transient analysis of some rewarded Markov models using randomization with quasistationarity detection
title_fullStr Transient analysis of some rewarded Markov models using randomization with quasistationarity detection
title_full_unstemmed Transient analysis of some rewarded Markov models using randomization with quasistationarity detection
title_sort Transient analysis of some rewarded Markov models using randomization with quasistationarity detection
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 Fault-tolerant computing
Tolerància als errors (Informàtica)
Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica
topic Fault-tolerant computing
Tolerància als errors (Informàtica)
Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica
description Rewarded homogeneous continuous-time Markov chain (CTMC) models can be used to analyze performance, dependability and performability attributes of computer and telecommunication systems. In this paper, we consider rewarded CTMC models with a reward structure including reward rates associated with states and two measures summarizing the behavior in time of the resulting reward rate random variable: the expected transient reward rate at time t and the expected averaged reward rate in the time interval [0, t]. Computation of those measures can be performed using the randomization method, which is numerically stable and has good error control. However, for large stiff models, the method is very expensive. Exploiting the existence of a quasistationary distribution in the subset of transient states of discrete-time Markov chains with a certain structure, we develop a new variant of the (standard) randomization method, randomization with quasistationarity detection, covering finite CTMC models with state space S\cup {f_1, f_2, ..., f_A}, A\geq 1, where all states in S are transient and reachable among them and the states f_i are absorbing. The method has the same good properties as the standard randomization method and can be much more efficient. We also compare the performance of the method with that of regenerative randomization.
publishDate 2004
dc.date.none.fl_str_mv 2004
2004-09-01
2013
2013-07-12
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/19946
url https://hdl.handle.net/2117/19946
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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