Beyond the mean in fork-join queues: efficient approximation for response-time tails

Fork-join queues are natural models for various computer and communications systems that involve parallel multitasking and the splitting and resynchronizing of data, such as parallel computing, query processing in distributed databases, and parallel disk access. Job response time in a fork-join queu...

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Fecha de publicación:
2015
Institución:
Universidad del Rosario
Repositorio:
Repositorio EdocUR - U. Rosario
Idioma:
eng
OAI Identifier:
oai:repository.urosario.edu.co:10336/27286
Acceso en línea:
https://doi.org/10.1016/j.peva.2015.06.007
https://repository.urosario.edu.co/handle/10336/27286
Palabra clave:
Fork-join queues
Response-time distribution
Matrix-analytic methods
Order statistics
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spelling e1d48e1f-f195-4e4b-9c1b-f62c730d04e5800352026006549d3b2-d9df-440b-acfa-5833e4b4b3232020-08-19T14:41:37Z2020-08-19T14:41:37Z2015-09Fork-join queues are natural models for various computer and communications systems that involve parallel multitasking and the splitting and resynchronizing of data, such as parallel computing, query processing in distributed databases, and parallel disk access. Job response time in a fork-join queue is a critical performance indicator but its exact analysis is challenging. We introduce a stochastic model for -node homogeneous fork-join queues () that focuses on the difference in length between any node-queue and the shortest one, truncating the state space such that the maximum difference is at most a constant . Whilst most previous methods focus on the mean response time, our model is also able to evaluate the response time distribution, as well as accommodating phase-type processing times and Markovian arrival processes. In order to tackle scenarios with high loads, which require a large value of to provide sufficient accuracy, we develop an efficient algorithm using matrix-analytic methods. Tests against simulation show that the proposed model yields accurate results for 2-node fork-join queues. As the model becomes numerically intractable for large values of , we further propose an approximate approach, based on properties of order statistics and extreme values. The approximation gives a high degree of accuracy on response time tails, and has the advantage of being efficient and scalable, requiring only the analytical results for a single-node and 2-node fork-join queues, which we obtain with the aforementioned matrix-analytic model. Comparison with simulation results shows that our approximation yields good fits for the tails, even in very large cases with general processing and inter-arrival times.application/pdfhttps://doi.org/10.1016/j.peva.2015.06.007ISSN: 0166-5316EISSN: 1872-745Xhttps://repository.urosario.edu.co/handle/10336/27286engElsevier11699Performance EvaluationVol. 91Performance Evaluation, ISSN: 0166-5316;EISSN: 1872-745X, Vol.91 (2015); pp. 99-116https://www.sciencedirect.com/science/article/pii/S0166531615000553Abierto (Texto Completo)http://purl.org/coar/access_right/c_abf2Performance Evaluationinstname:Universidad del Rosarioreponame:Repositorio Institucional EdocURFork-join queuesResponse-time distributionMatrix-analytic methodsOrder statisticsBeyond the mean in fork-join queues: efficient approximation for response-time tailsMás allá de la media en colas de unión en bifurcación: aproximación eficiente para colas de tiempo de respuestaarticleArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_6501Qiu, ZhanPérez, Juan F.Harrison, Peter G.ORIGINAL1-s2-0-S0166531615000553-main.pdfapplication/pdf727562https://repository.urosario.edu.co/bitstreams/f0ecc22e-8ecf-407d-a731-87f1c8a169c6/downloade5f509279e9418bfa3c15bd3c6821350MD51TEXT1-s2-0-S0166531615000553-main.pdf.txt1-s2-0-S0166531615000553-main.pdf.txtExtracted texttext/plain79460https://repository.urosario.edu.co/bitstreams/4aed4bf9-e91d-4d8a-9bb0-5411387f7c76/downloadcc043cc08fc6a517edcdc8cc2b01c670MD52THUMBNAIL1-s2-0-S0166531615000553-main.pdf.jpg1-s2-0-S0166531615000553-main.pdf.jpgGenerated Thumbnailimage/jpeg4199https://repository.urosario.edu.co/bitstreams/1d698ae3-9b1c-4a4c-8cb9-0ed14927c126/download7342fc1d7f6ac8cb17219f1c70c812beMD5310336/27286oai:repository.urosario.edu.co:10336/272862021-09-23 12:34:54.929https://repository.urosario.edu.coRepositorio institucional EdocURedocur@urosario.edu.co
dc.title.spa.fl_str_mv Beyond the mean in fork-join queues: efficient approximation for response-time tails
dc.title.TranslatedTitle.spa.fl_str_mv Más allá de la media en colas de unión en bifurcación: aproximación eficiente para colas de tiempo de respuesta
title Beyond the mean in fork-join queues: efficient approximation for response-time tails
spellingShingle Beyond the mean in fork-join queues: efficient approximation for response-time tails
Fork-join queues
Response-time distribution
Matrix-analytic methods
Order statistics
title_short Beyond the mean in fork-join queues: efficient approximation for response-time tails
title_full Beyond the mean in fork-join queues: efficient approximation for response-time tails
title_fullStr Beyond the mean in fork-join queues: efficient approximation for response-time tails
title_full_unstemmed Beyond the mean in fork-join queues: efficient approximation for response-time tails
title_sort Beyond the mean in fork-join queues: efficient approximation for response-time tails
dc.subject.keyword.spa.fl_str_mv Fork-join queues
Response-time distribution
Matrix-analytic methods
Order statistics
topic Fork-join queues
Response-time distribution
Matrix-analytic methods
Order statistics
description Fork-join queues are natural models for various computer and communications systems that involve parallel multitasking and the splitting and resynchronizing of data, such as parallel computing, query processing in distributed databases, and parallel disk access. Job response time in a fork-join queue is a critical performance indicator but its exact analysis is challenging. We introduce a stochastic model for -node homogeneous fork-join queues () that focuses on the difference in length between any node-queue and the shortest one, truncating the state space such that the maximum difference is at most a constant . Whilst most previous methods focus on the mean response time, our model is also able to evaluate the response time distribution, as well as accommodating phase-type processing times and Markovian arrival processes. In order to tackle scenarios with high loads, which require a large value of to provide sufficient accuracy, we develop an efficient algorithm using matrix-analytic methods. Tests against simulation show that the proposed model yields accurate results for 2-node fork-join queues. As the model becomes numerically intractable for large values of , we further propose an approximate approach, based on properties of order statistics and extreme values. The approximation gives a high degree of accuracy on response time tails, and has the advantage of being efficient and scalable, requiring only the analytical results for a single-node and 2-node fork-join queues, which we obtain with the aforementioned matrix-analytic model. Comparison with simulation results shows that our approximation yields good fits for the tails, even in very large cases with general processing and inter-arrival times.
publishDate 2015
dc.date.created.spa.fl_str_mv 2015-09
dc.date.accessioned.none.fl_str_mv 2020-08-19T14:41:37Z
dc.date.available.none.fl_str_mv 2020-08-19T14:41:37Z
dc.type.eng.fl_str_mv article
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dc.type.spa.spa.fl_str_mv Artículo
dc.identifier.doi.none.fl_str_mv https://doi.org/10.1016/j.peva.2015.06.007
dc.identifier.issn.none.fl_str_mv ISSN: 0166-5316
EISSN: 1872-745X
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url https://doi.org/10.1016/j.peva.2015.06.007
https://repository.urosario.edu.co/handle/10336/27286
identifier_str_mv ISSN: 0166-5316
EISSN: 1872-745X
dc.language.iso.spa.fl_str_mv eng
language eng
dc.relation.citationEndPage.none.fl_str_mv 116
dc.relation.citationStartPage.none.fl_str_mv 99
dc.relation.citationTitle.none.fl_str_mv Performance Evaluation
dc.relation.citationVolume.none.fl_str_mv Vol. 91
dc.relation.ispartof.spa.fl_str_mv Performance Evaluation, ISSN: 0166-5316;EISSN: 1872-745X, Vol.91 (2015); pp. 99-116
dc.relation.uri.spa.fl_str_mv https://www.sciencedirect.com/science/article/pii/S0166531615000553
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dc.publisher.spa.fl_str_mv Elsevier
dc.source.spa.fl_str_mv Performance Evaluation
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