Heavy-traffic revenue maximization in parallel multiclass queues

Motivated by revenue maximization in server farms with admission control, we investigate the optimal scheduling in parallel processor-sharing queues. Incoming customers are distinguished in multiple classes and we define revenue as a weighted sum of class throughputs. Under these assumptions, we des...

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Detalles Bibliográficos
Autores: Anselmi, J., Casale, G.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2013
País:España
Institución:Basque Center for Applied Mathematics (BCAM)
Repositorio:BIRD. BCAM's Institutional Repository Data
OAI Identifier:oai:bird.bcamath.org:20.500.11824/393
Acceso en línea:http://hdl.handle.net/20.500.11824/393
Access Level:acceso abierto
Palabra clave:Heavy-traffic approximations
Multiclass closed queueing networks
Revenue maximization
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spelling Heavy-traffic revenue maximization in parallel multiclass queuesAnselmi, J.Casale, G.Heavy-traffic approximationsMulticlass closed queueing networksRevenue maximizationMotivated by revenue maximization in server farms with admission control, we investigate the optimal scheduling in parallel processor-sharing queues. Incoming customers are distinguished in multiple classes and we define revenue as a weighted sum of class throughputs. Under these assumptions, we describe a heavy-traffic limit for the revenue maximization problem and study the asymptotic properties of the optimization model as the number of clients increases. Our main result is a simple heuristic that is able to provide tight guarantees on the optimality gap of its solutions. In the general case with M queues and R classes, we prove that our heuristic is (1+1M-1)-competitive in heavy-traffic. Experimental results indicate that the proposed heuristic is remarkably accurate, despite its negligible computational costs, both in random instances and using service rates of a web application measured on multiple cloud deployments.201720172013info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/20.500.11824/393reponame:BIRD. BCAM's Institutional Repository Datainstname:Basque Center for Applied Mathematics (BCAM)Ingléshttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84884819089&doi=10.1016%2fj.peva.2013.08.008&partnerID=40&md5=50008efb935f89e7f5660309a73d2aecReconocimiento-NoComercial-CompartirIgual 3.0 Españahttp://creativecommons.org/licenses/by-nc-sa/3.0/es/info:eu-repo/semantics/openAccessoai:bird.bcamath.org:20.500.11824/3932026-06-19T12:47:47Z
dc.title.none.fl_str_mv Heavy-traffic revenue maximization in parallel multiclass queues
title Heavy-traffic revenue maximization in parallel multiclass queues
spellingShingle Heavy-traffic revenue maximization in parallel multiclass queues
Anselmi, J.
Heavy-traffic approximations
Multiclass closed queueing networks
Revenue maximization
title_short Heavy-traffic revenue maximization in parallel multiclass queues
title_full Heavy-traffic revenue maximization in parallel multiclass queues
title_fullStr Heavy-traffic revenue maximization in parallel multiclass queues
title_full_unstemmed Heavy-traffic revenue maximization in parallel multiclass queues
title_sort Heavy-traffic revenue maximization in parallel multiclass queues
dc.creator.none.fl_str_mv Anselmi, J.
Casale, G.
author Anselmi, J.
author_facet Anselmi, J.
Casale, G.
author_role author
author2 Casale, G.
author2_role author
dc.subject.none.fl_str_mv Heavy-traffic approximations
Multiclass closed queueing networks
Revenue maximization
topic Heavy-traffic approximations
Multiclass closed queueing networks
Revenue maximization
description Motivated by revenue maximization in server farms with admission control, we investigate the optimal scheduling in parallel processor-sharing queues. Incoming customers are distinguished in multiple classes and we define revenue as a weighted sum of class throughputs. Under these assumptions, we describe a heavy-traffic limit for the revenue maximization problem and study the asymptotic properties of the optimization model as the number of clients increases. Our main result is a simple heuristic that is able to provide tight guarantees on the optimality gap of its solutions. In the general case with M queues and R classes, we prove that our heuristic is (1+1M-1)-competitive in heavy-traffic. Experimental results indicate that the proposed heuristic is remarkably accurate, despite its negligible computational costs, both in random instances and using service rates of a web application measured on multiple cloud deployments.
publishDate 2013
dc.date.none.fl_str_mv 2013
2017
2017
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url http://hdl.handle.net/20.500.11824/393
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
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dc.rights.none.fl_str_mv Reconocimiento-NoComercial-CompartirIgual 3.0 España
http://creativecommons.org/licenses/by-nc-sa/3.0/es/
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