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dc.contributor.authorAnselmi, J.
dc.contributor.authorCasale, G.
dc.date.accessioned2017-02-21T08:11:15Z
dc.date.available2017-02-21T08:11:15Z
dc.date.issued2013-12-31
dc.identifier.issn0166-5316
dc.identifier.urihttp://hdl.handle.net/20.500.11824/393
dc.description.abstractMotivated 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.
dc.formatapplication/pdf
dc.language.isoengen_US
dc.rightsReconocimiento-NoComercial-CompartirIgual 3.0 Españaen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/en_US
dc.subjectHeavy-traffic approximations
dc.subjectMulticlass closed queueing networks
dc.subjectRevenue maximization
dc.titleHeavy-traffic revenue maximization in parallel multiclass queues
dc.typeinfo:eu-repo/semantics/articleen_US
dc.identifier.doi10.1016/j.peva.2013.08.008
dc.relation.publisherversionhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84884819089&doi=10.1016%2fj.peva.2013.08.008&partnerID=40&md5=50008efb935f89e7f5660309a73d2aec
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen_US
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionen_US
dc.journal.titlePerformance Evaluationen_US


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Reconocimiento-NoComercial-CompartirIgual 3.0 España
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