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Conference Paper: Dynamic pricing and profit maximization for the cloud with geo-distributed data centers

TitleDynamic pricing and profit maximization for the cloud with geo-distributed data centers
Authors
Issue Date2014
PublisherIEEE Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000359
Citation
The 33rd IEEE Conference on Computer Communications (IEEE INFOCOM 2014), Toronto, ON., 27 April-2 May 2014. In IEEE Infocom Proceedings, 2014, p. 118-126 How to Cite?
AbstractCloud providers often choose to operate datacenters over a large geographic span, in order that users may be served by resources in their proximity. Due to time and spatial diversities in utility prices and operational costs, different datacenters typically have disparate charges for the same services. Cloud users are free to choose the datacenters to run their jobs, based on a joint consideration of monetary charges and quality of service. A fundamental problem with significant economic implications is how the cloud should price its datacenter resources at different locations, such that its overall profit is maximized. The challenge escalates when dynamic resource pricing is allowed and long-term profit maximization is pursued. We design an efficient online algorithm for dynamic pricing of VM resources across datacenters in a geo-distributed cloud, together with job scheduling and server provisioning in each datacenter, to maximize the profit of the cloud provider over a long run. Theoretical analysis shows that our algorithm can schedule jobs within their respective deadlines, while achieving a time-average overall profit closely approaching the offline maximum, which is computed by assuming that perfect information on future job arrivals are freely available. Empirical studies further verify the efficacy of our online profit maximizing algorithm. © 2014 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/201091
ISBN
ISSN

 

DC FieldValueLanguage
dc.contributor.authorZhao, Jen_US
dc.contributor.authorLi, Hen_US
dc.contributor.authorWu, Cen_US
dc.contributor.authorLi, Zen_US
dc.contributor.authorZhang, Zen_US
dc.contributor.authorLau, FCMen_US
dc.date.accessioned2014-08-21T07:13:29Z-
dc.date.available2014-08-21T07:13:29Z-
dc.date.issued2014en_US
dc.identifier.citationThe 33rd IEEE Conference on Computer Communications (IEEE INFOCOM 2014), Toronto, ON., 27 April-2 May 2014. In IEEE Infocom Proceedings, 2014, p. 118-126en_US
dc.identifier.isbn978-14799-3360-0-
dc.identifier.issn0743-166X-
dc.identifier.urihttp://hdl.handle.net/10722/201091-
dc.description.abstractCloud providers often choose to operate datacenters over a large geographic span, in order that users may be served by resources in their proximity. Due to time and spatial diversities in utility prices and operational costs, different datacenters typically have disparate charges for the same services. Cloud users are free to choose the datacenters to run their jobs, based on a joint consideration of monetary charges and quality of service. A fundamental problem with significant economic implications is how the cloud should price its datacenter resources at different locations, such that its overall profit is maximized. The challenge escalates when dynamic resource pricing is allowed and long-term profit maximization is pursued. We design an efficient online algorithm for dynamic pricing of VM resources across datacenters in a geo-distributed cloud, together with job scheduling and server provisioning in each datacenter, to maximize the profit of the cloud provider over a long run. Theoretical analysis shows that our algorithm can schedule jobs within their respective deadlines, while achieving a time-average overall profit closely approaching the offline maximum, which is computed by assuming that perfect information on future job arrivals are freely available. Empirical studies further verify the efficacy of our online profit maximizing algorithm. © 2014 IEEE.-
dc.languageengen_US
dc.publisherIEEE Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000359-
dc.relation.ispartofIEEE Infocom Proceedingsen_US
dc.rightsIEEE Infocom. Proceedings. Copyright © IEEE Computer Society.-
dc.rights©2014 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleDynamic pricing and profit maximization for the cloud with geo-distributed data centersen_US
dc.typeConference_Paperen_US
dc.identifier.emailWu, C: cwu@cs.hku.hken_US
dc.identifier.emailLau, FCM: fcmlau@cs.hku.hken_US
dc.identifier.authorityWu, C=rp01397en_US
dc.identifier.authorityLau, FCM=rp00221en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/INFOCOM.2014.6847931-
dc.identifier.scopuseid_2-s2.0-84904424951-
dc.identifier.hkuros232119en_US
dc.identifier.spage118-
dc.identifier.epage126-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 140822-

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