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Conference Paper: Profit-maximizing virtual machine trading in a federation of selfish clouds

TitleProfit-maximizing virtual machine trading in a federation of selfish clouds
Authors
KeywordsAuction mechanisms
Dynamic algorithm
Dynamic resource trading
Electricity prices
Long-term profits
Rigorous analysis
Temporal availability
Virtual machines
Issue Date2013
PublisherIEEE Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000359
Citation
The 32nd IEEE Conference on Computer Communications (IEEE INFOCOM 2013), Turin, Italy, 14-19 April 2013. In IEEE Infocom Proceedings, 2013, p. 25-29 How to Cite?
AbstractThe emerging federated cloud paradigm advocates sharing of resources among cloud providers, to exploit temporal availability of resources and diversity of operational costs for job serving. While extensive studies exist on enabling interoperability across different cloud platforms, a fundamental question on cloud economics remains unanswered: When and how should a cloud trade VMs with others, such that its net profit is maximized over the long run? In order to answer this question by the federation, a number of important, correlated decisions, including job scheduling, server provisioning and resource pricing, need to be dynamically made, with long-term profit optimality being a goal. In this work, we design efficient algorithms for inter-cloud resource trading and scheduling in a federation of geo-distributed clouds. For VM trading among clouds, we apply a double auction-based mechanism that is strategyproof, individual rational, and ex-post budget balanced. Coupling with the auction mechanism is an efficient, dynamic resource trading and scheduling algorithm, which carefully decides the true valuations of VMs in the auction, optimally schedules stochastic job arrivals with different SLAs onto the VMs, and judiciously turns on and off servers based on the current electricity prices. Through rigorous analysis, we show that each individual cloud, by carrying out our dynamic algorithm, can achieve a time-averaged profit arbitrarily close to the offline optimum. © 2013 IEEE.
DescriptionMini-Conference - IEEE INFOCOM 2013
Persistent Identifierhttp://hdl.handle.net/10722/186479
ISBN
ISSN

 

DC FieldValueLanguage
dc.contributor.authorLi, Hen_US
dc.contributor.authorWu, Cen_US
dc.contributor.authorLi, Zen_US
dc.contributor.authorLau, FCMen_US
dc.date.accessioned2013-08-20T12:11:09Z-
dc.date.available2013-08-20T12:11:09Z-
dc.date.issued2013en_US
dc.identifier.citationThe 32nd IEEE Conference on Computer Communications (IEEE INFOCOM 2013), Turin, Italy, 14-19 April 2013. In IEEE Infocom Proceedings, 2013, p. 25-29en_US
dc.identifier.isbn978-1-4673-5946-7-
dc.identifier.issn0743-166X-
dc.identifier.urihttp://hdl.handle.net/10722/186479-
dc.descriptionMini-Conference - IEEE INFOCOM 2013-
dc.description.abstractThe emerging federated cloud paradigm advocates sharing of resources among cloud providers, to exploit temporal availability of resources and diversity of operational costs for job serving. While extensive studies exist on enabling interoperability across different cloud platforms, a fundamental question on cloud economics remains unanswered: When and how should a cloud trade VMs with others, such that its net profit is maximized over the long run? In order to answer this question by the federation, a number of important, correlated decisions, including job scheduling, server provisioning and resource pricing, need to be dynamically made, with long-term profit optimality being a goal. In this work, we design efficient algorithms for inter-cloud resource trading and scheduling in a federation of geo-distributed clouds. For VM trading among clouds, we apply a double auction-based mechanism that is strategyproof, individual rational, and ex-post budget balanced. Coupling with the auction mechanism is an efficient, dynamic resource trading and scheduling algorithm, which carefully decides the true valuations of VMs in the auction, optimally schedules stochastic job arrivals with different SLAs onto the VMs, and judiciously turns on and off servers based on the current electricity prices. Through rigorous analysis, we show that each individual cloud, by carrying out our dynamic algorithm, can achieve a time-averaged profit arbitrarily close to the offline optimum. © 2013 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©20xx 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.subjectAuction mechanisms-
dc.subjectDynamic algorithm-
dc.subjectDynamic resource trading-
dc.subjectElectricity prices-
dc.subjectLong-term profits-
dc.subjectRigorous analysis-
dc.subjectTemporal availability-
dc.subjectVirtual machines-
dc.titleProfit-maximizing virtual machine trading in a federation of selfish cloudsen_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/INFCOM.2013.6566728-
dc.identifier.scopuseid_2-s2.0-84883112358-
dc.identifier.hkuros217643en_US
dc.identifier.spage25-
dc.identifier.epage29-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 140822-

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