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Conference Paper: Federated Private Clouds via Broker’s Marketplace: A Stackelberg-Game Perspective

TitleFederated Private Clouds via Broker’s Marketplace: A Stackelberg-Game Perspective
Authors
Issue Date2014
PublisherI E E E Computer Society.
Citation
The IEEE 7th International Conference on Cloud Computing (CLOUD), Anchorage, Alaska, USA, 27 June-2 July 2014. In the Proceedings of the IEEE International Conference on Cloud Computing (CLOUD), 2014, p. 296-303 How to Cite?
AbstractMore and more enterprises have set up their own private clouds by applying virtualization to their data centers, the benefit is flexible resource supply to different internal demands. Aiming to meet the peak demand in their resource provisioning, private clouds are often under-utilized. A new paradigm has emerged that advocates leasing the spare resources to external users, when and if adequate rental prices are offered. A broker is typically employed which pools the spare resources of multiple private clouds together and leases them to serve external users' jobs. Good mechanisms have yet to be derived for the broker to set the offered prices to buy spare resources from the private clouds, and to schedule jobs on the available resources, such that the economic benefits of both the broker and the private clouds are maximized. The design of the mechanism is especially challenging when we consider the dynamic arrival of users' jobs and volatile availability of spare resources at the private clouds, while aiming at long-term profit optimality. In this paper, we model the interaction between the broker and the private clouds as a two-stage Stackelberg game. As the leader in the game, the broker decides and offers prices for renting VMs of different types from each private cloud. As a follower, each private cloud responds with the number of VMs of each type that it is willing to lease. By combining with the Stackelberg game model we design online algorithms for the broker to set the prices and schedule jobs on the private clouds, and for the private cloud to decide the numbers of VMs to lease, based on the Lyapunov optimization theory. We prove that the broker achieves a time-averaged profit that is close to the offline optimum with complete information on future job arrivals and resource availability, while each private cloud makes their best earning. The proposed online algorithm is carefully evaluated based on usage traces of Google cluster and Amazon EC2.
Persistent Identifierhttp://hdl.handle.net/10722/201099
ISBN

 

DC FieldValueLanguage
dc.contributor.authorQiu, Xen_US
dc.contributor.authorWu, Cen_US
dc.contributor.authorLi, Hen_US
dc.contributor.authorLi, Zen_US
dc.contributor.authorLau, FCMen_US
dc.date.accessioned2014-08-21T07:13:33Z-
dc.date.available2014-08-21T07:13:33Z-
dc.date.issued2014en_US
dc.identifier.citationThe IEEE 7th International Conference on Cloud Computing (CLOUD), Anchorage, Alaska, USA, 27 June-2 July 2014. In the Proceedings of the IEEE International Conference on Cloud Computing (CLOUD), 2014, p. 296-303en_US
dc.identifier.isbn9781479950621-
dc.identifier.urihttp://hdl.handle.net/10722/201099-
dc.description.abstractMore and more enterprises have set up their own private clouds by applying virtualization to their data centers, the benefit is flexible resource supply to different internal demands. Aiming to meet the peak demand in their resource provisioning, private clouds are often under-utilized. A new paradigm has emerged that advocates leasing the spare resources to external users, when and if adequate rental prices are offered. A broker is typically employed which pools the spare resources of multiple private clouds together and leases them to serve external users' jobs. Good mechanisms have yet to be derived for the broker to set the offered prices to buy spare resources from the private clouds, and to schedule jobs on the available resources, such that the economic benefits of both the broker and the private clouds are maximized. The design of the mechanism is especially challenging when we consider the dynamic arrival of users' jobs and volatile availability of spare resources at the private clouds, while aiming at long-term profit optimality. In this paper, we model the interaction between the broker and the private clouds as a two-stage Stackelberg game. As the leader in the game, the broker decides and offers prices for renting VMs of different types from each private cloud. As a follower, each private cloud responds with the number of VMs of each type that it is willing to lease. By combining with the Stackelberg game model we design online algorithms for the broker to set the prices and schedule jobs on the private clouds, and for the private cloud to decide the numbers of VMs to lease, based on the Lyapunov optimization theory. We prove that the broker achieves a time-averaged profit that is close to the offline optimum with complete information on future job arrivals and resource availability, while each private cloud makes their best earning. The proposed online algorithm is carefully evaluated based on usage traces of Google cluster and Amazon EC2.-
dc.languageengen_US
dc.publisherI E E E Computer Society.-
dc.relation.ispartofIEEE International Conference on Cloud Computing (CLOUD)en_US
dc.rightsIEEE International Conference on Cloud Computing (CLOUD). Copyright © I E E E 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.titleFederated Private Clouds via Broker’s Marketplace: A Stackelberg-Game Perspectiveen_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/CLOUD.2014.48-
dc.identifier.hkuros232128en_US
dc.identifier.spage296-
dc.identifier.epage303-
dc.publisher.placeUnited States-

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