File Download
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1109/INFOCOM.2014.6847931
- Scopus: eid_2-s2.0-84904424951
- Find via
Supplementary
-
Citations:
- Scopus: 0
- Appears in Collections:
Conference Paper: Dynamic pricing and profit maximization for the cloud with geo-distributed data centers
Title | Dynamic pricing and profit maximization for the cloud with geo-distributed data centers |
---|---|
Authors | |
Issue Date | 2014 |
Publisher | IEEE 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? |
Abstract | Cloud 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 Identifier | http://hdl.handle.net/10722/201091 |
ISBN | |
ISSN | 2023 SCImago Journal Rankings: 2.865 |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Zhao, J | en_US |
dc.contributor.author | Li, H | en_US |
dc.contributor.author | Wu, C | en_US |
dc.contributor.author | Li, Z | en_US |
dc.contributor.author | Zhang, Z | en_US |
dc.contributor.author | Lau, FCM | en_US |
dc.date.accessioned | 2014-08-21T07:13:29Z | - |
dc.date.available | 2014-08-21T07:13:29Z | - |
dc.date.issued | 2014 | en_US |
dc.identifier.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 | en_US |
dc.identifier.isbn | 978-14799-3360-0 | - |
dc.identifier.issn | 0743-166X | - |
dc.identifier.uri | http://hdl.handle.net/10722/201091 | - |
dc.description.abstract | Cloud 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.language | eng | en_US |
dc.publisher | IEEE Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000359 | - |
dc.relation.ispartof | IEEE Infocom Proceedings | en_US |
dc.title | Dynamic pricing and profit maximization for the cloud with geo-distributed data centers | en_US |
dc.type | Conference_Paper | en_US |
dc.identifier.email | Wu, C: cwu@cs.hku.hk | en_US |
dc.identifier.email | Lau, FCM: fcmlau@cs.hku.hk | en_US |
dc.identifier.authority | Wu, C=rp01397 | en_US |
dc.identifier.authority | Lau, FCM=rp00221 | en_US |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/INFOCOM.2014.6847931 | - |
dc.identifier.scopus | eid_2-s2.0-84904424951 | - |
dc.identifier.hkuros | 232119 | en_US |
dc.identifier.spage | 118 | - |
dc.identifier.epage | 126 | - |
dc.publisher.place | United States | - |
dc.customcontrol.immutable | sml 140822 | - |
dc.identifier.issnl | 0743-166X | - |