File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1109/ICC.2018.8422842
- Scopus: eid_2-s2.0-85051440680
Supplementary
-
Citations:
- Scopus: 0
- Appears in Collections:
Conference Paper: Online Cloud Resource Allocation and Pricing with Server Speed Scaling
Title | Online Cloud Resource Allocation and Pricing with Server Speed Scaling |
---|---|
Authors | |
Issue Date | 2018 |
Publisher | IEEE. The Journal's web site is located at https://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000104 |
Citation | IEEE International Conference on Communications (IEEE ICC 2018), Kansas City, MO, USA, 20-24 May 2018 How to Cite? |
Abstract | The provisioning of cloud computing services typically incurs huge electricity costs. Utilization maximization of the cloud resources and efficient resource pricing have been key factors determining a cloud provider's revenue. On the other hand, dynamic CPU speed scaling has been widely supported by modern operating systems and hypervisors as an efficient technique for CPU energy saving, potentially useful for cutting down provider's electricity bill. In this paper, we propose an online mechanism for resource allocation and pricing on a cloud platform, which enables dynamic CPU speed scaling for achieving the best job execution efficiency. Using a novel compact infinite optimization technique and the primal-dual online algorithm design framework, our online mechanism achieves computational efficiency, truthfulness, and near-optimal social welfare during the long run of the cloud system. Trace-driven simulation studies further demonstrate good performance of our mechanism in realistic settings. |
Persistent Identifier | http://hdl.handle.net/10722/259646 |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Luo, Z | - |
dc.contributor.author | Li, Z | - |
dc.contributor.author | Wu, C | - |
dc.date.accessioned | 2018-09-03T04:11:25Z | - |
dc.date.available | 2018-09-03T04:11:25Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | IEEE International Conference on Communications (IEEE ICC 2018), Kansas City, MO, USA, 20-24 May 2018 | - |
dc.identifier.uri | http://hdl.handle.net/10722/259646 | - |
dc.description.abstract | The provisioning of cloud computing services typically incurs huge electricity costs. Utilization maximization of the cloud resources and efficient resource pricing have been key factors determining a cloud provider's revenue. On the other hand, dynamic CPU speed scaling has been widely supported by modern operating systems and hypervisors as an efficient technique for CPU energy saving, potentially useful for cutting down provider's electricity bill. In this paper, we propose an online mechanism for resource allocation and pricing on a cloud platform, which enables dynamic CPU speed scaling for achieving the best job execution efficiency. Using a novel compact infinite optimization technique and the primal-dual online algorithm design framework, our online mechanism achieves computational efficiency, truthfulness, and near-optimal social welfare during the long run of the cloud system. Trace-driven simulation studies further demonstrate good performance of our mechanism in realistic settings. | - |
dc.language | eng | - |
dc.publisher | IEEE. The Journal's web site is located at https://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000104 | - |
dc.relation.ispartof | IEEE International Conference on Communications (ICC) | - |
dc.rights | IEEE International Conference on Communications (ICC). Copyright © IEEE. | - |
dc.title | Online Cloud Resource Allocation and Pricing with Server Speed Scaling | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Wu, C: cwu@cs.hku.hk | - |
dc.identifier.authority | Wu, C=rp01397 | - |
dc.identifier.doi | 10.1109/ICC.2018.8422842 | - |
dc.identifier.scopus | eid_2-s2.0-85051440680 | - |
dc.identifier.hkuros | 288751 | - |
dc.publisher.place | United States | - |