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- Publisher Website: 10.1109/HiPC.2013.6799101
- Scopus: eid_2-s2.0-84900334346
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Conference Paper: Minimization of cloud task execution length with workload prediction errors
Title | Minimization of cloud task execution length with workload prediction errors |
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Authors | |
Issue Date | 2013 |
Publisher | IEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000322 |
Citation | The 20th International Conference on High Performance Computing (HiPC 2013), Bengaluru (Bangalore), India, 18-21 December 2013. In Conference Proceedings, 2013, p. 1-10 How to Cite? |
Abstract | In cloud systems, it is non-trivial to optimize task’s execution performance under user’s affordable budget, especially with possible workload prediction errors. Based on an optimal algorithm that can minimize cloud task’s execution length with predicted workload and budget, we theoretically derive the upper bound of the task execution length by taking into account the possible workload prediction errors. With such a state-of-the-art bound, the worst-case performance of a task execution with a certain workload prediction errors is predictable. On the other hand, we build a close-to-practice cloud prototype over a real cluster environment deployed with 56 virtual machines, and evaluate our solution with different resource contention degrees. Experiments show that task execution lengths under our solution with estimates of worst-case performance are close to their theoretical ideal values, in both non-competitive situation with adequate resources and the competitive situation with a certain limited available resources. We also observe a fair treatment on the resource allocation among all tasks. |
Persistent Identifier | http://hdl.handle.net/10722/191546 |
DC Field | Value | Language |
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dc.contributor.author | Di, S | en_US |
dc.contributor.author | Kondo, D | en_US |
dc.contributor.author | Wang, CL | en_US |
dc.date.accessioned | 2013-10-15T07:10:16Z | - |
dc.date.available | 2013-10-15T07:10:16Z | - |
dc.date.issued | 2013 | en_US |
dc.identifier.citation | The 20th International Conference on High Performance Computing (HiPC 2013), Bengaluru (Bangalore), India, 18-21 December 2013. In Conference Proceedings, 2013, p. 1-10 | en_US |
dc.identifier.uri | http://hdl.handle.net/10722/191546 | - |
dc.description.abstract | In cloud systems, it is non-trivial to optimize task’s execution performance under user’s affordable budget, especially with possible workload prediction errors. Based on an optimal algorithm that can minimize cloud task’s execution length with predicted workload and budget, we theoretically derive the upper bound of the task execution length by taking into account the possible workload prediction errors. With such a state-of-the-art bound, the worst-case performance of a task execution with a certain workload prediction errors is predictable. On the other hand, we build a close-to-practice cloud prototype over a real cluster environment deployed with 56 virtual machines, and evaluate our solution with different resource contention degrees. Experiments show that task execution lengths under our solution with estimates of worst-case performance are close to their theoretical ideal values, in both non-competitive situation with adequate resources and the competitive situation with a certain limited available resources. We also observe a fair treatment on the resource allocation among all tasks. | - |
dc.language | eng | en_US |
dc.publisher | IEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000322 | - |
dc.relation.ispartof | International Conference on High Performance Computing Proceedings | en_US |
dc.title | Minimization of cloud task execution length with workload prediction errors | en_US |
dc.type | Conference_Paper | en_US |
dc.identifier.email | Di, S: sdi@cs.hku.hk | en_US |
dc.identifier.email | Wang, CL: clwang@cs.hku.hk | - |
dc.identifier.authority | Wang, CL=rp00183 | en_US |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/HiPC.2013.6799101 | - |
dc.identifier.scopus | eid_2-s2.0-84900334346 | - |
dc.identifier.hkuros | 225319 | en_US |
dc.identifier.spage | 1 | - |
dc.identifier.epage | 10 | - |
dc.publisher.place | United States | - |
dc.customcontrol.immutable | sml 140217 | - |