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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Sheng Di Cho-Li Wang |
| Copyright Year | 2013 |
| Description | Author affiliation: INRIA, Sophia-Antipolis, France (Sheng Di) || Univ. of Hong Kong, Hong Kong, China (Cho-Li Wang) |
| 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. |
| Sponsorship | IEEE Comput. Soc. |
| Starting Page | 69 |
| Ending Page | 78 |
| File Size | 997183 |
| Page Count | 10 |
| File Format | |
| ISBN | 9781479907304 |
| DOI | 10.1109/HiPC.2013.6799101 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-12-18 |
| Publisher Place | India |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Vectors Prediction algorithms Resource management Equations Upper bound Mathematical model Convex functions |
| Content Type | Text |
| Resource Type | Article |
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