Name
Abstract | ||
|---|---|---|
Execution time prediction is an important issue in cloud computing. Predicting the execution time fast and accurately not only can help users to schedule jobs smarter, but also maximize the throughput and minimize the resource consumption of cloud platform. While hybrid cloud provides methods to federate multiple cloud platforms, different cloud platforms have different resource attributes, which will increase the difficulties to predict a job's execution time. In this paper, we exploit Rough Set Theory (RST), which is a well-known prediction technique that uses the historical data, to predict the execution time of jobs. The evaluation presents that RST can utilize the accuracy of the execution time, while the decision can be made in a short period of time. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/UIC-ATC.2012.41 | UIC/ATC |
Keywords | Field | DocType |
multiple cloud platform,hybrid cloud,different resource attribute,execution time,cloud platform,rough set theory,well-known prediction technique,cloud computing,execution time prediction,different cloud platform,resource consumption,set theory,dynamic scheduling,rough sets | Resource consumption,Set theory,Computer science,Exploit,Real-time computing,Rough set,Execution time,Throughput,Dynamic priority scheduling,Cloud computing,Distributed computing | Conference |
Citations | PageRank | References |
3 | 0.42 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chih-Tien Fan | 1 | 41 | 7.02 |
| Yue-Shan Chang | 2 | 295 | 37.68 |
| Wei-Jen Wang | 3 | 150 | 13.65 |
| Shyan-Ming Yuan | 4 | 634 | 139.65 |