DocumentCode
1823470
Title
Execution Time Prediction Using Rough Set Theory in Hybrid Cloud
Author
Fan, Chih-Tien ; Chang, Yue-Shan ; Wang, Wei-Jen ; Yuan, Shyan-Ming
Author_Institution
Dept. of Comp. Sci., Nat. Chiao Tung Univ., Hsinchu, Taiwan
fYear
2012
fDate
4-7 Sept. 2012
Firstpage
729
Lastpage
734
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.
Keywords
cloud computing; rough set theory; RST; cloud computing; cloud platform; execution time prediction; hybrid cloud; resource consumption; rough set theory; Approximation methods; Cloud computing; Dynamic scheduling; Educational institutions; Error analysis; Processor scheduling; Set theory; Execution Time Prediction; History Based Approach; Hybrid Cloud; Private Cloud; Public Cloud; Rough Set Theory; Rough Sets;
fLanguage
English
Publisher
ieee
Conference_Titel
Ubiquitous Intelligence & Computing and 9th International Conference on Autonomic & Trusted Computing (UIC/ATC), 2012 9th International Conference on
Conference_Location
Fukuoka
Print_ISBN
978-1-4673-3084-8
Type
conf
DOI
10.1109/UIC-ATC.2012.41
Filename
6332074
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