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
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;
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
DOI :
10.1109/UIC-ATC.2012.41