Cloud Task and Virtual Machine Allocation Strategy Based on Simulated Annealing-Genetic Algorithm

Xing Xu; Na Hu; Wei Qin Ying

doi:10.4028/www.scientific.net/AMM.513-517.391

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 513-517Cloud Task and Virtual Machine Allocation Strategy...

Cloud Task and Virtual Machine Allocation Strategy Based on Simulated Annealing-Genetic Algorithm

Abstract:

In order to solve and optimize the task and virtual machine allocation strategy in cloud computing environment, firstly allocation strategy mathematical model, the target of which is the total running time, is established, then a simulated annealing and genetic hybrid algorithm is proposed to solve the mathematical model. The integer coding, a crossover operator, two kinds of mutation operators and the selection mechanism based on simulated annealing strategy are applied in the hybrid algorithm. In the experiments, three sets of data are used to verify the performance of the hybrid algorithm in the Cloudsim software. And the experimental results show that the series of cloud tasks can effectively be assigned to the virtual machine by the hybrid algorithm and the total running time is also minimized by the algorithm.

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Periodical:

Applied Mechanics and Materials (Volumes 513-517)

Pages:

391-394

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.513-517.391

Citation:

Cite this paper

Online since:

February 2014

Authors:

Xing Xu*, Na Hu, Wei Qin Ying

Keywords:

Cloud Computing, Genetic Algorithm (GA), Hybrid Algorithm, Simulated Annealing (SA)

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* - Corresponding Author

References

[1] Rajkumar Buyya, Christian Vecchiola, S. Thamarai Selvi. Mastering Cloud Computing: Foundations and Applications Programming [M]. Morgan Kaufmann, (2013).

DOI: 10.1016/b978-0-12-411454-8.00008-5

Google Scholar

[2] Wanjun Liu, Menghua Zhang, Wenyue Guo. Cloud Computing Resource Schedule Strategy Based on MPSO Algorithm [J]. Computer Engineering, 2011 37(11) 43-44, 48.

Google Scholar

[3] Jianhua Gu, Jinhua Hu, Tianhai Zhao, et al. A New Resource Scheduling Strategy Based on Genetic Algorithm in Cloud Computing Environment [J]. Journal of Computers, 2012 7(1) 42-52.

DOI: 10.1109/paap.2010.65

Google Scholar

[4] Dawei Sun, Guiran Chang, Fengyun Li, et al. Optimizing Multi-Dimensional QoS Cloud Resource Scheduling by Immune Clonal with Preference [J]. Acta Electronica Sinica, 2011 39(8) 1824-1831.

Google Scholar

[5] Bingxiang Liu, Xing Xu, Hao Hu. Task scheduling and virtual machine allocation strategy based on thermodynamics evolutionary algorithm in cloud computing environment [J]. Science Tecknology and Engineering, 2013 13(15) 290-294.

Google Scholar

[6] Xu Liang, Ming Huang. Modern intelligent optimization hybrid algorithm and its application [M]. Beijing: Publishing House of Electronics Industry, (2011).

Google Scholar