Abstract—There are various significant issues in resource allocation, such as maximum computing performance and the green computing, attract researchers’ attentions recently. Therefore, how to accomplish tasks with the lowest cost has become an important issue when the resource on the earth is getting less. The goal of this research is to design a sub-optimal resource allocation system in cloud computing environment. A prediction mechanism is realized by using Support Vector Regressions (SVRs) to estimate the response time in the next measurement period, and the resources are redistributed based on the current status of all virtual machine installed in physical machines. Notably, a resource dispatch mechanism using genetic algorithms (GAs) is proposed in this study to determine the reallocation of resources. The experimental results show that the proposed scheme achieves an effective configuration via reaching the agreement between the utilization of resources within physical machine monitored by physical machine monitor and Service Level Agreements (SLA) between virtual machines operator and cloud services provider. In addition, our proposed mechanism can fully utilize hardware resources and maintain desirable performance in the cloud environment.
Index Terms—Cloud computing, support vector regression, genetic algorithms, resource allocation, prediction.
Chenn-Jung Huang, Yu-Wu Wang are with the Department of Computer Science and Information Engineering, National Dong Hwa University, Hualien, Taiwan (e-mail: cjhuang@mail.ndhu.edu.tw).
Chih-Tai Guan, Heng-Ming Chen, Jui-Jiun Jian are with the Department of Electrical Engineering, National Dong Hwa University, Hualien, Taiwan.
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Cite:Chenn-Jung Huang, Yu-Wu Wang, Chih-Tai Guan, Heng-Ming Chen, and Jui-Jiun Jian, "Applications of Machine Learning to Resource Management in Cloud Computing," International Journal of Modeling and Optimization vol. 3, no. 2, pp. 148-152, 2013.
