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The Scientific World Journal
Volume 2014, Article ID 208983, 12 pages
http://dx.doi.org/10.1155/2014/208983
Research Article

Prediction Based Proactive Thermal Virtual Machine Scheduling in Green Clouds

1Department of Computer Science and Engineering, SGGS World University, Fatehgarh Sahib, Punjab, India
2School of Mathematics and Computer Applications, Thapar University, Patiala, India

Received 30 August 2013; Accepted 29 December 2013; Published 11 March 2014

Academic Editors: F. Di Martino and D.-L. Yang

Copyright © 2014 Supriya Kinger et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [5 citations]

The following is the list of published articles that have cited the current article.

  • Niharika Verma, and Anju Sharma, “Workload prediction model based on supervised learning for energy efficiency in cloud,” 2017 2nd International Conference on Communication Systems, Computing and IT Applications (CSCITA), pp. 66–71, . View at Publisher · View at Google Scholar
  • R. Jayamala, and A. Valarmathi, “An investigation of scheduling algorithm and their metrics in cloud computing,” 2017 International Conference on Computation of Power, Energy Information and Commuincation (ICCPEIC), pp. 096–101, . View at Publisher · View at Google Scholar
  • Md Whaiduzzaman, Mohammad Nazmul Haque, Md Rejaul Karim Chowdhury, and Abdullah Gani, “A Study on Strategic Provisioning of Cloud Computing Services,” The Scientific World Journal, vol. 2014, pp. 1–16, 2014. View at Publisher · View at Google Scholar
  • Guangjie Han, Wenhui Que, Gangyong Jia, and Lei Shu, “An Efficient Virtual Machine Consolidation Scheme for Multimedia Cloud Computing,” Sensors, vol. 16, no. 2, pp. 246, 2016. View at Publisher · View at Google Scholar
  • Wanlin Gao, Hui Hu, Dongbo Xu, and Ganghong Zhang, “Virtual machine load prediction model for agricultural cloud video platform based on semi-supervised partial least squares,” Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering, vol. 33, pp. 225–230, 2017. View at Publisher · View at Google Scholar