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The Scientific World Journal
Volume 2014 (2014), Article ID 321231, 12 pages
http://dx.doi.org/10.1155/2014/321231
Research Article

Efficient Resources Provisioning Based on Load Forecasting in Cloud

1School of Computer, National University of Defense Technology, Changsha 410073, China
2National Supercomputer Center, Tianjin 300457, China

Received 4 November 2013; Accepted 19 December 2013; Published 20 February 2014

Academic Editors: J. Comellas, J.-X. Du, and S.-S. Liaw

Copyright © 2014 Rongdong Hu 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 [9 citations]

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

  • Masoud Barati, and Saeed Sharifian, “A hybrid heuristic-based tuned support vector regression model for cloud load prediction,” Journal Of Supercomputing, vol. 71, no. 11, pp. 4235–4259, 2015. View at Publisher · View at Google Scholar
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  • Rongdong Hu, Guangming Liu, Jingfei Jiang, and Lixin Wang, “A New Resources Provisioning Method Based on QoS Differentiation and VM Resizing in IaaS,” Mathematical Problems in Engineering, vol. 2015, pp. 1–9, 2015. View at Publisher · View at Google Scholar
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  • Jitendra Kumar, and Ashutosh Kumar Singh, “Workload prediction in cloud using artificial neural network and adaptive differential evolution,” Future Generation Computer Systems, vol. 81, pp. 41–52, 2018. View at Publisher · View at Google Scholar
  • Hang Wei, Guan-Yu Hu, Xiaoxia Han, Peili Qiao, Zhiguo Zhou, Zhi-Chao Feng, and Xiao-Jing Yin, “A New BRB Model for Cloud Security-State Prediction Based on the Large-Scale Monitoring Data,” IEEE Access, vol. 6, pp. 11907–11920, 2018. View at Publisher · View at Google Scholar