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Mathematical Problems in Engineering
Volume 2014 (2014), Article ID 141930, 9 pages
http://dx.doi.org/10.1155/2014/141930
Research Article

Research on Virtual Machine Response Time Prediction Method Based on GA-BP Neural Network

College of Information Science & Technology Engineering, Northeastern University, Shenyang, China

Received 17 March 2014; Revised 18 May 2014; Accepted 1 June 2014; Published 17 June 2014

Academic Editor: Qingsong Xu

Copyright © 2014 Jun Guo 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.

Linked References

  1. M. Armbrust, A. Fox, R. Griffith et al., “A view of cloud computing,” Communications of the ACM, vol. 53, no. 4, pp. 50–58, 2010. View at Publisher · View at Google Scholar · View at Scopus
  2. M. Alhamad, T. Dillon, and E. Chang, “Conceptual SLA framework for cloud computing,” in Proceedings of the 4th IEEE International Conference on Digital Ecosystems and Technologies (DEST '10), pp. 606–610, Dubai, United Arab Emirates, April 2010. View at Publisher · View at Google Scholar · View at Scopus
  3. G. Box, G. Jenkins, and G. Reinsel, Time Series Analysis, Holden-day, San Francisco, Calif, USA, 1970.
  4. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” Nature, vol. 323, no. 6088, pp. 533–536, 1986. View at Publisher · View at Google Scholar · View at Scopus
  5. X. Zheng, J. Zhao, and Z. Cheng, “Web service response time dynamic prediction approach,” Journal of Chinese Computer System, no. 8, pp. 1570–1574, 2011. View at Google Scholar
  6. Y.-J. Chiang and Y.-C. Ouyang, “Profit optimization in SLA-aware cloud service with a finite capacity queuing model,” Mathematical Problems in Engineering, vol. 2014, Article ID 534510, 11 pages, 2014. View at Publisher · View at Google Scholar
  7. N. U. Bhar, An Introduction to Queuing Theory Model and Analysis in Application, Birkhauser, Boston, Mass, USA, 2007.
  8. K. Xiong and H. Perros, “Service performance and analysis in cloud computing,” in Proceedings of the IEEE World Congress on Services, pp. 693–700, Los Angeles, Calif, USA, July 2009. View at Publisher · View at Google Scholar · View at Scopus
  9. J. Yi, Q. Wang, D. Zhao, and J. T. Wen, “BP neural network prediction-based variable-period sampling approach for networked control systems,” Applied Mathematics and Computation, vol. 185, no. 2, pp. 976–988, 2007. View at Publisher · View at Google Scholar · View at Scopus
  10. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182–197, 2002. View at Publisher · View at Google Scholar · View at Scopus
  11. H. R. Nielson, Neurocomputing, Addison Wesley Publishing Company, Boston, Mass, USA, 1991.
  12. J. S. Judd, Neural Network Design & the Complexity of Learning, California Instruction of Technology, Cambrige, UK, 1988.
  13. N. Yamashita and M. Fukushima, “On the rate of convergence of the levenberg-marquardt method,” Computing, vol. 15, pp. 239–249, 2001. View at Google Scholar
  14. C. Z. Janikow and Z. Michalewicz, “An experimental comparisons of binary and floating point representations in genetic algorithms,” in Proceeding of the 9th Conference on Genertic Algorithm, pp. 31–36, 1991.
  15. Z. Michalewicz, C. Z. Janikow, and J. B. Krawczyk, “A modified genetic algorithm for optimal control problems,” Computers and Mathematics with Applications, vol. 23, no. 12, pp. 83–94, 1992. View at Google Scholar · View at Scopus