Advertisement

Research on Meteorology Indices Forecasting Framework based on Hybrid Cloud Computing Platforms

  • Jia Fu
  • Junchao Wang
  • Lu Jing
  • Chen Zhenghong
  • Mingqiong He
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 214)

Abstract

With the development of cloud computing, the whole internet computing has entered an era of high-performance computing and application. Cloud computing makes it possible for resources to be transacted as goods, and for anyone at any time and any place to make use of the mighty power of cloud computing. As is known to all, the meteorological science involves large amount of data and high demand of calculation. In order to improve the accuracy and timeliness of meteorology indices forecasting computing in this paper, employing cloud computing technology in the high performance computing, a meteorological science data computing and process control framework based on hybrid clouds is put forward. And with the practical application, the usability, feasibility and expansibility of this framework has been proved. A large number of evaluation data has demonstrated that this framework can provide higher computational efficiency and prediction products with higher resolution time.

Keywords

Cloud computing Hybrid cloud Meteorology indices forecasting High-performance computing 

References

  1. 1.
    Yong, Z., Raicu, I., Lu, S.: Cloud computing and grid computing 360-Degree compared. In: Grid Computing Environments Workshop, 2008. GCE ‘08, pp. 1–10Google Scholar
  2. 2.
    Qi, Z., Lu, C., Raouf B.: Cloud computing: state-of-the-art and research challenges. J. Int. Serv. Appl. 1(1), 7–18 (2010)Google Scholar
  3. 3.
    Feng, D.-G., Zhang, M., Zhang, Y., Xu, Z.: Study on cloud computing security. J. Softw. 3, 71–83 (2011)Google Scholar
  4. 4.
    Ostermann, S., Yigitbasi, M.N., Prodan, R., Fahringer, T., Epema, D.H.J.,: Performance analysis of cloud computing services for many-tasks scientific computing. IEEE Trans. Parallel Distrib. Syst. 22(6), 931–945 (2011)Google Scholar
  5. 5.
    Ji, W.: Agaric—A hybrid cloud based testing platform. Loud and Service Computing (CSC), 2011 International Conference, vol. 1, no. 1, pp. 87–94Google Scholar
  6. 6.
    Gargate, L.: Expansion of a plasma cloud into the solar wind. Plasma Sci. IEEE Trans. 36(4), 1168–1169 (2008)CrossRefGoogle Scholar
  7. 7.
    Erbes, J.: The future of enterprise IT in the cloud. Computer. 45(5), 66–72 (2012)CrossRefGoogle Scholar
  8. 8.
    Chakravarty, S.: Forecasting stock market indices using hybrid network. Nat. Biol. Inspired Comput. 1(1), 1225–1230 (2009)Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Jia Fu
    • 1
  • Junchao Wang
    • 2
  • Lu Jing
    • 1
  • Chen Zhenghong
    • 1
  • Mingqiong He
    • 1
  1. 1.Meteorological Service Center of Hubei ProvinceWuhanPeople Republic of China
  2. 2.Chinese Meteorology AdministrationWuhan Research Institute of RainstormWuhanPeople Republic of China

Personalised recommendations