Grid Resource Scheduling Method Based on BP Neural Network

  • Min Li
  • Zhenhua Li
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 316)


The grid mesh is a high performance calculation of the main direction, the influence of the grid function and performance of the main factors for the efficiency of the grid resources scheduling, because of the complexity of the grid, the resource management compared with the traditional distributed network more complicated, so efficient grid resources scheduling algorithm is grid research hot spot and the difficulty. This paper puts forward a layered resource scheduling model and a simple structure function complete resources scheduling method, and put forward feedback of the BP neural network algorithm applied to the grid resources scheduling of better solve the grid resources scheduling problem.


Grid Resource Scheduling BP 


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  1. 1.
    Hu, Z., Hu, Z., Li, L.: Grid Task Scheduling Algorithm Using Resource Performance Evaluation. Journal of System Simulation, 3542–3548 (November 12, 2009)Google Scholar
  2. 2.
    Zhong, S.: Optimization models and algorithms based on dynamic load balancing strategy for grid task scheduling. Computer Applications, 28(11) (2008)Google Scholar
  3. 3.
    Liu, H., Hao, W., Gao, Q.: Distributed deployment of grid services scheduling algorithm and QoS Performance Analysis. Journal of Computer Science (November 6, 2011)Google Scholar
  4. 4.
    Li, X., Sun, Z.: Selective disposition of grid resources. Journal of Computer Science (November 4, 2010)Google Scholar
  5. 5.
    Gao, Z.: Grid task scheduling, quality of service issues related to research. Beijing Jiaotong University (2010)Google Scholar
  6. 6.
    Niu, D., Shi, H., Li, J., Xu, C.: Research on power load forecasting based on combined model of Markov and BP neural network. In: 2010 8th World Congress on Intelligent Control and Automation (WCICA 2010), pp. 4372–4375 (2010)Google Scholar
  7. 7.
    Minh, T., Le, N., Cao, J.: Flexible and Semantics-Based Support for Web Services Transaction Protocols. In: Wu, S., Yang, L.T., Xu, T.L. (eds.) GPC 2008. LNCS, vol. 5036, pp. 492–503. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  8. 8.
    Nan, X., Yu, J., You, Z., Li, Q.: Wind speed forecasting based on combination forecasting model. In: 2010 International Conference of Information Science and Management Engineering (ISME 2010), vol. 2, pp. 185–189 (2010)Google Scholar
  9. 9.
    Zhang, M.-G., Li, L.-R.: Short-term load combined forecasting method based on BPNN and LS-SVM. In: 2011 IEEE Power Engineering and Automation Conference, vol. 1, pp. 319–322 (2011)Google Scholar
  10. 10.
    Lv, G.: Grid resource scheduling based on BP algorithm. Harbin Polytechnic University (March 2007)Google Scholar
  11. 11.
    Li, Z., Xie, L.: Grid architecture and its development. Computer Engineering (July 2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Min Li
    • 1
    • 2
  • Zhenhua Li
    • 2
  1. 1.Jingchu University of TechnologyJingmenChina
  2. 2.China University of GeosciencesWuhanChina

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