Advertisement

Resource Managing Method for Parallel Computing Systems Using Fuzzy Data Preprocessing for Input Tasks Parameters

  • Anastasia Voitsitskaya
  • Alexader Fedulov
  • Yaroslav Fedulov
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 874)

Abstract

In this paper, the method of dispatching and optimal distribution of resources of various types in parallel computing systems is considered, based on preliminary processing of the individual problems parameters, construction of fuzzy evaluation systems and hybrid neural-fuzzy production systems. The application of this method provides advantages in conditions of inaccurate, incomplete and difficult to formalize information about the characteristics of performed tasks, taking into account initially established preferences and achieving the desired performance indicators for the tasks and selected planning strategy.

Keywords

Fuzzy evaluation models Resource management Neuro-fuzzy production models Data preprocessing Parallel computing systems 

References

  1. 1.
    Blaiewicz, J., Drozdowski, M., Markiewicz, M.: Divisible task scheduling – concept and verification. Parallel Comput. 25, 87–98 (1999)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Borisov, V.V., Fedulov, A.S., Fedulov, Y.A.: “Compatible” fuzzy cognitive maps for direct and inverse inference. In: Proceedings of the 18th International Conference on Computer Systems and Technologies, CompSysTech 2017, Ruse, Bulgaria, 23–24 June. ACM International Conference Proceeding Series, vol. 1369 (2017)Google Scholar
  3. 3.
    Fan, G., et al.: A hybrid fuzzy evaluation method for curtain grouting efficiency assessment based on an AHP method extended by D numbers. Expert Syst. Appl. 44, 289–303 (2016)CrossRefGoogle Scholar
  4. 4.
    Golubev, I.A., Smirnov, A.N.: Clustering and classification tasks adaptation to cloud environment. In: IEEE RNW Section Proceedings, vol. 2. IEEE (2011)Google Scholar
  5. 5.
    HTCondor Version 8.0.0 Manual. University of Wisconsin–Madison: Center for High Throughput Computing (2013)Google Scholar
  6. 6.
    Neuman, B., Rao, S.: Resource management for distributed parallel systems. In: Proceedings of 2nd International Symposium on High Performance Distributed Computing (1993)Google Scholar
  7. 7.
    Rauber, T., Runger, G.: Parallel Programming: For Multicore and Cluster Systems. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  8. 8.
    Tang, W., Feng, W.: Parallel map projection of vector-based big spatial data: coupling cloud computing with graphics processing units. Comput. Environ. Urban Syst. 61, 187–197 (2017)CrossRefGoogle Scholar
  9. 9.
    Torque v.4.2.4 Administrator Guide. Adaptive Computing Enterprises (2013)Google Scholar
  10. 10.
    Yuan, Z.-W., Wang, Y.-H.: Research on K nearest neighbor non-parametric regression algorithm based on KD-tree and clustering analysis. In: Proceedings of the 2012 Fourth International Conference on Computational and Information Sciences, ICCIS 2012. IEEE Computer Society, Washington, DC (2012)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Anastasia Voitsitskaya
    • 1
  • Alexader Fedulov
    • 2
  • Yaroslav Fedulov
    • 2
  1. 1.National Research University “Moscow Power Engineering Institute”MoscowRussia
  2. 2.Smolensk Branch of National Research University “Moscow Power Engineering Institute”SmolenskRussia

Personalised recommendations