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The Analytical Model for Distributed Computer System Parameters Control Based on Multi-factoring Estimations

  • Zhengbing Hu
  • Vadym Mukhin
  • Yaroslav Kornaga
  • Oksana Herasymenko
  • Yevgenii Mostoviy
Article

Abstract

In this paper the approach and mechanisms for the complex analysis of the Distributed Computer System (DCS) parameters taking into account several criteria DCS functioning to select an efficient configuration of DCS resources are suggested. There are the analytical evaluations of DCS parameters such as: performance, security, reliability, data transfer rate, depending on the DCS dimension (number of nodes) taking into account the specific of their realization. The complex analytical model for the DCS parameters is suggested. The model allows estimate the parameters of DCS, depending on the number of nodes. The suggested model allows evaluate the impact of the number of nodes on the parameters of its functionality. In addition, this model on the design phase allows determine the DCS parameters for a certain number of DCS nodes. The suggested model allows set a certain level for the normalized DCS parameters, which should meet the all four parameters of the DCS, and to get the set of nodes, which should form the DCS cluster.

Keywords

System functioning Complex analysis Characteristics evaluation Resources allocation 

Notes

Acknowledgements

This scientific work was financially supported by self-determined research funds of CCNU from the colleges’ basic research and operation of MOE (CCNU16A02015).

References

  1. 1.
    Atzori, L., Iera, A., Morabito, G.: The internet of things: a survey. Comput. Netw. 19, 2728–2805 (2010).  https://doi.org/10.1016/j.comnet.2010.05.010 MATHGoogle Scholar
  2. 2.
    Miorandi, D., Sicari, S., Pellegrini, F., Chlamtac, I.: Internet of things: vision, applications and research challenges. Ad Hoc Netw. 10, 1497–1516 (2012)CrossRefGoogle Scholar
  3. 3.
    Gubbi, J., Buyya, R., Marusic, S., Palaniswami, M.: Internet of things (IoT): a vision, architectural elements, and future directions. Future Gener. Comput. Syst. 29, 1645–1660 (2013)CrossRefGoogle Scholar
  4. 4.
    Fernando, N., Loke, S.W., Rahayu, W.: Mobile cloud computing: a survey. Future Gener. Comput. Syst. 29, 84–106 (2013)CrossRefGoogle Scholar
  5. 5.
    Dinh, H.T., Lee, C., Niyato, D., Wang, P.: A survey of mobile cloud computing: architecture, applications, and approaches. Wirel. Commun. Mob. Comput. https://www.eecis.udel.edu/~cshen/859/papers/survey_MCC.pdf (2016). Accessed 30 Oct 2016
  6. 6.
    Hung, P.P., Bui, T.-A., Soonil, K., Huh, E.-N.: A new technique for optimizing resource allocation and data distribution in mobile cloud computing. Elektronika ir Elektrotechnika 22(1), 73–80 (2016)CrossRefGoogle Scholar
  7. 7.
    Hwang, K., Bai, X., Shi, Y., Li, M., Chen, W.-G., Wu, Y.: Cloud performance modeling with benchmark evaluation of elastic scaling strategies. IEEE Trans. Parallel Distrib. Syst. 27, 130–143 (2016)CrossRefGoogle Scholar
  8. 8.
    Wang, C., Ming, L., Zhao, J., Wang, D.: A general framework for network survivability testing and evaluation. J. Netw. 6, 831–841 (2011)Google Scholar
  9. 9.
    Tarvainen, P.: Survey of the survivability of it systems. VTT Technical Research Center of Finland. http://virtual.vtt.fi/virtual/proj1/projects/merlin/pub/survey_of_the_survivability_of_it_systems.pdf (2004). Accessed 30 Oct 2016
  10. 10.
    Trivedi, K.S., Xia, R.: Quantification of system survivability. Telecommun. Syst. 60, 451–470 (2015)CrossRefGoogle Scholar
  11. 11.
    Mukhin, V.Y., Shyrochin, V.P.: Adaptive security mechanisms for the computer networks based on risk analysis. J. Qafqaz Univ. Issue Math. Comput. Sci. 1(1), 11–16 (2013)Google Scholar
  12. 12.
    Wang, C., Wang, Q., Ren, K., Cao, N., Lou, W.: Toward secure and dependable storage services in cloud computing. IEEE Trans. Serv. Comput. 5(2), 220–232 (2012)CrossRefGoogle Scholar
  13. 13.
    Allcock, B., Bester, J., Bresnahan, J., Hervenak, A.L., Foster, I., Kesselman, C., Meder, S., Nefedova, V., Quesnel, D., Tuecke, S.: Data management and transfer in high-performance computational grid environments. http://toolkit.globus.org/alliance/publications/papers/dataMgmt.pdf (2002). Accessed 30 Oct 2016
  14. 14.
  15. 15.
    Hegr, T., Voznak, M., Kozak, M., Bohac, L.: Measurement of switching latency in high data rate ethernet networks. Elektronika ir Elektrotechnika 21(3), 74–78 (2015)CrossRefGoogle Scholar
  16. 16.
    Mawata, C.: Graph theory lessons. Lesson 4: complete graphs. http://www.mathcove.net/petersen/lessons/get-lesson?les=4 (2010). Accessed 30 Oct 2016
  17. 17.
    Gene, A.: Validity of the single processor approach to achieving largescale computing capabilities. In: 1967 AFIPS Conference Proceedings, pp. 483–485 (1967)Google Scholar
  18. 18.
    Sun, X.-H., Chen, Y.: Reevaluating Amdahl’s law in the multicore era. J. Parallel Distrib. Comput. 70, 183–188 (2010)CrossRefMATHGoogle Scholar
  19. 19.
    Chowdhury, M., Zaharia, M., Ma, J., Jordan, M.I., Stoica, I.: Managing data transfers in computer clusters with orchestra. In: 2011 Proceedings of the ACM SIGCOMM 2011 Conference, pp. 98–109 (2011)Google Scholar
  20. 20.
    Xie, M., Dai, Y.S., Poh, K.-L.: Computing System Reliability. Models and Analysis. Kluwer, New York (2004)Google Scholar
  21. 21.
    Vishwanath, K.V., Nagappan, N.: Characterizing cloud computing hardware reliability. In: 2010 SoCC’10 Proceedings of the 1st ACM Symposium on Cloud Computing, pp. 193–204 (2010)Google Scholar
  22. 22.
    Mukhin, V.Y., Volokyta, A.N.: Integrated safety mechanisms based on security risks minimization for the distributed computer systems. Int. J. Comput. Netw. Inf. Secur. (IJCNIS) 5(2), 21–28 (2013)Google Scholar
  23. 23.
    Mukhin, V.: The rating mechanism for the trusted relationship establishment for the security of the distributed computer systems. Int. J. Comput. Netw. Inf. Secur. (IJCNIS) 6(6), 41–47 (2014).  https://doi.org/10.5815/ijcnis.2014.06.06 Google Scholar
  24. 24.
    Khare, A.K., Rana, J.L., Jain, R.C.: Detection of wormhole, blackhole and DDOS attack in MANET using trust estimation under fuzzy logic methodology. Int. J. Comput. Netw. Inf. Secur. (IJCNIS) 9(7), 29–35 (2017).  https://doi.org/10.5815/ijcnis.2017.07.04 Google Scholar
  25. 25.
    Jha, R.K., Kharga, P.: A comparative performance analysis of routing protocols in MANET using NS3 simulator. Int. J. Comput. Netw. Inf. Secur. 2015(4), 62–68 (2015)Google Scholar
  26. 26.
    Madhurya, M., Ananda Krishna, B., Subhashini, T.: Implementation of enhanced security algorithms in mobile ad hoc networks. Int. J. Comput. Netw. Inf. Secur. 2, 30–37 (2014)Google Scholar
  27. 27.
    Mukhin, V.Y., Bidkov, A.Y., Thinh, V.D.: The forming of trust level to the nodes in the distributed computer systems. In: 2012 Proceedings of XIth International Conference “Modern Problems of Radio Engineering, Telecommunications and Computer Science TCSET’2012”, p. 362 (2012)Google Scholar
  28. 28.
    Nyquist–Shannon sampling theorem. https://en.wikipedia.org/wiki/Nyquist%E2%80%93Shannon_sampling_theorem. Accessed 30 Oct 2016

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Educational Information TechnologyCentral China Normal UniversityWuhanChina
  2. 2.Department of Mathematical Methods of System AnalysisNational Technical University of Ukraine “Igor Sikorsky Kiev Polytechnic Institute”KievUkraine
  3. 3.Technical Cybernetics DepartmentNational Technical University of Ukraine “Igor Sikorsky Kiev Polytechnic Institute”KievUkraine
  4. 4.Network and Internet Technology DepartmentTaras Shevchenko National University of KievKievUkraine

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