Math Modeling of the Reliability Control and Monitoring System of Complex Network Platforms

  • Elmira Yu. KalimulinaEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 941)


This paper is concerned with analytical models and methods for reliability planning, optimization, and operation of the control and monitoring system of SMS-network platforms. The difference between classical models of reliability and models developed here is that the last ones take into account the economy is often an important aspect of reliability planning. Network providers operate in a competitive environment, so the main principle of reliability planning is not so much to increase reliability as well (as it’s done in fully technical approach) as to maximise the total profit of the network. Thus, new methods must allow to find the combination of factors (costs, investments into reliability, redundancy and increased maintenance, the profit from system operation, penalties for delays and failures, risks defined in a service level agreement, etc.) that maximises the profit. We employ the well-known from an actuarial science a model that considers a network through two cash flows: incoming cash from customers and outgoing claims paid due unreliability under SLA. Also we include into the optimisation model more general assumptions about the reliability of systems: different ways of reliability improvements and redundancy, non-exponentially distributed failures and recoveries, dependent failures, and reliability control.


Reliability model Reliability planning Reliability optimisation Redundancy Reservation Dependent failures Markov models Cash flows 


  1. 1.
    Lei, L.: Study on reliability optimization problem of computer network. Int. J. Secur. Appl. 9(4), 161–174 (2015)Google Scholar
  2. 2.
    Elshqeirat, B., Soh, S., Rai, S., Lazarescu, M.: Topology design with minimal cost subject to network reliability constraint. IEEE Trans. Reliab. 64(1), 118–131 (2015)CrossRefGoogle Scholar
  3. 3.
    Caserta, M., Vob, S.: An exact algorithm for the reliability redundancy allocation problem. Eur. J. Oper. Res. 244(1), 110–116 (2015)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Mangey, R.: On system reliability approaches: a brief survey. Int. J. Syst. Assur. Eng. Manag. 4(2), 101–117 (2013)CrossRefGoogle Scholar
  5. 5.
    Moradijoz, M., Moghaddam, M.P., Haghifam, M.R., Alishahi, E.: A multi-objective optimization problem for allocating parking lots in a distribution network. Int. J. Electr. Power Energy Syst. 46, 115–122 (2013)CrossRefGoogle Scholar
  6. 6.
    Soltani, R.: Reliability optimization of binary state non-repairable systems: a state of the art survey. Int. J. Ind. Eng. Comput. 5(3), 339–364 (2014)MathSciNetGoogle Scholar
  7. 7.
    ITU-T Recommendations, E.862(06/92): Dependability planning of telecommunication networks, 15 pages. ITU Telecommunication Standardization Sector, Geneva (1992)Google Scholar
  8. 8.
    Kalimulina, E.Y.: Reliability computation for complex systems with parallel structure that are completely repairable during use. Autom. Remote Control 71(6), 1257–1264 (2010)CrossRefGoogle Scholar
  9. 9.
    Kalimulina, E.Yu.: Mathematical model for reliability optimisation of distributed telecommunications networks. In: 2011 International Conference on Computer Science and Network Technology (ICCSNT), 24–26 December 2011, vol. 4, pp. 2847–2853 (2011)Google Scholar
  10. 10.
    Kalimulina, E.Y.: Analysis of system reliability with control, dependent failures, and arbitrary repair times. Int. J. Syst. Assur. Eng. Manag. 8, 180 (2017). Scholar
  11. 11.
    Kalimulina, E.Y.: A new approach for dependability planning of network systems. Int. J. Syst. Assur. Eng. Manag. 4, 215 (2013). Scholar
  12. 12.
    Gertsbakh, I.B., Shpungin, Y.: Models of Network Reliability : Analysis, Combinatorics, and Monte Carlo. CRC Press, Boca Raton (2010)zbMATHGoogle Scholar
  13. 13.
    Kalimulina, E.: Analytical reliability models and their application for planning and optimisation of telecommunication networks, 11 February 2016. SSRN: or

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.V. A. Trapeznikov Institute of Control SciencesRussian Academy of SciencesMoscowRussia

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