Resource-Oriented Approaches to Implementation of Traffic Control Technologies in Safety-Critical I&C Systems

  • Georgiy Kuchuk
  • Andriy KovalenkoEmail author
  • Vyacheslav Kharchenko
  • Anatoliy Shamraev
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 105)


Changes in approaches to safety-critical Instrumentation and Control (I&C) systems traffic control results in changes in system power consumption. Thus, timely detection of traffic anomalies allows significant reducing of power consumption due to effective redistribution of the data flows. Well-balanced traffic allows reducing the power consumption of the network compared to the same network, but without such traffic balancing. In this chapter we consider appropriate approaches intended to solve the problem of traffic control in safety-critical I&C systems in order to reduce data flows transmission time. Method of rapid identification of traffic parameters on the basis of the current samples for timely detection of traffic anomalies is proposed. The method allows reducing time of traffic main parameters identification by 15–20%. This makes it possible to determine the behavior of the existing traffic. Moreover, method for short-term prediction of traffic behavior is also proposed. Standard tools are used for flexible and poorly aggregated traffic. An original approach is proposed for the prediction of fractal traffic. Such approach is based on certain properties of fractal processes, including self-similarity and linearity of autocorrelation function. The results of the prediction are used for dynamic control of load distribution in virtual connections, and methods of its effective redistribution allow reducing system power consumption. It is significant that minimal power consumption was achieved when alternative routes were used to reroute the traffic, which was previously flowing through network bottlenecks. Simulation results proved that proposed approach allowed reducing power consumption during appearance anomalies in traffic behavior, compared to standard approaches, by approximately 20%. Moreover, even if traffic behavior is free from anomalies, proposed approach allows increase energy efficiency of the system. Thus, due to well-balanced load caused by data flows into physical links, power consumption of safety-critical I&C systems can be reduced by 7%.


Power consumption Traffic Prediction Control I&C system 


  1. 1.
    Kharchenko, V., Kondratenko, Y., Kacprzyk, J. (eds.): Green IT Engineering: Concepts, Models, Complex Systems Architectures, Studies in Systems, Decision and Control, vol. 74. Springer International Publishing, Berlin (2017). doi: 10.1007/978-3-319-44162-7
  2. 2.
    Yastrebenetskiy, M., Kharchenko, V. (eds.): Nuclear power plant instrumentation and control systems for safety and security. In: Advances in Environmental Engineering and Green Technologies (AEEGT) Book Series, 470 pp. IGI Global, Hershey, Pennsylvania, United States of America (2014)Google Scholar
  3. 3.
    Michalski, J., Lanzone, A., Trent, J., Smith, S.: Sandia Report SAND2007-3345. Secure ICCP Integration Considerations and Recommendations, 98 pp. Sandia National Laboratories, Albuquerque, New Mexico. (2007)Google Scholar
  4. 4.
    Torres-Pomales, W.: NASA/TM-2014-218242. Selecting an Architecture for a Safety-Critical Distributed Computer System with Power, Weight and Cost Considerations, 24 pp. Langley Research Center, Hampton, Virginia (2014)Google Scholar
  5. 5.
    Kharchenko, V., Illiashenko, O.: Concepts of green IT engineering: taxonomy, principles and implementation. In: Kharchenko, V., Kondratenko, Y., Kacprzyk, J. (eds.) Green IT Engineering: Concepts, Models, Complex Systems Architectures, Studies in Systems, Decision and Control, vol. 74, pp. 3–20. Springer International Publishing, Berlin (2017). doi: 10.1007/978-3-319-44162-7_1
  6. 6.
    Shamraev, A., Shamraeva, E., Dovbnya, A., Kovalenko, A., Ilyunin, O.: Green microcontrollers in control system for the linear electron accelerator magnetic elements. In: Kharchenko, V., Kondratenko, Y., Kacprzyk, J. (eds.) Green IT Engineering: Concepts, Models, Complex Systems Architectures, Studies in Systems, Decision and Control, vol. 74, pp. 283–305. Springer International Publishing, Berlin (2017). doi: 10.1007/978-3-319-44162-7_15
  7. 7.
    Kovalenko, A.: Approaches to synthesis of informational structure of control system for critical application object. Syst. Inf. Process. 1(117), 180–184 (Kharkov). (2014)Google Scholar
  8. 8.
    Michele, R., Raffaella, V., Michele, Z.: Accurate analysis of TCP on channels with memory and finite round-trip delay. IEEE Trans. Wirel. Commun 3(2), 627–640 (2004)CrossRefGoogle Scholar
  9. 9.
    De Ghein, L.: MPLS Fundamentals, 672 pp. Cisco Press (2006)Google Scholar
  10. 10.
    D’Apice, C., Manzo, R., Likhanov, N., Salerno, S.: Network traffic modelling and packet loss probability approximation. Stability problems for stochastic models. J. Math. Sci. Part II 132(5), 590–601 (Kluwer-Plenum, New York-London). (2006)Google Scholar
  11. 11.
    Kuchuk, G., Kharchenko, V., Kovalenko, A., Ruchkov, E.: Approaches to selection of combinatorial algorithm for optimization in network traffic control of safety-critical systems. In: Proceedings of IEEE East-West Design and Test Symposium (EWDTS’2016), pp. 384–389 (2016)Google Scholar
  12. 12.
    Forouzan Behrouz, A.: Data Communications and Networking, 5th edn, 1264 pp. McGraw-Hill Science/Engineering/Math, New York (2012)Google Scholar
  13. 13.
    Kuchuk, G.A., Akimova, Y.A., Klimenko, L.A.: Method of optimal allocation of relational tables. Eng. Simul. 17(5), 681–689 (2000)Google Scholar
  14. 14.
    Kuchuk, G.A., Kovalenko, A.A., Mozhaev, A.A.: An approach to development of complex metric for multiservice network security assessment. In: Proceedings of International Conference on Statistical Methods of Signal and Data Processing (SMSDP—2010), pp. 158–160, NAU, RED, IEEE Ukraine section joint SP, Kiev, 13–14 Oct 2010Google Scholar
  15. 15.
    Al-Howaide, A.Z., Doulat, A.S., Khamayseh, Y.M.: Al-Howaide. Performance evaluation of different scheduling algorithms in WiMax. IJCSEA Int. J. Comput. Sci. Eng. Appl. 1(5), 81–94 (2011)Google Scholar
  16. 16.
    Teixeira, V., Guardieiro, A.: A new and efficient adaptive scheduling packets for the uplink traffic in WiMAX networks. EURASIP J. Wirel. Commun. Networking. 2011(1), 112–123 (2011)Google Scholar
  17. 17.
    Annadurai, C.: Review of packet scheduling algorithms in mobile ad hoc networks. Int. J. Comput. Appl. 15(1), 7–10 (2011)Google Scholar
  18. 18.
    Jandaeng, C., Suntiamontut, W., Elz, N.: Review PSA. The packet scheduling algorithm for wireless sensor networks. GRAPH-HOC Int. J. Appl. Graph Theory Wirel. Ad hoc Netw. Sens. Netw 3(3), 1–12 (2011)CrossRefGoogle Scholar
  19. 19.
    Karasaridis, A., Hatzinakos, D.: Network heavy traffic modeling using a-stable self-similar process. IEEE Trans. Commun 49(7), 1203–1214 (2001)CrossRefzbMATHGoogle Scholar
  20. 20.
    Hoang, V.-T., Julien, N., Berruet, P.: On-line self-diagnosis based on power measurement for a wireless sensor node. In: Proceedings of 1st Workshop on Highly-Reliable Power-Efficient Embedded Designs, 36 pp, Shenzhen, China. (2013)

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Georgiy Kuchuk
    • 1
  • Andriy Kovalenko
    • 2
    • 3
    Email author
  • Vyacheslav Kharchenko
    • 1
    • 3
  • Anatoliy Shamraev
    • 4
  1. 1.National Aerospace University “KhAI” Named After N.E. ZhukovskiyKharkivUkraine
  2. 2.Kharkiv National University of Radio ElectronicsKharkivUkraine
  3. 3.Research and Production Company RadiyKropivnitskiyUkraine
  4. 4.Belgorod State National Research UniversityBelgorodRussian Federation

Personalised recommendations