Functional improvement of monitoring the dynamic characteristics of information andcommunication networks

Authors

  • Юрий Олегович Бабич Odessa National Academy of Telecommunications named after O. S. Popov 1 Kovalska St., Odessa, Ukraine, 65029, Ukraine https://orcid.org/0000-0002-7888-7591
  • Леся Андреевна Никитюк Odessa National Academy of Telecommunications named after O. S. Popov 1 Kovalska St., Odessa, Ukraine, 65029, Ukraine https://orcid.org/0000-0001-5232-8735

DOI:

https://doi.org/10.15587/1729-4061.2015.47598

Keywords:

predictive monitoring, dynamic characteristics of the information and communication network, prognosis/prognostication, polynominal extrapolation

Abstract

The study reveals conceptual principles of predictive monitoring of the dynamic characteristics of modern information and communication networks. The suggested procedures of predictive monitoring are aimed at a functional improvement of the existing types of monitoring information and communication networks. The procedures that include consecutive stages of short-term, situational and long-term prognostication are implemented by means of statistic analysis and aimed at an early detection of possible emergencies.

The paper suggests asystem of predictive monitoring. The existing types of monitoring that are functionally improved by means of the procedures of predictive monitoring are illustrated in terms of TMNand TINA.

Predictive monitoring allows making timely decisions on reconfiguring the resources of the monitored objectin order to either prevent emergencies or decide on the network reconstruction if the means of reconfiguration are no more effective.

Author Biographies

Юрий Олегович Бабич, Odessa National Academy of Telecommunications named after O. S. Popov 1 Kovalska St., Odessa, Ukraine, 65029

Senior Lecturer

Department of telecommunication networks

Леся Андреевна Никитюк, Odessa National Academy of Telecommunications named after O. S. Popov 1 Kovalska St., Odessa, Ukraine, 65029

Candidate of Technical Sciences, Professor, Head of Department

Department of telecommunication networks

 

References

  1. Poikselka, M., Mayer, G. (2009). The IMS: IP multimedia concepts and services. West Sussex: John Wiley & Sons, Ltd, 560.
  2. Ghetie, J. (2008). Fixed-Mobile Wireless Networks Convergence. Technologies, Solutions, Services. New York: Cambridge University Press, 464.
  3. Vorobiienko, P. P., Nikitiuk, L. A., Reznichenko, P. I. (2010). Telekomunikatsiini ta informatsiini merezhi. K.: SAMMIT-KNYHA, 640.
  4. ITU-R Recommendation V.662-3 Terms and definitions. (2005). Approved 2005. Geneva: ITU, 19.
  5. Rekomendatsiia MSE-R BT.1790 Trebovaniia k kontroliu radioveshchatel'nyh tsepei v hode ekspluatatsii. (2007). Geneva: ITU, 6.
  6. ITU-T Recommendation G.8001 Terms and definitions for Ethernet frames over Transport. (2008). Approved 2008-03-29. Geneva: ITU, 12.
  7. ITU-T Recommendation I.113 Vocabulary of terms for broadband aspects of ISDN. (1997). Approved 1997-06-20. Geneva: ITU, 35.
  8. Ali, A., Khelil, A., Shaikh, F. K., Suri, N. (2010). MPM: Map Based Predictive Monitoring for Wireless Sensor Networks. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, Vol. 23, 79–95. doi:10.1007/978-3-642-11482-3_6
  9. Achir, M., Ouvry, L. Power consumption prediction in wireless sensor networks. The Pennsylvania State University. Available: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.60.1065&rep=rep1&type=pdf
  10. Landsiedel, O., Wehrle, K., Gotz, S. Accurate prediction of power consumption in sensor networks. The Pennsylvania State University. Available: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.112.6036&rep=rep1&type=pdf
  11. Mini, A. F., Nath, B., Loureiro, A. F. A probabilistic approach to predict the energy consumption in wireless sensor networks. The Pennsylvania State University. Available: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.11.4906&rep=rep1&type=pdf
  12. Wang, X., Ma, J.-J., Ding, L., Bi, D.-W. (2007, November). Robust Forecasting for Energy Efficiency of Wireless Multimedia Sensor Networks. Sensors, Vol. 7, № 11, 2779–2807. doi:10.3390/s7112779
  13. Clifton, L., Clifton, D. A., Pimentel, M. A. F., Watkinson, P. J., Tarassenko, L. (2014, May). Predictive Monitoring of Mobile Patients by Combining Clinical Observations With Data From Wearable Sensors. IEEE Journal of Biomedical and Health Informatics, Vol. 18, № 3, 722–730. doi:10.1109/jbhi.2013.2293059
  14. Moorman, J. R., Rusin, C. E., Hoshik Lee, Guin, L. E., Clark, M. T., Delos, J. B., Kattwinkel, J., Lake, D. E. (2011, August). Predictive monitoring for early detection of subacute potentially catastrophic illnesses in critical care. 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Institute of Electrical & Electronics Engineers (IEEE), 5515–5518. doi:10.1109/iembs.2011.6091407
  15. Metzger, A., Leitner, P., Ivanovic, D., Schmieders, E., Franklin, R., Carro, M., Dustdar, S., Pohl, K. (2015, February). Comparing and Combining Predictive Business Process Monitoring Techniques. IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 45, № 2, 276–290. doi:10.1109/tsmc.2014.2347265
  16. Franceschinis, M., Mauro, F., Pastrone, C., Spirito, M. A., Rossi, M. (2013, October). Predictive monitoring of train wagons conditions using wireless network technologies. 2013 XXIV International Conference on Information, Communication and Automation Technologies (ICAT). Institute of Electrical & Electronics Engineers (IEEE), 1–8. doi:10.1109/icat.2013.6684032
  17. Zain, S. M., Kien Kek Chua. (2011, March). Development of a neural network Predictive Emission Monitoring System for flue gas measurement. 2011 IEEE 7th International Colloquium on Signal Processing and its Applications. Institute of Electrical & Electronics Engineers (IEEE), 314–317. doi:10.1109/cspa.2011.5759894
  18. Bobalo, Yu. Ya., Danyk, Yu. H., Komarova, L. O., Lukianov, O. O., Maksymovych, V. M., Pysarchuk, O. O., Storonskyi, Yu. B., Strykhaliuk, B. M. (2014). Monitorynh obiektiv v umovakh apriornoi nevyznachenosti dzherel informatsii. Teoriia i praktyka. Lviv: Kolo, 252.
  19. Yoo, W., Sim, A. (2015, February). Network bandwidth utilization forecast model on high bandwidth networks. 2015 International Conference on Computing, Networking and Communications (ICNC). Institute of Electrical & Electronics Engineers (IEEE), 494–498. doi:10.1109/iccnc.2015.7069393
  20. Zaman, F., Hogan, G., Meer, S. Der, Keeney, J., Robitzsch, S., Muntean, G. (2015, January). A recommender system architecture for predictive telecom network management. IEEE Communications Magazine, Vol. 53, № 1, 286–293. doi:10.1109/mcom.2015.7010547
  21. Mirza, M., Sommers, J., Barford, P., Zhu, X. (2007, June). A machine learning approach to TCP throughput prediction. Proceedings of the 2007 ACM SIGMETRICS international conference on Measurement and modeling of computer systems – SIGMETRICS ’07, Vol. 35, № 1, 97–108 doi:10.1145/1254882.1254894
  22. Sazhin, Yu. V., Katyn', A. V., Saraikin, Yu. V. (2013). Analiz vremennyh riadov i prognozirovanie. Saransk: Izd-vo Mordov. Un-ta, 192.
  23. Chetyrkin, E. M. (1977). Statisticheskie metody prognozirovaniia. M.: Statistika, 200.
  24. ITU-T Recommendation M.3010 Principles for a telecommunications management network. (2000). Approved 2000-02-04. Geneva: ITU, 44.
  25. Chapman, M., Montesi, S. (1995, Feb. 17). TB_MDC.018_1.0_94. Overall Concepts and Principles of TINA. Telecommunications Information Networking Architecture Consortium. Available: http://www.tinac.com/specifications/documents/overall.pdf

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Published

2015-08-21

How to Cite

Бабич, Ю. О., & Никитюк, Л. А. (2015). Functional improvement of monitoring the dynamic characteristics of information andcommunication networks. Eastern-European Journal of Enterprise Technologies, 4(9(76), 9–15. https://doi.org/10.15587/1729-4061.2015.47598

Issue

Vol. 4 No. 9(76) (2015): Information and controlling system

Section

Information and controlling system

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Copyright (c) 2015 Леся Андреевна Никитюк, Юрий Олегович Бабич

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