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A Novel Estimation Framework for Quality of Resilience

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Abstract

As the increase of complexity in the telecommunication service and system, the importance of service quality and reliability has also gained more interest. In this paper, state-of-the-art of various quality terms has been discussed; as well, the relationship among these terms toward user satisfaction and a new reliability evaluation perspective has been presented through the measurement parameters. Moreover, the limitations of traditional reliability evaluation methods have been raised; accordingly, the selective resilience parameter algorithm and the modern reliability evaluation method are proposed by using Bayesian statistics. The proposed algorithm can provide practical reliability measurement and can apply for a preventive failure or maintenance plan. Besides, the novel estimation approach can incorporate the effect of both subjective and objective parameters into the service or system reliability estimation.

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References

  1. Tech. spec. ts 23.228 version 11.1.0, ip mul-timedia subsystem; stage 2 (release 11), June 2011. www.3gpp.org/ftp/Specs/html-info/23228.htm

  2. Meddour, D.-E., Javaid, U., Bihannic, N., Rasheed, T., & Boutaba, R. (2009). Completing the convergence puzzle: A survey and a roadmap. Wireless Communications, IEEE, 16(3), 86–96. doi:10.1109/MWC.2009.5109468.

    Article  Google Scholar 

  3. Hameed, S., Raza, A., Badii, A., & Lee, S. (2009). Converged next generation network architecture and its reliability. In: ECMS (pp. 693–707).

  4. Taleb, T., Hadjadj-Aoul, Y., & Ahmed, T. (2011). Challenges, opportunities, and solutions for converged satellite and terrestrial networks. Wireless Communications, IEEE, 18(1), 46–52.

    Article  Google Scholar 

  5. Cheboldaeff, M. (2011). Service charging challenges in converged networks. Communications Magazine, IEEE, 49(1), 118–123.

    Article  Google Scholar 

  6. Msakni, H.G., & Youssef, H. (2012). Provisioning qoe over converged networks: Issues and challenges. In: High Performance Computing and Communication and 2012 IEEE 9th International Conference on Embedded Software and Systems (HPCC-ICESS), 2012 IEEE 14th International Conference on, IEEE, 2012 (pp. 891–896).

  7. Xie, M., & Lai, C. (1996). Reliability analysis using an additive weibull model with bathtub-shaped failure rate function. Reliability Engineering and System Safety, 52(1), 87–93.

    Article  Google Scholar 

  8. Trivedi, K. S. (2002). Probability and statistics with reliability, queuing and computer science applications (2nd ed.). Chichester, UK: Wiley.

    MATH  Google Scholar 

  9. Sahner, R. A., Trivedi, K. S., & Puliafito, A. (1996). Performance and reliability analysis of computer systems: An example-based approach using the SHARPE software package. Norwell, MA: Kluwer Academic Publishers.

    Book  MATH  Google Scholar 

  10. Stankiewicz, R., Cholda, P., & Jajszczyk, A. (2011). Qox: What is it really? Communications Magazine, IEEE, 49(4), 148–158.

    Article  Google Scholar 

  11. Stankiewicz, R., & Jajszczyk, A. (2011). A survey of QoE assurance in converged networks. Computer Networks, 55(7), 1459–1473.

    Article  Google Scholar 

  12. Kuipers, F., Kooij, R., De Vleeschauwer, D., & Brunnström, K. (2010). Techniques for measuring quality of experience. In: Proceedings of the 8th international conference on Wired/Wireless Internet Communications, WWIC’10 (pp. 216–227). Berlin, Heidelberg: Springer.

  13. Dai, Q. (2011). A survey of quality of experience. In: R. Lehnert (Ed.), Energy-aware communications, vol. 6955 of Lecture Notes in ComputerScience (pp. 146–156). Berlin Heidelberg: Springer.

  14. Msakni, H., & Youssef, H. (2012). Provisioning QoE over converged networks: Issues and challenges. In: IEEE 14th International Conference on High Performance Computing and Communication 2012 IEEE 9th International Conference on Embedded Software and Systems (HPCC-ICESS), 2012 (pp. 891–896). doi:10.1109/HPCC.2012.127.

  15. Khan, A., Sun, L., Ifeachor, E., Fajardo, J.O., & Liberal, F. (2010). Video quality prediction model for h.264 video over umts networks and their application in mobile video streaming. In: IEEE International Conference on Communications (ICC), 2010 (pp. 1–5).

  16. Valerdi, J., Gonzalez, A., & Garrido, F. (2009). Automatic testing and measurement of qoe in IPTV using image and video comparison. In: Fourth International Conference on Digital Telecommunications, 2009. ICDT ’09 (pp. 75–81).

  17. Tapolcai, J., Mth, D., Zahemszky, A., Autenrieth, A., Chołda, P., Cinkler, T., . Colle, D., & Wajda, K. (2006). Quantification of resilience for voice-over-ip applications. In: Proceedings of the International Symposium on Broadband Access Technologies in Metropolitan Area Networks (ISBAT). Niagara Falls.

  18. Veugelers R. (Ed.) (2009). The evaluation of the finnish national innovation system: Full report. The Research Institute of the Finnish Economy.

  19. Ibarrola, E., Xiao, J., Liberal, F., & Ferro, A. (2011). Internet QoS regulation in future networks: A user-centric approach. Communications Magazine, IEEE, 49(10), 148–155.

    Article  Google Scholar 

  20. Cholda, P., Tapolcai, J., Cinkler, T., Wajda, K., & Jajszczyk, A. (2009). Quality of resilience as a network reliability characterization tool. Network, IEEE, 23, 11–19.

    Article  Google Scholar 

  21. Bolstad, W. M. (2007). Introduction to bayesian statistics (2nd ed.). New York: Wiley.

    Book  MATH  Google Scholar 

  22. Robert, C. (2007). The bayesian choice: From decision-theoretic foundations to computational implementation. New York: Springer.

    MATH  Google Scholar 

  23. Chang, R., & Stetter, M. (2007). Quantitative bayesian inference by qualitative knowledge modeling. In: International Joint Conference on Neural Networks, 2007. IJCNN 2007. (pp. 2563–2568).

  24. Zaidi, A., Bouamama, B. O., & Tagina, M. (2012). Bayesian reliability models of weibull systems: State of the art. Applied Mathematics and Computer Science, 22(3), 585–600.

    MathSciNet  MATH  Google Scholar 

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Correspondence to Chayapol Kamyod.

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Kamyod, C., Nielsen, R.H., Prasad, N.R. et al. A Novel Estimation Framework for Quality of Resilience. Wireless Pers Commun 90, 1369–1386 (2016). https://doi.org/10.1007/s11277-016-3395-5

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  • DOI: https://doi.org/10.1007/s11277-016-3395-5

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