Abstract
The investigation of traffic properties of modern networks requires new approaches, the use of adequate types of distributions of traffic components, and measurement errors should be also taken into account. The models of the request flow are approximated by different distributions with “light tails” (Gaussian, Poisson distributions) as well as “heavy tails” (Pareto, Weibull, log-normal distributions). Self-similar traffic models are widely used to describe traffic in packet-switched networks. The degree of self-similarity of traffic can be determined by various methods, one of them is the estimation of the Hurst index. In the paper, new approaches in simulation of self-similar traffic and theoretical estimation of Hurst index with measurement errors are studied, the statistical simulation of main needed distributions with heavy tails is also considered.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Messier, G.G., Finvers, I.G.: Traffic models for medical wireless sensor networks. IEEE Commun. Lett. 11(1), 13–15 (2007). https://doi.org/10.1109/LCOMM.2007.061291. https://ieeexplore.ieee.org/document/4114210
Wang, Q., Zhang, T.: Source traffic modeling in wireless sensor networks for target tracking. In: Proceedings of the 5th ACM International Symposium on Performance Evaluation of Wireless Ad-Hoc, Sensor, and Ubiquitous Networks (PE-WASUN 2008), pp. 96–100 (2008)
Vybornova, A., Koucheryavy, A.: Traffic analysis in target tracking ubiquitous sensor networks. In: Balandin, S., Andreev, S., Koucheryavy, Y. (eds.) Internet of Things, Smart Spaces, and Next Generation Networks and Systems, pp. 389–398. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10353-2_34
Levakov, A.K., Sokolov, A.N., Sokolov, N.A.: Models of incoming traffic in packet networks. T-Comm 9(5), 91–94 (2015)
Hosseini, S.A., Akbarzadeh-T, M.-R., Naghibi-Sistani, M.-B.: Qualitative and quantitative evaluation of EEG signals in epileptic seizure recognition. Int. J. Intell. Syst. Appl. (IJISA) 5(6), 41–46 (2013). https://doi.org/10.5815/ijisa.2013.06.05
Goshvarpour, A., Goshvarpour, A.: Nonlinear analysis of human gait signals. Int. J. Inf. Eng. Electron. Bus. 4(2), 15–21 (2012). https://doi.org/10.5815/ijieeb.2012.02.03
Goshvarpour, A., Goshvarpour, A.: Chaotic behavior of heart rate signals during Chi and Kundalini meditation. Int. J. Image Graph. Signal Process. 4(2), 23–29 (2012). https://doi.org/10.5815/ijigsp.2012.02.04
Norros, I.: A storage model with self-similar input. Queueing Syst. 16(3–4), 387–396 (1994). https://doi.org/10.1007/BF01158964
Kilpi, J., Norros, I.: Testing the Gaussian approximation of aggregate traffic. In: Proceedings of the Second ACM SIGCOMM Workshop, Marseille, France, pp. 49–61 (2002)
Sheluhin, O.I., Smolskiy, S.M., Osin, A.V.: Similar processes in telecommunication. Wiley, Hoboken (2007)
Ageev, D.V.: Parametric synthesis of multiservice telecommunication systems in the transmission of group traffic with the effect of self-similarity. Electron. Sci. Specialized Ed.: Prob. Telecommun. 1(10), 46–65 (2013)
Pashko, A.: Simulation of telecommunication traffic using statistical models of fractional Brownian motion. In: 4th International Scientific-Practical Conference Problems of Infocommunications. Science and Technology. Conference Proceedings, 10–13 October 2017, pp. 414–418 (2017)
Pashko, A., Tretynyk, V.: Methods of the statistical simulation of the self-similar traffic. In: Zhengbing, H., Petoukhov, S., Dychka, I., He, M. (eds.) ICCSEEA 2018. AISC, vol. 754, pp. 54–63. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91008-6_6
Shi, F., Li, C., Zhu, Y.: Robustness evaluation for military communication effectiveness based on multiple data sources and Monte Carlo simulation. Int. J. Mod. Educ. Comput. Sci. 3(5), 1–9 (2011). https://doi.org/10.5815/ijmecs.2011.05.01
Mehrdoust, F., Fathi, K., Saber, N.: Accelerated simulation scheme for solving financial problems. Int. J. Inf. Technol. Comput. Sci. (IJITCS) 6(4), 43–48 (2014). https://doi.org/10.5815/ijitcs.2014.04.05
Burkhardt, J.: Bayesian parameter inference of explosive yields using Markov chain Monte Carlo techniques. Int. J. Math. Sci. Comput. (IJMSC) 6(2), 1–17 (2020). https://doi.org/10.5815/ijmsc.2020.02.01
Kozachenko, Y., Pashko, A.A.: Accuracy of simulation of the Gaussian random processes with continuous spectrum. Comput. Model. New Technol. 18(3), 7–12 (2014)
Kurchenko, O.O.: One strong consistency estimate of the Hurst parameter of the fractional Brownian motion. Theory Probab. Meth. Stat. 67, 97–106 (2003)
Czachórski, T., Domańska, J., Pagano, M.: On stochastic models of Internet traffic. In: Dudin, A., Nazarov, A., Yakupov, R. (eds.) ITMM 2015. CCIS, vol. 564, pp. 289–303. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25861-4_25
Kozachenko, Yu., Pogorilyak, O., Rozora, I., Tegza, A.: Simulation of Stochastic Processes with Given Accuracy and Reliability. ISTE Press & Elsevier. London (2016)
Pashko, A.O., Rozora, I.V.: Accuracy of simulation for the network traffic in the form of fractional Brownian motion. In: 2018 14th International Conference on Advanced Trends in Radioelecrtronics, Telecommunications and Computer Engineering (TCSET), Slavske, pp. 840–845 (2018). https://doi.org/10.1109/TCSET.2018.8336328
Pashko, A., Oleshko, T., Syniavska, O.: Estimation of hurst parameter for self-similar traffic. In: Hu, Z., Petoukhov, S., Dychka, I., He, M. (eds.) ICCSEEA 2020. AISC, vol. 1247, pp. 181–191. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-55506-1_16
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Pashko, A., Rozora, I., Syniavska, O. (2021). Estimation of Hurst Index and Traffic Simulation. In: Hu, Z., Petoukhov, S., Dychka, I., He, M. (eds) Advances in Computer Science for Engineering and Education IV. ICCSEEA 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 83. Springer, Cham. https://doi.org/10.1007/978-3-030-80472-5_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-80472-5_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-80471-8
Online ISBN: 978-3-030-80472-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)