DOI: https://doi.org/10.32515/2664-262X.2019.1(32).173-183
The Method of Generating a Fractally Similar Numerical Sequence Based on a Finite Automaton for Modeling Traffic in a Network
About the Authors
Hanna Drieieva, teacher, Central Ukraіnian National Technical University, Kropyvnytskyi, Ukraine
Oleksii Smirnov, Professor, Doctor in Technics (Doctor of Technics Sciences), Central Ukraіnian National Technical University, Kropyvnytskyi, Ukraine
Oleksandr Drieiev, Associate Professor, PhD in Technics (Candidate of Technics Sciences), Central Ukraіnian National Technical University, Kropyvnytskyi, Ukraine
Abstract
In this paper, the problem of presentation of traffic, for modeling its behavior when loading computer networks is considered. It is determined that traffic in computer networks on certain scales is fractal-like and at the same time the classical laws of calculation of parameters of a mass service system give false results.
The subject of study in the article is a method of generating a fractal numerical sequence based on a finite automaton for modeling traffic in a network. The purpose of the work is to create a generator of fractal binary sequences based on a finite automaton and to use the method of generating a fractal numerical sequence based on a finite automaton for modeling traffic in a network. To do this, the following tasks were solved: Fractal traffic was built using the proposed random number generator, its defects were determined; the place of the fractal traffic generator in the simulation systems is determined; the generation of a fractal numerical sequence on the basis of a finite automaton was performed; The statistical properties of partial sums of generated sequences are estimated. The result of the work is the implementation of the method of generating a fractal numerical sequence based on a finite automaton for modeling traffic in the network.
The relevance of the problem of creating generators of fractal binary sequences without the use of infinite distributions is shown; it is suggested to use a generator of a fractal binary sequence based on a finite automaton; the possibility of preliminary determination of the fractal dimension of the generated traffic with intensity λ = 0.5 is shown. Further directions of research, which consist in solving the following tasks, are determined: to carry out analytical estimations of the Hurst index of generated binary sequence with the traffic intensity λ = 0.5; show the variability of the fractal dimension of the binary sequence and with other intensities λ; output analytical expressions for generator parameters with a given output bits density with the control of their fractal dimension; improve analytical ratings and generalize them to the arbitrary intensity of generated traffic.
Keywords
fractal binary sequence generator, traffic, computer networks
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References
1. Broek, D. (1980). Osnovyi mehaniki razrusheniya. Moskva: Vyisshaya shkola. Perevod Dorofeeva Viktora Ivanovicha. (1974). Leyden: Elementary engineering fracture mechanics [in Russian].
2. Ushanev, K.V. (2015). Imitatsionnyie modeli sistemyi massovogo obsluzhivaniya tipa Pa/M/1, H2/M/1 i issledovanie na ih osnove kachestva obsluzhivaniya trafika so slozhnoy strukturoy. Sistemyi upravleniya, svyazi i bezopasnosti, 4, 217-251 [in Russian].
3. Dobrovolskiy, E.V. & Nechiporuk, O.L. (2005). Modelirovanie setevogo trafika s ispolzovaniem kontekstnyih metodov. NaukovI pratsI ONAZ Im. O.S. Popova, 1, 24-32 [in Russian].
4. Semenov, S.G., Meleshko, E.V. & Ilyushko, Ya.V. (2011). Matematicheskaya model multiservisnogo kanala svyazi na osnove eksponentsialnoy GERT-seti. Sistemi ozbroEnnya I vIyskova tehnIka, HUPS, 3(27), 64-67 [in Russian].
5. Tamara Radivilova, Yousef Ibrahim Daradkeh & Lyudmyla Kirichenko. (2018). Development of QoS Methods in the Information Networks with Fractal Traffic. International Journal of Electronics and Telecommunications, 64 (1), 27-32 [in English].
6. Mahdi Barat, Zadeh Joveini, Javad Sadri & Hoda Alavi Khoushhal (2018). Fractal Modeling of Big Data Networks. International Conference on Pattern Recognition and Artificial Intelligence (ICPRAI 2018). Canada, Montreal: Concordia University, 1-4 [in English].
7. Jiang, D., Huo, L. & Li, Y. (2018). Fine-granularity inference and estimations to network traffic for SDN. PLoS ONE, 13(5). Doi.org/10.1371/journal.pone.0194302 [in English].
8. Bulakh, V., Kirichenko, L. & Radivilova, T. (2018). Time Series Classification Based on Fractal Properties. International Conference on Data Stream Mining & Processing (DSMP): іn Proceedings of the 2018 IEEE Second, 21–25 August. Lviv. (рр. 198–201). Doi:10.1109/DSMP.2018.8478532 [in English].
9. Youri Raaijmakers, Hansjoerg Albrecher & Onno Boxma. (2017) The single server queue with mixing dependencies February 6. (http://www.hec.unil.ch/halbrech_files/QueueMixing.pdf) [in English].
10. Xie, K., Peng, C, Wang, X., Xie, G. & Wen, J. (2017). Accurate recovery of internet traffic data under dynamic measurements, in Proc. of INFOCOM’17, (рр. 1–9) [in English].
11. Wang, C, Maguluri, S T & Javidi, T. (2017). Heavy traffic queue length behavior in switches with reconfiguration delay, in Proc. of INFOCOM’17, (рр. 1–9) [in English].
12. Xie, G, Xie, K, Huang, J, Wang, X, Chen, Y. & Wen, J. (2017). Fast low-rank matrix approximation with locality sensitive hashing for quick anomaly detection, in Proc. of INFOCOM’17, (рр. 1–9) [in English].
13. Tatamikova Tatiana Mikhailovna & Kutuzov Oleg Ivanovich. (2016). Evaluation and comparison of classical and fractal queuing systems. XV International Symposium Problems of Redundancy in Information and Control Systems, 155 – 157 [in English].
14. Czarkowski Michał, Kaczmarek Sylwester & Wolff Maciej. (2016). Influence of Self -Similar Traffic Type on Perform ance of QoS Routing Algorithms. INTL Journal of electronics and telecommunications, Vol. 62, 1, 81-87 [in English].
15. Lakhmi Priya Das, Sanjay Kumar Patra (2016). Sarojananda Mishra. Impact of hurst parameter value in self-similarity behaviour of network traffic. International Journal of Research in Computer and Communication Technology, Vol 5, 12, 631-633 [in English].
16. Hae-Duck Joshua Jeong. (2002). Modelling of self-similar teletraffic for simulation. University of Canterbury [in English].
17. Kuchuk, G. A, MozhaEv, O. O. & Vorobyov, O. V. (2006). Metod prognozuvannya fraktalnogo trafIka. RadIoelektronnI I komp’yuternI sistemi, 6, 181–188. URL: http://nbuv.gov.ua/UJRN/recs_2006_6_34 [in English].
18. Kuchuk, G. A, MozhaEv, O. O. & Vorobyov, O. V. (2007). Prognozirovanie trafika dlya upravleniya peregruzkami integrirovannoy telekommunikatsionnoy seti. RadIoelektronnI I komp’yuternI sistemi, 8, 261–271. URL: http://nbuv.gov.ua/UJRN/recs_2007_8_48 [in Russian].
19. Kuchuk, G. A., MozhaEv, O. O. & Vorobyov, O. V. (2006). AnalIz ta modelI samopodIbnogo trafIka. Aviatsionno-kosmicheskaya tehnika i tehnologiya, 9, 173–180. URL: http://nbuv.gov.ua/UJRN/ aktit_2006_9_35 [in English].
20. Kovalenko, A. A., Kuchuk, G. A. & Mozhaev, A. A. (2010). Postroenie eksponentsialnyih vremennyih shkal pri analize ocheredey multiservisnyih setey. RadIoelektronnI I komp’yuternI sistemi, 7, 257–262. URL: http://nbuv.gov.ua/UJRN/recs_2010_7_52 [in Russian].
GOST Style Citations
- Броек Д.. Основы механики разрушения. Высшая школа, Москва, 1980./ Перевод Дорофеева Виктора Ивановича (Broek D. Elementary engineering fracture mechanics, Лейден, 1974).
- Ушанев К.В. Имитационные модели системы массового обслуживания типа Pa/M/1, H2/M/1 и исследование на их основе качества обслуживания трафика со сложной структурой. Системы управления, связи и безопасности. 2015. №4. С.217-251.
- Добровольский Е.В., Нечипорук О.Л. Моделирование сетевого трафика с использованием контекстных методов. Наукові праці ОНАЗ ім. О.С. Попова. 2005. № 1. С.24-32.
- Семенов С.Г., Мелешко Е.В., Илюшко Я.В. Математическая модель мультисервисного канала связи на основе экспоненциальной GERT-сети. Системи озброєння і військова техніка, ХУПС, 2011. № 3(27). С.64-67.
- Tamara Radivilova, Yousef Ibrahim Daradkeh, Lyudmyla Kirichenko. Development of QoS Methods in the Information Networks with Fractal Traffic. International Journal of Electronics and Telecommunications. 2018. 64 (1). Р. 27-32.
- Mahdi Barat, Zadeh Joveini, Javad Sadri, Hoda Alavi Khoushhal. Fractal Modeling of Big Data Networks. International Conference on Pattern Recognition and Artificial Intelligence (ICPRAI 2018). Canada, Montreal: Concordia University. 2018. Р. 1-4.
- Jiang D., Huo L., Li Y. Fine-granularity inference and estimations to network traffic for SDN. PLoS ONE 2018, 13(5). Doi.org/10.1371/journal.pone.0194302.
- Bulakh V., Kirichenko L., Radivilova T. Time Series Classification Based on Fractal Properties. International Conference on Data Stream Mining & Processing (DSMP): іn Proceedings of the 2018 IEEE Second, 21–25 August 2018, Lviv, 2018. Р. 198–201. Doi:10.1109/DSMP.2018.8478532.
- Youri Raaijmakers, Hansjoerg Albrecher, Onno Boxma. The single server queue with mixing dependencies, 2017. February 6. (http://www.hec.unil.ch/halbrech_files/QueueMixing.pdf).
- Xie K., Peng C, Wang X., Xie G., Wen J. Accurate recovery of internet traffic data under dynamic measurements, in Proc. of INFOCOM’17. 2017. Р. 1–9.
- Wang C, Maguluri S T, Javidi T. Heavy traffic queue length behavior in switches with reconfiguration delay, in Proc. of INFOCOM’17. 2017. Р. 1–9.
- Xie G, Xie K, Huang J, Wang X, Chen Y., Wen J. Fast low-rank matrix approximation with locality sensitive hashing for quick anomaly detection, in Proc. of INFOCOM’17. 2017. Р. 1–9.
- Tatamikova Tatiana Mikhailovna, Kutuzov Oleg Ivanovich. Evaluation and comparison of classical and fractal queuing systems. XV International Symposium Problems of Redundancy in Information and Control Systems. 2016. Р.155 – 157.
- Czarkowski Michał, Kaczmarek Sylwester, Wolff Maciej. Influence of Self -Similar Traffic Type on Perform ance of QoS Routing Algorithms. INTL Journal of electronics and telecommunications. 2016. Vol. 62, no. 1. Р. 81-87.
- Lakhmi Priya Das, Sanjay Kumar Patra, Sarojananda Mishra. Impact of hurst parameter value in self-similarity behaviour of network traffic. International Journal of Research in Computer and Communication Technology. 2016. Vol 5, no 12. P.631-633.
- Hae-Duck Joshua Jeong. Modelling of self-similar teletraffic for simulation. University of Canterbury. July 2002. 270 p.
- Кучук Г. А, О., Можаєв О., Воробйов О. В. Метод прогнозування фрактального трафіка. Радіоелектронні і комп’ютерні системи. 2006. №6,.С. 181–188. URL: http://nbuv.gov.ua/ UJRN/recs_2006_6_34.
- Кучук Г. А, Можаєв О. О., Воробйов О. В. Прогнозирование трафика для управления перегрузками интегрированной телекоммуникационной сети. Радіоелектронні і комп’ютерні системи. 2007. № 8. С. 261–271. URL: http://nbuv.gov.ua/UJRN/recs_2007_8_48.
- Кучук Г. А., Можаєв О. О., Воробйов О. В. Аналіз та моделі самоподібного трафіка. Авиационно-космическая техника и технология. 2006. № 9. С. 173–180. URL: http://nbuv.gov.ua/UJRN/ aktit_2006_9_35.
- Коваленко А. А., Кучук Г. А., Можаев А. А. Построение экспоненциальных временных шкал при анализе очередей мультисервисных сетей. Радіоелектронні і комп’ютерні системи. 2010. № 7. С. 257–262. URL: http://nbuv.gov.ua/UJRN/recs_2010_7_52.
Copyright (c) 2019 Hanna Drieieva, Oleksii Smirnov, Oleksandr Drieiev
The Method of Generating a Fractally Similar Numerical Sequence Based on a Finite Automaton for Modeling Traffic in a Network
About the Authors
Hanna Drieieva, teacher, Central Ukraіnian National Technical University, Kropyvnytskyi, Ukraine
Oleksii Smirnov, Professor, Doctor in Technics (Doctor of Technics Sciences), Central Ukraіnian National Technical University, Kropyvnytskyi, Ukraine
Oleksandr Drieiev, Associate Professor, PhD in Technics (Candidate of Technics Sciences), Central Ukraіnian National Technical University, Kropyvnytskyi, Ukraine
Abstract
Keywords
Full Text:
PDFReferences
1. Broek, D. (1980). Osnovyi mehaniki razrusheniya. Moskva: Vyisshaya shkola. Perevod Dorofeeva Viktora Ivanovicha. (1974). Leyden: Elementary engineering fracture mechanics [in Russian].
2. Ushanev, K.V. (2015). Imitatsionnyie modeli sistemyi massovogo obsluzhivaniya tipa Pa/M/1, H2/M/1 i issledovanie na ih osnove kachestva obsluzhivaniya trafika so slozhnoy strukturoy. Sistemyi upravleniya, svyazi i bezopasnosti, 4, 217-251 [in Russian].
3. Dobrovolskiy, E.V. & Nechiporuk, O.L. (2005). Modelirovanie setevogo trafika s ispolzovaniem kontekstnyih metodov. NaukovI pratsI ONAZ Im. O.S. Popova, 1, 24-32 [in Russian].
4. Semenov, S.G., Meleshko, E.V. & Ilyushko, Ya.V. (2011). Matematicheskaya model multiservisnogo kanala svyazi na osnove eksponentsialnoy GERT-seti. Sistemi ozbroEnnya I vIyskova tehnIka, HUPS, 3(27), 64-67 [in Russian].
5. Tamara Radivilova, Yousef Ibrahim Daradkeh & Lyudmyla Kirichenko. (2018). Development of QoS Methods in the Information Networks with Fractal Traffic. International Journal of Electronics and Telecommunications, 64 (1), 27-32 [in English].
6. Mahdi Barat, Zadeh Joveini, Javad Sadri & Hoda Alavi Khoushhal (2018). Fractal Modeling of Big Data Networks. International Conference on Pattern Recognition and Artificial Intelligence (ICPRAI 2018). Canada, Montreal: Concordia University, 1-4 [in English].
7. Jiang, D., Huo, L. & Li, Y. (2018). Fine-granularity inference and estimations to network traffic for SDN. PLoS ONE, 13(5). Doi.org/10.1371/journal.pone.0194302 [in English].
8. Bulakh, V., Kirichenko, L. & Radivilova, T. (2018). Time Series Classification Based on Fractal Properties. International Conference on Data Stream Mining & Processing (DSMP): іn Proceedings of the 2018 IEEE Second, 21–25 August. Lviv. (рр. 198–201). Doi:10.1109/DSMP.2018.8478532 [in English].
9. Youri Raaijmakers, Hansjoerg Albrecher & Onno Boxma. (2017) The single server queue with mixing dependencies February 6. (http://www.hec.unil.ch/halbrech_files/QueueMixing.pdf) [in English].
10. Xie, K., Peng, C, Wang, X., Xie, G. & Wen, J. (2017). Accurate recovery of internet traffic data under dynamic measurements, in Proc. of INFOCOM’17, (рр. 1–9) [in English].
11. Wang, C, Maguluri, S T & Javidi, T. (2017). Heavy traffic queue length behavior in switches with reconfiguration delay, in Proc. of INFOCOM’17, (рр. 1–9) [in English].
12. Xie, G, Xie, K, Huang, J, Wang, X, Chen, Y. & Wen, J. (2017). Fast low-rank matrix approximation with locality sensitive hashing for quick anomaly detection, in Proc. of INFOCOM’17, (рр. 1–9) [in English].
13. Tatamikova Tatiana Mikhailovna & Kutuzov Oleg Ivanovich. (2016). Evaluation and comparison of classical and fractal queuing systems. XV International Symposium Problems of Redundancy in Information and Control Systems, 155 – 157 [in English].
14. Czarkowski Michał, Kaczmarek Sylwester & Wolff Maciej. (2016). Influence of Self -Similar Traffic Type on Perform ance of QoS Routing Algorithms. INTL Journal of electronics and telecommunications, Vol. 62, 1, 81-87 [in English].
15. Lakhmi Priya Das, Sanjay Kumar Patra (2016). Sarojananda Mishra. Impact of hurst parameter value in self-similarity behaviour of network traffic. International Journal of Research in Computer and Communication Technology, Vol 5, 12, 631-633 [in English].
16. Hae-Duck Joshua Jeong. (2002). Modelling of self-similar teletraffic for simulation. University of Canterbury [in English].
17. Kuchuk, G. A, MozhaEv, O. O. & Vorobyov, O. V. (2006). Metod prognozuvannya fraktalnogo trafIka. RadIoelektronnI I komp’yuternI sistemi, 6, 181–188. URL: http://nbuv.gov.ua/UJRN/recs_2006_6_34 [in English].
18. Kuchuk, G. A, MozhaEv, O. O. & Vorobyov, O. V. (2007). Prognozirovanie trafika dlya upravleniya peregruzkami integrirovannoy telekommunikatsionnoy seti. RadIoelektronnI I komp’yuternI sistemi, 8, 261–271. URL: http://nbuv.gov.ua/UJRN/recs_2007_8_48 [in Russian].
19. Kuchuk, G. A., MozhaEv, O. O. & Vorobyov, O. V. (2006). AnalIz ta modelI samopodIbnogo trafIka. Aviatsionno-kosmicheskaya tehnika i tehnologiya, 9, 173–180. URL: http://nbuv.gov.ua/UJRN/ aktit_2006_9_35 [in English].
20. Kovalenko, A. A., Kuchuk, G. A. & Mozhaev, A. A. (2010). Postroenie eksponentsialnyih vremennyih shkal pri analize ocheredey multiservisnyih setey. RadIoelektronnI I komp’yuternI sistemi, 7, 257–262. URL: http://nbuv.gov.ua/UJRN/recs_2010_7_52 [in Russian].
GOST Style Citations
- Броек Д.. Основы механики разрушения. Высшая школа, Москва, 1980./ Перевод Дорофеева Виктора Ивановича (Broek D. Elementary engineering fracture mechanics, Лейден, 1974).
- Ушанев К.В. Имитационные модели системы массового обслуживания типа Pa/M/1, H2/M/1 и исследование на их основе качества обслуживания трафика со сложной структурой. Системы управления, связи и безопасности. 2015. №4. С.217-251.
- Добровольский Е.В., Нечипорук О.Л. Моделирование сетевого трафика с использованием контекстных методов. Наукові праці ОНАЗ ім. О.С. Попова. 2005. № 1. С.24-32.
- Семенов С.Г., Мелешко Е.В., Илюшко Я.В. Математическая модель мультисервисного канала связи на основе экспоненциальной GERT-сети. Системи озброєння і військова техніка, ХУПС, 2011. № 3(27). С.64-67.
- Tamara Radivilova, Yousef Ibrahim Daradkeh, Lyudmyla Kirichenko. Development of QoS Methods in the Information Networks with Fractal Traffic. International Journal of Electronics and Telecommunications. 2018. 64 (1). Р. 27-32.
- Mahdi Barat, Zadeh Joveini, Javad Sadri, Hoda Alavi Khoushhal. Fractal Modeling of Big Data Networks. International Conference on Pattern Recognition and Artificial Intelligence (ICPRAI 2018). Canada, Montreal: Concordia University. 2018. Р. 1-4.
- Jiang D., Huo L., Li Y. Fine-granularity inference and estimations to network traffic for SDN. PLoS ONE 2018, 13(5). Doi.org/10.1371/journal.pone.0194302.
- Bulakh V., Kirichenko L., Radivilova T. Time Series Classification Based on Fractal Properties. International Conference on Data Stream Mining & Processing (DSMP): іn Proceedings of the 2018 IEEE Second, 21–25 August 2018, Lviv, 2018. Р. 198–201. Doi:10.1109/DSMP.2018.8478532.
- Youri Raaijmakers, Hansjoerg Albrecher, Onno Boxma. The single server queue with mixing dependencies, 2017. February 6. (http://www.hec.unil.ch/halbrech_files/QueueMixing.pdf).
- Xie K., Peng C, Wang X., Xie G., Wen J. Accurate recovery of internet traffic data under dynamic measurements, in Proc. of INFOCOM’17. 2017. Р. 1–9.
- Wang C, Maguluri S T, Javidi T. Heavy traffic queue length behavior in switches with reconfiguration delay, in Proc. of INFOCOM’17. 2017. Р. 1–9.
- Xie G, Xie K, Huang J, Wang X, Chen Y., Wen J. Fast low-rank matrix approximation with locality sensitive hashing for quick anomaly detection, in Proc. of INFOCOM’17. 2017. Р. 1–9.
- Tatamikova Tatiana Mikhailovna, Kutuzov Oleg Ivanovich. Evaluation and comparison of classical and fractal queuing systems. XV International Symposium Problems of Redundancy in Information and Control Systems. 2016. Р.155 – 157.
- Czarkowski Michał, Kaczmarek Sylwester, Wolff Maciej. Influence of Self -Similar Traffic Type on Perform ance of QoS Routing Algorithms. INTL Journal of electronics and telecommunications. 2016. Vol. 62, no. 1. Р. 81-87.
- Lakhmi Priya Das, Sanjay Kumar Patra, Sarojananda Mishra. Impact of hurst parameter value in self-similarity behaviour of network traffic. International Journal of Research in Computer and Communication Technology. 2016. Vol 5, no 12. P.631-633.
- Hae-Duck Joshua Jeong. Modelling of self-similar teletraffic for simulation. University of Canterbury. July 2002. 270 p.
- Кучук Г. А, О., Можаєв О., Воробйов О. В. Метод прогнозування фрактального трафіка. Радіоелектронні і комп’ютерні системи. 2006. №6,.С. 181–188. URL: http://nbuv.gov.ua/ UJRN/recs_2006_6_34.
- Кучук Г. А, Можаєв О. О., Воробйов О. В. Прогнозирование трафика для управления перегрузками интегрированной телекоммуникационной сети. Радіоелектронні і комп’ютерні системи. 2007. № 8. С. 261–271. URL: http://nbuv.gov.ua/UJRN/recs_2007_8_48.
- Кучук Г. А., Можаєв О. О., Воробйов О. В. Аналіз та моделі самоподібного трафіка. Авиационно-космическая техника и технология. 2006. № 9. С. 173–180. URL: http://nbuv.gov.ua/UJRN/ aktit_2006_9_35.
- Коваленко А. А., Кучук Г. А., Можаев А. А. Построение экспоненциальных временных шкал при анализе очередей мультисервисных сетей. Радіоелектронні і комп’ютерні системи. 2010. № 7. С. 257–262. URL: http://nbuv.gov.ua/UJRN/recs_2010_7_52.