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Development of amodified method of network traffic forming

Treść / Zawartość
Identyfikatory
Warianty tytułu
PL
Opracowanie zmodyfikowanej metody formowania ruchu sieciowego
Języki publikacji
EN
Abstrakty
EN
Based on the analysis of the statistical characteristics of heterogeneous network traffic of "Quadruple Play" mobile subscribers, it is shown that it cannot be represented by Poisson or Erlang distributions. It is shown that for such similar traffic, the rate of growth of the required buffer volume increases as the Hurst parameter increases. A method of adaptive traffic formation using the control of the length of intervals of the intensity of the arrival of data packets has been developed. Congestion management is carried out by changing the frequency of the marker generator (GM) on the basis of the results of forecasting the required bandwidth of the system and the required buffer size. The shaper adapts to changes in the length and instantaneous intensity of packet input in real-time.
PL
Na podstawie analizy charakterystyk statystycznych heterogenicznego ruchu sieciowego abonentów telefonii komórkowej "Quadruple Play" wykazano, że nie może on być reprezentowany przez rozkłady Poissona lub Erlanga. Pokazano, że dla takiego podobnego ruchu tempo wzrostu wymaganej objętości bufora wzrasta wraz ze wzrostem parametru Hursta. Opracowano metodę adaptacyjnego kształtowania ruchu wykorzystującą sterowanie długością interwałów natężenia napływu pakietów danych. Zarządzanie ograniczeniami odbywa się poprzez zmianę częstotliwości generatora znaczników (GM) na podstawie wyników prognozowania wymaganej przepustowości systemu oraz wymaganej wielkości bufora. Shaper dostosowuje się do zmian długości i chwilowej intensywności wprowadzanych pakietów w czasie rzeczywistym.
Rocznik
Strony
50--53
Opis fizyczny
Bibliogr. 19 poz., wykr.
Twórcy
  • National Aviation University, Faculty of Cybernetics, Computer and Software Engineering, Department of Information Security, Kyiv, Ukraine
  • Lutsk National Technical University, Faculty of Computer and Information Technologies, Department of Electronics and Telecommunications, Lutsk, Ukraine
  • Lutsk National Technical University, Faculty of Computer and Information Technologies, Department of Electronics and Telecommunications, Lutsk, Ukraine
autor
  • Lutsk National Technical University, Faculty of Computer and Information Technologies, Department of Electronics and Telecommunications, Lutsk, Ukraine
  • Lutsk National Technical University, Faculty of Computer and Information Technologies, Department of Electronics and Telecommunications, Lutsk, Ukraine
Bibliografia
  • [1] Barreiros M., Lundqvist P.: Policing and Shaping. QOS-Enabled Networks: Tools and Foundations. Wiley, 2015 [http://doi.org/10.1002/9781119109136.ch6].
  • [2] Chakraborty T. et al.: Searching for Heavy-Tailed Probability Distributions for Modeling Real-World Complex Networks. IEEE Access 10, 2022, 115092-115107 [http://doi.org/10.1109/ACCESS.2022.3218631].
  • [3] Chang C. S. et al.: SDL Constructions of FIFO, LIFO and Absolute Contractors. IEEE INFOCOM 2009, 738–746 [http://doi.org/10.1109/INFCOM.2009.5061982].
  • [4] Farhadi V. et al.: Service Placement and Request Scheduling for Data-intensive Applications in Edge Clouds. IEEE Conference on Computer Communications – INFOCOM 2019, 1279–1287 [http://doi.org/10.1109/INFOCOM.2019.8737368].
  • [5] Han C. et al.: Analytical Study of the IEEE 802.11p MAC Sublayer in Vehicular Networks. IEEE Transactions on Intelligent Transportation Systems 13(2), 2012, 873–886 [http://doi.org/10.1109/TITS.2012.2183366].
  • [6] He J. et al.: Buffer-Aided Relaying for Two-Hop Secure Communication with Limited Packet Lifetime. IEEE 20th International Conference on High Performance Switching and Routing (HPSR), 2019, 1–7 [http://doi.org/10.1109/HPSR.2019.8807995].
  • [7] Kirichenko L. et al.: Classification of Fractal Time Series Using Recurrence Plots. International Scientific-Practical Conference Problems of Infocommunications, Science and Technology (PIC S&T), Kharkiv, 2018, 719–724 [http://doi.org/10.1109/INFOCOMMST.2018.8632010].
  • [8] Kotenko I. et al.: A technique for early detection of cyberattacks using the traffic self-similarity property and a statistical approach. 29th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), Valladolid, 2021, 281–284 [http://doi.org/10.1109/PDP52278.2021.00052].
  • [9] Li Z. et al.: Detecting Saturation Attacks Based on Self-Similarity of OpenFlow Traffic. IEEE Transactions on Network and Service Management 17(1), 2020, 607–621 [http://doi.org/10.1109/TNSM.2019.2959268].
  • [10] Millán G.: Proposal of an Estimator of the Hurst Parameter for a Self-similar Process Representative of the Degree of Randomness of the Recorded Traffic in IEEE 802.3-2005 Networks. OSF Preprints. 2021 [http://doi.org/10.31219/osf.io/pgbvm].
  • [11] Moltafet M. et al.: Average Age of Information for a Multi-Source M/M/1 Queueing Model With Packet Management. IEEE International Symposium on Information Theory (ISIT), Los Angeles, 2020, 1765–1769 [http://doi.org/10.1109/ISIT44484.2020.9174099].
  • [12] Patilkulkarni S. et al.: Programmable Delay and Variable Bit Rate Enabled Video Streaming using C++. Third International Conference on Advances in Electronics, Computers and Communications (ICAECC), Bengaluru, 2020, 1–7 [http://doi.org/10.1109/ICAECC50550.2020.9339499].
  • [13] Plevyak T., Sahin V.: Management of Triple/Quadruple Play Services from a Telecom Perspective. Next Generation Telecommunications Networks, Services, and Management, 2010, 15–52 [http://doi.org/10.1002/9780470594025.ch2].
  • [14] Ren D. et al.: Adaptive Request Scheduling and Service Caching for MEC-Assisted IoT Networks: An Online Learning Approach. IEEE Internet of Things Journal 9(18), 2022, 17372–17386 [http://doi.org/10.1109/JIOT.2022.3157677].
  • [15] Serinaldi F.: Use and misuse of some Hurst parameter estimators applied to stationary and non-stationary financial time series. Physica A: Statistical Mechanics and its Applications 389(14), 2010, 2770–2781 [http://doi.org/10.1016/j.physa.2010.02.044].
  • [16] Wang W. et al.: Software architecture based on message queue. 6th International Conference on Information Science, Computer Technology and Transportation – ISCTT, Xishuangbanna, 2021, 1–5.
  • [17] Yakymchuk N. et al.: Monitoring of Link-Level Congestion in Telecommunication Systems Using Information Criteria. Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska – IAPGOS 12(4), 2022, 26–30 [http://doi.org/10.35784/iapgos.3076].
  • [18] Zablotskyi V. et al.: Method for Evaluation Quality Parameters of Telecommunications Services. Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska – IAPGOS 12(2), 2022, 30–33 [http://doi.org/10.35784/iapgos.2918].
  • [19] Zhang Y. et al.: Recent Advances on HEVC Inter-Frame Coding: From Optimization to Implementation and Beyond. IEEE Transactions on Circuits and Systems for Video Technology 30(11), 2020, 4321–4339 [http://doi.org/10.1109/TCSVT.2019.2954474].
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a1cd5ffd-d64c-4444-b4de-ae58454621b1
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