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Traffic Modeling in Industrial Ethernet Networks

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Języki publikacji
EN
Abstrakty
EN
This article discusses the traffic types typically used in industrial networks. The authors propose a number of methods of generating traffic that can be used in modeling traffic sources in the networks under consideration. The proposed traffic models have been developed on the basis of the ON/OFF model. The proposed solutions can be applied to model typical traffic types that are used in industrial systems, such as Time-Triggered (TT) traffic, Audio-Video Bridging (AVB) traffic or Best Effort traffic. The article discusses four traffic models with modifications and shows how the proposed models can be used in modeling different traffic types used in industrial networks.
Twórcy
  • Poznan University of Technology, Poznan, Poland
  • Poznan University of Technology, Poznan, Poland
autor
  • Poznan University of Technology, Poznan, Poland
  • Poznan University of Technology, Poznan, Poland
  • Poznan University of Technology, Poznan, Poland
autor
  • Huawei Technologies Co., Ltd., Shenzhen, Guangdong, China
Bibliografia
  • [1] A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, M. Ayyash, ”Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications”, IEEE Communications Surveys & Tutorials, 17(4), 2347– 2376 (2015).
  • [2] L.D. Xu, W. He., and S. Li, ”Internet of Things in Industries: A Survey”, IEEE Transactions on Industrial Informatics, 10(4), 2233–2243 (2014).
  • [3] L. Karbowiak, ”Przemysl 4.0 - jak sie przygotowac na nieuniknione’, Computerworld, 2, 8–11 (2019).
  • [4] Time-Sensitive Networking Task Group, http://www.ieee802.org/1/pages/tsn.html (2019)
  • [5] Time Sensitive etworks for Felxible Manufacturing testbad – Description of Converged Traffic Types, White Paper, Industrial Internet Consortium, No. IIC: WHT:IS3:V1.0:PB:2018017 , 2018
  • [6] H. Bidgoli, ”The Handbook of Computer Networks, Volume 1, Key Concepts, Data Transmission, and Digital and Optical Networks”, Wiley (2007).
  • [7] D. Staehle, K. Leibnitz, and P. Tran-Gia, ”Source Traffic Modeling of Wireless Applications”, Würzburg, Raport no. 261 (2000).
  • [8] B. Mandelbrot and J. Ness, ”Long-run linearity, locally Gaussian processes, H-spectra and infinite variances”, International Economic Review, 10, 82–111 (1969).
  • [9] H. Heffes and D. Lucantoni, ”A Markov Modulated Characterization of Packetized Voice and Data Traffic and Related Statistical Multiplexer Performance”, IEEE Journal on Selected Areas in Communications, 4(6), 856–868 (1986).
  • [10] B. Ryu and A. Elwalid, ”The importance of long-range dependence of VBR video traffic in ATM traffic engineering: Myths and realities”, ACM Computer Communication Review, 26, 3–14 (1996).
  • [11] O. Sheluhin, S. M. Smolskiy, and A. V. Osin, ”Self-Similar Processes in Telecommunications”, Wiley, Chichester, England (2007).
  • [12] W. E. Leland, M. S. Taqqu, and W. Willinger, ”On the self-similar nature of Ethernet traffic (extended version)”, IEEE/ACM Transactions on Networking. 2(1), 1–15 (1994).
  • [13] W. Willinger, M. Taqqu, R. Sherman, and D. Wilson, ”Self-similarity through high variability: Statistical analysis of ethernet LAN traffic at the source level”, IEEE/ACM Transactions on Networking, 5(1-2), 71–86 (1997).
  • [14] K. Park and W. Willinger, ”Self-Similar Network Traffic and Performance Evaluation”, Wiley, New York, NY (2000).
  • [15] H. Hurst and R. Hudson, ”The (mis) Behavior of Markets”, Reward, Basic Books (2004).
  • [16] M. Głąbowski, S. Hanczewski, M. Stasiak, and P. Zwierzykowski, ”Report on traffic modeling in IT/OT”, Huawei (2018).
  • [17] T. Yoshihara, S. Kasahara, and Y. Takahashi, ”Practical time-scale fitting of self-similar traffic with Markov-modulated Poisson process”, Telecommunication Systems, 17(1-2), 185–211 (2001).
  • [18] P. Salvador, R. Valdas, and A. Pacheco, ”Multiscale Fitting Procedure Using Markov Modulated Poisson Process”, Telecommunication Systems, 23(1), 123-148 (2003).
  • [19] R. J. Adler, R. E. Feldman, and M. S. Taqqu, (Eds), ”A Practical Guide to Heavy Tails”, Birkhauser, Boston (1998).
  • [20] P. Doukhan, G. Oppenheim, and M. S. Taqqu, (Eds), ”Theory and Applications of Long Range Dependence”, Birkhauser, Boston (2003).
  • [21] A. Rajabi, ”Resource Provisioning for Web Applications under Time-varyingTraffic”, Phd thesis, University of Waterloo, Ontario, Canada (2016).
  • [22] Hurst, H., ”Long Term Storage Capacity of Reservoirs”, Transactions of the American Society of Civil Engineers, 116, 770–799.
  • [23] H. Heffes, ”A Class of Data Traffic Processes - Covariance Function Characterization and Related Queuing Results”, The Bell System Technical Journal, 59(6), 897–929 (1980).
  • [24] P. Didier, J. Fontaine, ”Results, Insights and best Practices from IIC Testbeds: Time-Sensitive Networking Testbed”, Industrial Interenet Consortium (2017).
  • [25] ”B&R joins Huawei’s OPC UA TSN testbed”, https://www.instrumentation.co.uk (2018)
  • [26] Labs Network Industrie 4.0 e.V. Statute of the Association ”Labs Network Industrie 4.0 e.V.” – Current Version, https://lni40.de (2018)
Uwagi
Authors email addresses : firstname.lastname@put.poznan.pl
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-df0e3998-2f61-425b-810a-1741e3ed7d88
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