PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

A delay reliability estimation method for Avionics Full Duplex Switched Ethernet based on stochastic network calculus

Autorzy
Treść / Zawartość
Identyfikatory
Warianty tytułu
PL
Oparta na stochastycznym rachunku sieciowym metoda estymacji niezawodności czasu transmisji dla przełączanej pokładowej sieci ethernetowej typu AFDX umożliwiającej równoczesną transmisję dwukierunkową
Języki publikacji
EN
Abstrakty
EN
The delay reliability estimation is required in order to guarantee the real-time communication for avionics full duplex switched Ethernet (AFDX). Stochastic network calculus (SNC) can be applied to estimate the reliability with a delay upper bound. However, only linear deterministic traffic envelope function is used to bound its traffic, which cannot represent the traffic randomness and is far from practice. In this paper, a stochastic traffic envelope function, which randomizes the input of SNC, is proposed to solve the problem. A new probabilistic algorithm is derived to estimate the delay reliability based on stochastic envelope functions. A test was conducted to demonstrate our method on an AFDX testbed, and the test results verify that the estimation of delay reliability via our algorithm is much closer to the empirical estimation.
PL
Ocena niezawodności czasu transmisji (czasu opóźnienia) jest niezbędną procedurą gwarantującą komunikację w czasie rzeczywistym za pośrednictwem przełączanej pokładowej sieci ethernetowej typu AFDX (Avionics Full Duplex Switched Ethernet), która umożliwia równoczesną transmisję dwukierunkową. Stochastyczny rachunek sieciowy (SNC) można stosować do oceny niezawodności przy zadanej górnej granicy opóźnienia. Do tej pory jednak, do ograniczania ruchu telekomunikacyjnego stosowano tylko liniową deterministyczną funkcję obwiedni (traffic envelope), która nie oddaje losowości ruchu telekomunikacyjnego i odbiega dalece od rzeczywistości. W niniejszej pracy zaproponowano rozwiązanie tego problemu wykorzystujące stochastyczną funkcję obwiedni ruchu telekomunikacyjnego. Wyprowadzono nowy algorytm probabilistyczny, który pozwala ocenić niezawodność czasu transmisji na podstawie funkcji obwiedni. Przeprowadzono badanie, w ramach którego testowano zaproponowaną metodę w środowisku testowym AFDX; wyniki testu pokazują, że ocena niezawodności czasu transmisji z wykorzystaniem zaproponowanego przez nas algorytmu jest znacznie bardziej zbliżona do estymacji empirycznej.
Rocznik
Strony
288--296
Opis fizyczny
Bibliogr. 39 poz., rys., tab.
Twórcy
autor
  • School of Reliability and Systems Engineering, Beihang University No.37, Xueyuan Road, Haidian District, Beijing, 100191, China Science and Technology on Reliability and Environmental Engineering Laboratory No. 37, Xueyuan Road, Haidian District Beijing, 100191, China
autor
  • School of Reliability and Systems Engineering, Beihang University No.37, Xueyuan Road, Haidian District, Beijing, 100191, China Science and Technology on Reliability and Environmental Engineering Laboratory No. 37, Xueyuan Road, Haidian District Beijing, 100191, China
autor
  • School of Reliability and Systems Engineering, Beihang University No.37, Xueyuan Road, Haidian District, Beijing, 100191, China Science and Technology on Reliability and Environmental Engineering Laboratory No. 37, Xueyuan Road, Haidian District Beijing, 100191, China
autor
  • School of Reliability and Systems Engineering, Beihang University No.37, Xueyuan Road, Haidian District, Beijing, 100191, China Science and Technology on Reliability and Environmental Engineering Laboratory No. 37, Xueyuan Road, Haidian District Beijing, 100191, China
Bibliografia
  • 1. Abdrabou A, Liang B, Zhuang W H. Delay Analysis for Sparse Vehicular Sensor Networks with Reliability Considerations. IEEE Transactions on Wireless Communications 2013; 12: 4402-4413, http://dx.doi.org/10.1109/TW.2013.072313.121397.
  • 2. Addie, R G, Neame T D, Zukerman M. Performance evaluation of a queue fed by a Poisson Pareto burst process', Computer Networks-the International Journal Of Computer And Telecommunications Networking 2002; 40(3): 377-397.
  • 3. AIM GmbH. The AIM's AFDX/ ARINC664P7. http://www.aim-online.com/index.aspx (accessed 10 Dec 2013).
  • 4. ARINC. ARINC 664 Part 7:Aircraft Data Network-Deterministic Networks. 2003.
  • 5. Asakura Y, Kashiwadani M. Road network reliability caused by daily fluctuation of traffc flow. in: Proceedings of Seminar G Held at the PTRC Transport, Highways and Planning Summer Annual Meeting, University of Sussex, United Kingdom, 1991:73–84.
  • 6. Ball M O, Colbourn C J, Provan J S. Network reliability. Handbooks in operations research and management science. 1995; 7: 673-762, http://dx.doi.org/10.1016/S0927-0507(05)80128-8.
  • 7. Clegg R G, Di Cairano-Gilfedder C, Zhou S. A critical look at power law modelling of the Internet. Computer Communications 2010; 33: 259-268, http://dx.doi.org/10.1016/j.comcom.2009.09.009.
  • 8. Duffield N G, O'connell N. Large deviations and overflow probabilities for the general single-server queue. Mathematical Proceedings of the Cambridge Philosophical Society with applications, Cambridge Univ Press, 1995:363-374.
  • 9. Erramilli A, Roughan M, Veitch D, Willinger W. Self-similar traffic and network dynamics. Proceedings of the IEEE 2002; 90: 800-819, http://dx.doi.org/10.1109/JPROC.2002.1015008.
  • 10. Field T, Harder U, Harrison P. Network traffic behaviour in switched Ethernet systems. Performance Evaluation 2004; 58(2-3): 243-260, http://dx.doi.org/10.1016/j.peva.2004.07.017.
  • 11. Fras M, Mohorko J, Cucej Z. Modeling of measured self-similar network traffic in OPNET simulation tool. Informacije Midem-Journal Of Microelectronics Electronic Components And Materials 2010; 40(3): 224-231.
  • 12. Fras M, Mohorko J, Cucej Z. Limitations of a Mapping Algorithm with Fragmentation Mimics (MAFM) when modeling statistical data sources based on measured packet network traffic. Computer Networks 2013; 57(17): 3686-3700, http://dx.doi.org/10.1016/j.comnet.2013.07.032.
  • 13. Ha S, Le L, Rhee I, Xu L. Impact of background traffic on performance of high-speed TCP variant protocols. Computer Networks 2007; 51: 1748-1762, http://dx.doi.org/10.1016/j.comnet.2006.11.005.
  • 14. Huang N, Hou D, Chen Y, Xing L D, Kang R. A network reliability evaluation method based on applications and topological structure. Eksploatacja I Niezawodnosc-Maintenance and Reliability 2011; 3: 77-83.
  • 15. Jiang Y M. A basic Stochastic network calculus. Computer Communication Review 2006; 36: 123-134, http://dx.doi.org/10.1145/1151659.1159929.
  • 16. Jiang Y M, Liu Y. Stochastic network calculus, Springer, 2008.
  • 17. Jiang Y M, Yin Q, Liu Y, Jiang S. Fundamental calculus on generalized stochastically bounded bursty traffic for communication networks. Computer Networks 2009; 53: 2011-2021, http://dx.doi.org/10.1016/j.comnet.2009.03.004.
  • 18. Jin P Y, Tanaka S. Reliability Evaluation of a Network with Delay. IEEE Transactions on Reliability 1979; R-28:320-324, http://dx.doi.org/10.1109/TR.1979.5220618.
  • 19. Karagiannis T, Molle M, Faloutsos. Long-range dependence ten years of Internet traffic modeling. IEEE on Internet Computing 2004; 8: 57-64, http://dx.doi.org/10.1109/MIC.2004.46.
  • 20. Karagiannis T. The SELFIS Tool, 2002. http://alumni.cs.ucr.edu/tkarag/Selfis/Selfis.html(accessed10Dec2013).
  • 21. Leland W E, Taqqu M S, Willinger W, Wilson D V. On the self-similar nature of Ethernet traffic. ACM SIGCOMM Computer Communication Review 1993: 183-193, http://dx.doi.org/10.1145/167954.166255.
  • 22. Li R Y, Huang N, Kang R. Modeling and simulation for network transmission time reliability. Reliability and Maintainability Symposium (RAMS) 2010: 1-6.
  • 23. Liu C, Wang T, Zhao C, Xiong H G. Worst-case flow model of VL for worst-case delay analysis of AFDX. Electronics Letters 2012; 48: 327-328, http://dx.doi.org/10.1049/el.2011.4028.
  • 24. Liu Y, Tham C K, Jiang Y M. A calculus for stochastic QoS analysis. Performance Evaluation 2007; 64: 547-572, http://dx.doi.org/10.1016/j.peva.2006.07.003.
  • 25. Ma X M. On the reliability and performance of real-time one-hop broadcast MANETs. Wireless Networks 2011; 17: 1323-1337, http://dx.doi.org/10.1007/s11276-011-0351-x.
  • 26. Meyer J F. Performability evaluation: Where it is and what lies ahead. Computer Performance and Dependability Symposium 1995:334-343.
  • 27. Nadarajah S. Comment on "A general model for long-tailed network traffic approximation". Journal Of Supercomputing 2008; 44(1): 98-101, http://dx.doi.org/10.1007/s11227-007-0150-4.
  • 28. Ramirez-Marquez J E, Coit D W. A Monte-Carlo simulation approach for approximating multi-state two-terminal reliability. Reliability Engineering & System Safety 2005; 87: 253-264, http://dx.doi.org/10.1016/j.ress.2004.05.002.
  • 29. Ridouard F, Scharbarg J L, Fraboul C. Probabilistic upper bounds for heterogeneous flows using a static priority queuing on an AFDX network. IEEE International Conference on Emerging Technologies and Factory Automation(ETFA) 2008: 1220-1227.
  • 30. Rizk A, Fidler M. Non-asymptotic end-to-end performance bounds for networks with long range dependent fBm cross traffic. Computer Networks 2012; 56: 127-141, http://dx.doi.org/10.1016/j.comnet.2011.07.027.
  • 31. Rizk A, Fidler M. Sample path bounds for long memory FBM traffic. Proceedings IEEE INFOCOM 2010:1-5.
  • 32. Scharbarg J L, Ridouard F, Fraboul C. A probabilistic analysis of end-to-end delays on an AFDX avionic network. IEEE Transactions on Industrial Informatics 2009; 5: 38-49, http://dx.doi.org/10.1109/TII.2009.2016085
  • 33. Vaze R. Throughput-delay-reliability tradeoff in ad hoc networks. Proceedings of the 8th International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks (WiOpt) 2010: 459-464.
  • 34. Wang S P, Sun D, Shi J, Tomovic M. Time delay oriented reliability analysis of Avionics Full Duplex Switched Ethernet. 8th IEEE Conference on Industrial Electronics and Applications (ICIEA) 2013: 982-987.
  • 35. Willinger W, Paxson V, Taqqu M S. Self-similarity and heavy tails: Structural modeling of network traffic. A practical guide to heavy tails: statistical techniques and applications 1998; 23: 27-53.
  • 36. Willinger W, Taqqu M S, Sherman R, Wilson D V. Self-similarity through high-variability: statistical analysis of Ethernet LAN traffic at the source level. ACM SIGCOMM Computer Communication Review 1995; 25: 100-113, http://dx.doi.org/10.1145/217391.217418.
  • 37. Yamkhin D, Won Y. Modeling and Analysis of Wireless LAN Traffic. Journal Of Information Science And Engineering 2009; 25(6): 1783-1801.
  • 38. Yao M, Qiu Z, Kwak K. Leaky bucket algorithms in AFDX. Electronics Letters 2009; 45: 543-545, http://dx.doi.org/10.1049/el.2009.1043.
  • 39. Zeng X, Song D. The research on end-to-end delay calculation method for real-time network AFDX. International Conference on Computational Intelligence and Software Engineering(CiSE) 2009:1-4.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-3893d2ae-ce2b-448e-ad89-feff238dccce
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.