Performance Analysis on Transmission Estimation for Avionics Real-Time System Using Optimized Network Calculus

  • Qingfei XuEmail author
  • Xinyu Yang
Original Paper


With growing avionics applications, information transmission has been increasing in real-time systems of airplanes; AFDX (Avionics Full Duplex Switched Ethernet) standardized as ARINC 664 is widely used in avionics transmission systems for its high speed and duplex characteristics. For safety–critical systems such as flight control system, the time limit of information transmission should be deterministic and it is used to evaluate the performance of the real-time systems. Network Calculus is a powerful approach used to estimate the performance of avionics real-time system by calculating the possible longest transmission time, but the performance analysis is usually pessimistic for the pessimistic assumptions in the computation. Grouping strategy can improve that analysis by considering serialization effect of information transmitting from the same physical link, but the analysis of system performance is still pessimistic. In this paper, we explore the pessimism in Network Calculus and Grouping strategy, and then propose a Rate-constrained grouping strategy to improve the analysis of system performance. The experiments on both sample avionics system and real industrial system confirm the validity and applicability of the performance analysis.


Avionics Real-time system Network Calculus Rated-constrained Grouping Strategy 



Maximum transmission speed per physical link, in Mbit/s


Max (0, x)


Technological switch latency


Bandwidth allocation gap


Virtual link


The x-coordinate (time) of inflection point


Arrival curve


Burst of arrival curve


Transmission rate of arrival curve


Service curve of a witch


Waiting time when passing a switch



This work was supported by RPDU Project of Commercial Aircraft Corporation of China Ltd and Aviation Industry Corporation Shanghai Aviation Electric Co.,Ltd, besides, TTTech Computertechnik AG provided much help about Time-Triggered network.


  1. 1.
    Baga Y, Ghaffari F, Zante E, Nahmiyace M, Declercq D (2016) Worst frame backlog estimation in an avionics full-duplex switched ethernet end-system. In: IEEE/AIAA 35th digital avionics systems conference (DASC), SacramentoGoogle Scholar
  2. 2.
    ARINC Specification 664 (2005) Aircraft data network, Part 7. Aeronotical Radio Inc., Annapolis (Tech. Rep) Google Scholar
  3. 3.
    Jean-Yves B, Patrick T (2012) Network Calculus: a theory of deterministic queuing systems for the internet, 1st edn. Springer.
  4. 4.
    Cruz L (1991) A calculus for network delay, part I, network elements in isolation. IEEE Trans Inf Theory 37:114–141MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Luxi Z, Qiao L, Ying Z, Zhong Z, Huagang Z (2013) Using multi-link grouping technique to achieve tight latency in Network Calculus. In: digital avionics systems conference (DASC), IEEE/AIAA 32nd, Oct. 2013.Google Scholar
  6. 6.
    Hernri B, Jean-Luc S, Christan F (2010) Improving the worst-case delay analysis of an AFDX network using an optimized trajectory approach. IEEE Trans Ind Inf 6(4):521–533CrossRefGoogle Scholar
  7. 7.
    Feng H, Ershuai L (2017) Deterministic bound for avionics switched networks according to networking features using network calculus. Chinese J Aeronaut 30:1941–1957CrossRefGoogle Scholar
  8. 8.
    Muhammad A, Jean-Luc S, Jérôme E (2010) Model for worst case delay analysis of an AFDX network using timed automata. In: IEEE conference on emerging technologies and factory automation (ETFA)Google Scholar
  9. 9.
    Jérôme E, Christian F (2013) Modeling a spacewire architecture using timed automata to compute worst-case end-to-end delays. In: IEEE 18th conference on emerging technologies and factory automation (ETFA)Google Scholar
  10. 10.
    Hussein C, Jean-Luc S, Jerome E, Christan F (2006) Methods for bounding end-to-end delays on an AFDX network. In: proceedings of the 18th Euromicro conference on real-time systemsGoogle Scholar
  11. 11.
    Ning H, Tangqi L, Ning H (2011) Applying trajectory approach for computing worst-case end-to-end delays on an AFDX network. Procedia Eng 15:2555–2560CrossRefGoogle Scholar
  12. 12.
    Henri B, Jean-Luc S, Christan F (2009) Applying and optimizing Trajectory approach for performance evaluation of AFDX avionics network. In: IEEE conference on emerging technologies and factory automation.Google Scholar
  13. 13.
    Li Meng, Guchuan Z, Yvon S, Michael L (2017) Reliability Enhancement of Redundency management in AFDX Networks. IEEE Trans Ind Inf 13:2118–2129CrossRefGoogle Scholar
  14. 14.
    Aakash S, Xiaoting Li, Jean-Luc S., Christan F (2017) Working in progess paper: pessimism analysis of Network Calculus approach on AFDX networks. In: IEEE International symposium on industrial embedded systemsGoogle Scholar
  15. 15.
    Ashjaei Mohammad, Behnam Moris, Nolte Thomas (2016) A modular simulation tool for switched etnernet networks. J Syst Archit 65:1–14CrossRefGoogle Scholar
  16. 16.
    Li Jian, Yao Jianguo, Huang Dongshan (2015) Ethernet-based avionic databus and time-space partition switch design. J Commun Netw 17:286–295CrossRefGoogle Scholar
  17. 17.
    Kemayo G, Benammar N, Ridouard F, Bauer H, Richard P (2015) Improving AFDX end-to-end delay analysis. In: IEEE 20th conference on emerging technologies and factory automation (ETFA)Google Scholar

Copyright information

© The Korean Society for Aeronautical & Space Sciences 2019

Authors and Affiliations

  1. 1.School of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’anChina

Personalised recommendations