Skip to main content
Springer Nature Link
Log in

Complex Real-Time Network Topology Generation Optimization Based on Message Flow Control

  • Conference paper
  • First Online:
Data Science (ICDS 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1179))

Included in the following conference series:

  • 1312 Accesses

Abstract

There are high requirements for real-time performance of some complex systems, such as in-vehicle systems, avionics systems and so on. Large-scale message interaction within these systems constitutes a complex message interaction network, and the topology of the interaction network has a great impact on its real-time performance as different topologies can cause dramatic differences in message transmission delays. Community discovery and topological grouping are the mainly methods for network topology generation. However, these methods cannot directly guarantee real-time performance. This paper proposes a complex real-time network topology generation algorithm based on message flow control, and compares its real-time performance with manually designed network topology based on balanced strategy. Considering that the control mechanism of message flow is the main influencing factor for network real-time performance, frame length and bandwidth allocation gap (BAG) of the message in the network are measured as the influence factors in the process of network topology construction. The nodes in the network are clustered according to the tightness of communication to ensure the real-time performance of the network. Analytic methods are used to verify the real-time performance of network topology. In the detailed comparison process, the queuing strategy of message in the nodes is divided into two cases: First-In-First-Out (FIFO) and Static Priority (SP). The results show that the real-time performance of almost 74% of the message flow in the algorithm generated network topology based on flow control is better than the artificially designed network topology for the two different queuing strategies.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Wang, D., Huang, C., Ju, Z.: Performance optimization of distributed real-time computing system JStorm. In: International Conference on Information Science & Control Engineering, pp. 532–537. IEEE Computer Society, Piscataway (2017)

    Google Scholar 

  2. Nasri, M., Brandenburg, B.B.: A non-preemptive scheduling technique for resource-constrained embedded real-time systems (Outstanding Paper). In: 2017 IEEE Real-Time and Embedded Technology and Applications Symposium, RTAS, pp. 75–86, Piscataway (2017)

    Google Scholar 

  3. Stroe, G., Andrei, I.C., Frunzulica, F.: Analysis of control system responses for aircraft stability and efficient numerical techniques for real-time simulations. In: International Conference on Mathematical Problems in Engineering, pp. 020156 (2017)

    Google Scholar 

  4. Baillieul, J., Antsaklis, P.J.: Control and communication challenges in networked. In: Proceedings of the IEEE on Real-Time Systems, pp. 9–28, Piscataway (2007)

    Google Scholar 

  5. Chen, H.Y., Tsai, J.J.P., Yaodong, B.I.: An event-based real-time logic for the specification and analysis of real-time systems. Int. J. Artif. Intell. Tools 2(1), 71–91 (2013)

    Article  Google Scholar 

  6. Berlian, M.H., Sahputra, T.E. R., Ardi, B.J.W.: Design and implementation of smart environment monitoring and analytics in real-time system framework based on internet of underwater things and big data. In: Electronics Symposium, pp. 403–408 (2017)

    Google Scholar 

  7. Dietrich, C., Wägemann, P., Ulbrich, P.: SysWCET: whole-system response-time analysis for fixed-priority real-time systems (Outstanding Paper). In: IEEE Real-Time and Embedded Technology and Applications Symposium, RTAS, pp. 37–48, Piscataway (2017)

    Google Scholar 

  8. Faucou, S., Pinho, L.M.: Guest editorial: real-time networks and systems. Real-Time Syst. 54(4), 797–799 (2018)

    Article  Google Scholar 

  9. Gan, W.Y., Nan, H.E., De-Yi, L.I.: Community discovery method in networks based on topological potential. J. Softw. 20(8), 2241–2254 (2009)

    Article  Google Scholar 

  10. Lee, Y., Lee, S.: Path selection algorithms for real-time communication. Int. J. High Speed Comput. 11(4), 215–222 (2000)

    Article  Google Scholar 

  11. Coscia, M., Giannotti, F., Pedreschi, D.: A classification for community discovery methods in complex networks. Stat. Anal. Data Min. ASA Data Sci. J. 4(5), 512–546 (2011)

    Article  MathSciNet  Google Scholar 

  12. Lin, C., Ishwar, P., Ding, W.: Node embedding for network community discovery. In: IEEE International Conference on Acoustics, pp. 4129–4133, Piscataway (2017)

    Google Scholar 

  13. Kechris, A.S., Nies, A., Tent, K.: The complexity of topological group isomorphism. J. Symb. Logic 83(3), 1190–1203 (2017)

    Article  MathSciNet  Google Scholar 

  14. Hai-Long, T., Xian-Rong, C., Li, X.: Applicable method of fast network topology generation and partial modification for DTS. Electr. Power Autom. Equip. (2005)

    Google Scholar 

  15. Kriz, P., Maly, F.: Topology discovery in wireless community network. In: International Conference on Circuits, pp. 267–272. Springer, Piscataway (2011)

    Google Scholar 

  16. Wang, C., Huang, N., Bai, Y.: A method of network topology optimization design considering application process characteristic. Mod. Phys. Lett. B 32(07), 1850091 (2018)

    Article  Google Scholar 

  17. Mengnan, H., Zhixiao, W., Jing, H.E.: Hierarchical community discovery algorithm for social network on topology potential. Comput. Eng. Appl. 55(01), 56–63 (2019)

    Google Scholar 

  18. Vdovin, P.M., Kostenko, V.A.: Organizing message transmission in AFDX networks. Program. Comput. Softw. 43(1), 1–12 (2017)

    Article  MathSciNet  Google Scholar 

  19. Annighoefer, B., Reif, C., Thieleck, F.: Network topology optimization for distributed integrated modular avionics. In: IEEE/AIAA 33rd Digital Avionics Systems Conference (DASC), pp. 4A1-1–4A1-12, Piscataway (2014)

    Google Scholar 

  20. Ashjaei, M., Pedreiras, P., Behnam, M.: Response time analysis of multi-hop HaRTES ethernet switch networks. In: 10th IEEE Workshop on Factory Communication Systems (WFCS 2014), pp. 1–10, Piscataway (2014)

    Google Scholar 

  21. Hsieh, P.C., Xi, L., Jian, J.: SysWCET: throughput-optimal scheduling for multi-hop networked transportation systems with switch-over delay. In: Acm International Symposium on Mobile Ad Hoc Networking & Computing, pp. 1–10, Piscataway (2017)

    Google Scholar 

  22. Jiang, Y.: SysWCET: network calculus and queueing theory: two sides of one coin: invited paper. In: International Icst Conference on Performance Evaluation Methodologies & Tools, pp. 37–48 (2009)

    Google Scholar 

  23. Ebina, R., Nakamura, K., Oyanagi, S.: A real-time burst analysis method. Int. J. Artif. Intell. Tools 22(05), 1360009 (2013)

    Article  Google Scholar 

  24. Dai, Z.: The Optimization design and performance comparative analysis of avionics real-time Ethernet networks. Beihang University, Beijing, pp. 6–7(2016)

    Google Scholar 

Download references

Acknowledgement

This paper is supported by National Natural Science Foundation of China (71701020), Defense Research Field Foundation (61403120404), Qinghai Major Science and Technology Project (2017-NK-A4) and Qin Xin Talents Cultivation Program, Beijing Information Science & Technology University (QXTCP C201707).

Author information

Authors and Affiliations

  1. School of Electronics and Information Engineering, Beihang University, Beijing, 100191, China

    Feng He & Zhiyu Wang

  2. School of Information Management, Beijing Information Science and Technology University, Beijing, 100192, China

    Xiaoyan Gu

Authors
  1. Feng He
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhiyu Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaoyan Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaoyan Gu .

Editor information

Editors and Affiliations

  1. Swinburne University of Technology, Melbourne, VIC, Australia

    Jing He

  2. University of Illinois at Chicago, Chicago, USA

    Philip S. Yu

  3. College of Information Science and Technology, University of Nebraska at Omaha, Omaha, NE, USA

    Yong Shi

  4. Research Institute of Extenics and Innovation Methods, Guangdong University of Technology, Guangzhou, China

    Xingsen Li

  5. Ningbo University, Ningbo, China

    Zhijun Xie

  6. Deakin University, Burwood, VIC, Australia

    Guangyan Huang

  7. Department of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, China

    Jie Cao

  8. Nanjing University of Posts and Telecommunications, Nanjing, China

    Fu Xiao

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

He, F., Wang, Z., Gu, X. (2020). Complex Real-Time Network Topology Generation Optimization Based on Message Flow Control. In: He, J., et al. Data Science. ICDS 2019. Communications in Computer and Information Science, vol 1179. Springer, Singapore. https://doi.org/10.1007/978-981-15-2810-1_59

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-2810-1_59

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-2809-5

  • Online ISBN: 978-981-15-2810-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics