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International Conference on Data Service

ICDS 2019: Data Science pp 639–651Cite as

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

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

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.

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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).

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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
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  2. Zhiyu Wang
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  3. Xiaoyan Gu
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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

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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

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

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-2809-5

  • Online ISBN: 978-981-15-2810-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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