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Network health monitoring method based on multimodal spatiotemporal correlation fuzzy inference

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Abstract

By using the traditional active E2E (end-to-end) detection method to locate the congested link in IP network, we can only judge whether the network is congestion or not, and the congested cause cannot be effectively known. However, the passive detection method needs to acquire the syslog of all routers in the managed network, which requires a large amount of data. In this paper, a kind of multimodal spatiotemporal correlation fuzzy inference method for network health monitoring method was proposed through combining with the method of active E2E detection and passive detection. First, the set of network-congested links is obtained based on the Bayesian theorem through E2E path performance detection; then, the router syslog associated with the congested links will be acquired for a period of time, and the abnormal events in the syslog are detected based on the principle of mutual information (MI), so as to find the causes of network congestion. Based on the multimodal spatiotemporal correlation fuzzy paradigms combining with the active end-to-end detection and passive detection technology, the amount of data analysis will be greatly reduced and the operation speed will be improved. Simulation experiments and actual experiments verified the accuracy of the method this paper proposed.

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Abbreviations

MI:

Mutual information

IP:

Internet Protocol

E2E:

End to end

SNMP:

Simple Network Management Protocol

PCA:

Principal component analysis

SCP:

Set coverage problem

MAP:

Maximum A-posterior probability

ARPP:

Agreement Rate of Path Properties

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Funding

This study was supported by the Key Scientific Research Projects of Henan Colleges and Universities in 2018. (No. 18A510019), and the Science and Technology Development Program Project of Henan Province (No. 192102210109).

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Authors and Affiliations

  1. School of Intelligent Engineering, Zhengzhou University of Aeronautics, Zhengzhou, 450015, China

    Yu Chen & Xinling Wen

  2. Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI, 48824, USA

    Jarong Chou

  3. Shanghai Probetest Information Technology Co., Ltd, Shanghai, 518031, China

    Nengjie Zhu & Shaojun Hu

  4. Dbappsecurity Co., Ltd, Hangzhou, 310051, China

    Shuai Liao

Authors
  1. Yu Chen
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  2. Xinling Wen
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  3. Jarong Chou
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  4. Nengjie Zhu
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  6. Shuai Liao
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Correspondence to Yu Chen.

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Chen, Y., Wen, X., Chou, J. et al. Network health monitoring method based on multimodal spatiotemporal correlation fuzzy inference. Pers Ubiquit Comput 27, 1977–1990 (2023). https://doi.org/10.1007/s00779-019-01263-8

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  • DOI: https://doi.org/10.1007/s00779-019-01263-8

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