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ProbD: Faulty Path Detection Based on Probability in Software-Defined Networking

Jiangyuan Yao1, Jiawen Wang1, Shuhua Weng1, Minrui Wang1, Deshun Li1,*, Yahui Li2, Xingcan Cao3

1 School of Computer Science and Technology, Hainan University, Haikou, 570228, China
2 School of Software, Beijing Jiaotong University, Beijing, 10004, China
3 University of British Columbia, Vancouver, V5K1K5, Canada

* Corresponding Author: Deshun Li. Email: email

Intelligent Automation & Soft Computing 2023, 36(2), 1783-1796.


With the increasing number of switches in Software-Defined Networking (SDN), there are more and more faults rising in the data plane. However, due to the existence of link redundancy and multi-path forwarding mechanisms, these problems cannot be detected in time. The current faulty path detection mechanisms have problems such as the large scale of detection and low efficiency, which is difficult to meet the requirements of efficient faulty path detection in large-scale SDN. Concerning this issue, we propose an efficient network path fault testing model ProbD based on probability detection. This model achieves a high probability of detecting arbitrary path fault in the form of small-scale random sampling. Under a certain path fault rate, ProbD obtains the curve of sample size and probability of detecting arbitrary path fault by randomly sampling network paths several times. After a small number of experiments, the ProbD model can correctly estimate the path fault rate of the network and calculate the total number of paths that need to be detected according to the different probability of detecting arbitrary path fault and the path fault rate of the network. The final experimental results show that, compared with the full path coverage test, the ProbD model based on probability detection can achieve efficient network testing with less overhead. Besides, the larger the network scale is, the more overhead will be saved.


Cite This Article

J. Yao, J. Wang, S. Weng, M. Wang, D. Li et al., "Probd: faulty path detection based on probability in software-defined networking," Intelligent Automation & Soft Computing, vol. 36, no.2, pp. 1783–1796, 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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