Fault Diagnosis Algorithm of Service Function Chain Based on Deep Dynamic Bayesian Network

Lun TANG; Hao LIAO; Rui CAO; Zhaokun WANG; Qianbin CHEN

doi:10.11999/JEIT200029

Volume 43 Issue 12

Dec. 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(12): 3588-3596

Lun TANG, Hao LIAO, Rui CAO, Zhaokun WANG, Qianbin CHEN. Fault Diagnosis Algorithm of Service Function Chain Based on Deep Dynamic Bayesian Network[J]. Journal of Electronics & Information Technology, 2021, 43(12): 3588-3596. doi: 10.11999/JEIT200029

Citation:

Lun TANG, Hao LIAO, Rui CAO, Zhaokun WANG, Qianbin CHEN. Fault Diagnosis Algorithm of Service Function Chain Based on Deep Dynamic Bayesian Network[J]. Journal of Electronics & Information Technology, 2021, 43(12): 3588-3596. doi: 10.11999/JEIT200029

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Fault Diagnosis Algorithm of Service Function Chain Based on Deep Dynamic Bayesian Network

doi: 10.11999/JEIT200029

1.
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Key Laboratry of Mobile Communication Technology, Chongqing University of Post and Telecommunications, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (61571073), The Science and Technology Research Program of Chongqing Municipal Education Commission (KJZD- M201800601)

Received Date: 2020-01-08
Rev Recd Date: 2021-06-14

Available Online: 2021-07-10

Publish Date: 2021-12-21

Abstract

Abstract

To solve the problem of the abnormal performance of multiple service function chains caused by the failure of the underlying physical node under the 5G end-to-end network slicing scenario, a service function chain fault diagnosis algorithm based on Deep Dynamic Bayesian Network (DDBN) is proposed in this paper. This algorithm builds a dependency relationship between faults and symptoms based on a multi-layer propagation model of faults in a network virtualization environment. This algorithm first builds a dependency graph model of faults and symptoms based on the multi-layer propagation relationship of faults in a network virtualization environment, and collects symptoms by monitoring performance data of multiple virtual network functions on physical nodes. Then, considering the diversity of network symptom observation data based on Software Defined Network (SDN) and Network Function Virtualization (NFV) architecture and the spatial correlation between physical nodes and virtual network functions, a deep belief network is introduced to extract the characteristics of the observation data, and the adaptive learning rate algorithm with momentum is used to fine-tune the model to accelerate the convergence speed. Finally, dynamic Bayesian network is introduced to diagnose the root cause of faults in real time by using the temporal correlation between faults. The simulation results show that the algorithm can effectively diagnose the root cause of faults and has good diagnostic accuracy.
- Virtual network function,
- Service Function Chain (SFC),
- Fault diagnosis,
- Deep dynamic Bayesian network

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References(15)

References

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