Research on Fault Tracing Technology for 5G Mass Network Management Data

doi:10.13190/j.jbupt.2018-193

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2018, Vol. 41 ›› Issue (5): 131-136,142.doi: 10.13190/j.jbupt.2018-193

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Research on Fault Tracing Technology for 5G Mass Network Management Data

CHEN Mo¹, JIN Lei², GONG Xiang-yang¹, MAN Yi²

1. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China;
2. School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received:2018-08-10 Online:2018-10-28 Published:2018-11-20

Abstract

Abstract: Aiming at solving the problem of difficult traceability of mass network management data and large redundancy of correlation mining in the fifth generation of mobile communications system (5G) environment, sequential alarm correlation mining algorithm based on network topology was proposed by combining with the time constraint, sliding time window and classification hierarchy technology, which effectively reduced candidate sets, and realized the efficiently compressing and rapidly tracing the mass network management data. Simulation verified that the improved candidate set of fault tracing had actual correlation in topology, which was more effective and reliable than other correlation algorithms.

Key words: resource topology, alarm association, timing alarm, network management data

CLC Number:

TN911.22

CHEN Mo, JIN Lei, GONG Xiang-yang, MAN Yi. Research on Fault Tracing Technology for 5G Mass Network Management Data[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2018, 41(5): 131-136,142.

References

[1] Lameski P, Zdravevski E, Koceski S, et al. Suppression of intensive care unit false alarms based on the arterial blood pressure signal[J]. IEEE Access, 2017, 5:5829-5836.
[2] Hu W, Chen T, Shah S L. Detection of frequent alarm patterns in industrial alarm floods using itemset mining methods[J]. IEEE Transactions on Industrial Electronics, 2018, 65(9):7290-7300.
[3] Karoly R, Abonyi J. Multi-temporal sequential pattern mining based improvement of alarm management systems[C]//IEEE International Conference on Systems, Man, and Cybernetics.[S.l.]:IEEE, 2017:003870-003875.
[4] He Q, Zhou W, Xu H, et al. A distributed network alarm correlation analysis mechanism for heterogeneous networks[J]. Journal of Circuits Systems and Computers, 2017, 27(1):1850012.
[5] Wei L, Lu M, Zhu H, et al. Alarm correlation analysis method for smart power distribution and utilization communication network based on bayesian networks[C]//International Conference on Frontiers of Manufacturing Science and Measuring Technology. Taiyuan:ATLANTIS Press, 2017:1259-1264.
[6] Tjhai G C, Furnell S M, Papadaki M, et al. A preliminary two-stage alarm correlation and filtering system using SOM neural network and k-means algorithm[J]. Computers & Security, 2010, 29(6):712-723.
[7] 刘洪波, 陈刚, 宫钦. 基于神经网络的通信网络告警关联分析及应用[J]. 电信技术, 2018(5):32-35. Liu Hongbo, Chen Gang, Gong Qin. Analysis and application of alarm correlation in communication network based on neural network[J]. Telecommunications Technology, 2018(5):32-35.
[8] 罗明, 孟传伟, 黄海量. 基于加权频繁模式树的通信网络告警规则挖掘方法[J]. 计算机工程, 2016, 42(4):190-196. Luo Ming, Meng Chuanwei, Huang Hailiang. Communication network alarm rule mining method based on weighted frequent pattern tree[J]. Computer Engineering, 2016, 42(4):190-196.
[9] Hu W, Chen T, Shah S L. Discovering association rules of mode-dependent alarms from alarm and event logs[J]. IEEE Transactions on Control Systems Technology, 2018, 26(3):971-983.
[10] Yan Limei, Zhou Zhongyuan, Xu Jianjun, et al. Research on the method of fault location of transmission device based on time series of alarm[J]. Power System Protection and Control, 2018, 46(7):38-48.
[11] Dorgo G, Abonyi J. Sequence mining based alarm suppression[J]. IEEE Access, 2018(6):15365-15379.
[12] Savasere A, Omiecinski E, Navathe S B. An efficient algorithm for mining association rules in large databases[C]//International Conference on Very Large Data Bases.[S.l.]:Morgan Kaufmann Publishers Inc, 1995:432-444.
[13] 徐章艳, 刘美玲, 张师超, 等. Apriori算法的三种优化方法[J］. 计算机工程与应用, 2004, 40(36): 190-192. Xu Zhangyan, Liu Meiling, Zhang Shichao, et al. Three optimization methods for Apriori algorithm[J]. Computer Engineering and Applications, 2004, 40(36): 190-192.
[14] Bodon F. A fast APRIORI implementation[C］//Proceedings of the IEEE Icdm Workshop on Frequent Itemset Mining Implementations. Florida, USA: IEEE Press, 2003: 56-65.
[15] Srikant R, Agrawal R. Mining sequential patterns: generalizations and performance improvements[C］//International Conference on Extending Database Technology: Advances in Database Technology. [S.l.]: Springer-Verlag, 1996: 3-17.
[16] 张忠林, 田苗凤, 刘宗成, 等. 大数据环境下关联规则并行分层挖掘算法研究[J］. 计算机科学, 2016, 43(1): 286-289. Zhang Zhonglin, Tian Miaofeng, Liu Zongcheng, et al. Research on parallel hierarchical mining algorithm of association rules in big data environment [J]. Computer Science, 2016, 43(1): 286-289.

Research on Fault Tracing Technology for 5G Mass Network Management Data

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