面向电信行业网络告警系统的告警过滤算法

doi:10.11772/j.issn.1001-9081.2018040879

计算机应用 ›› 2018, Vol. 38 ›› Issue (10): 2881-2885.DOI: 10.11772/j.issn.1001-9081.2018040879

面向电信行业网络告警系统的告警过滤算法

徐冰珂¹, 周宇喆¹, 杨茂林¹, 谢远航², 李晓瑜¹, 雷航¹

1. 电子科技大学信息与软件工程学院, 成都 610054;
2. 北京思特奇信息技术股份有限公司, 北京 100046

收稿日期:2018-04-28 修回日期:2018-06-11 出版日期:2018-10-10 发布日期:2018-10-13
通讯作者: 杨茂林
作者简介:徐冰珂(1997-),女,四川乐山人,主要研究方向:数据分析;周宇喆(1997-),女,四川遂宁人,主要研究方向:自然语言处理、数据分析;杨茂林(1987-),男,四川宜宾人,助理研究员,博士,CCF会员,主要研究方向:数据分析、实时计算;谢远航(1990-),男,四川南充人,工程师,主要研究方向:电信运维监控系统、大数据分析;李晓瑜(1984-),女,山东菏泽人,副教授,博士,CCF会员,主要研究方向:量子计算、机器学习、大数据分析;雷航(1960-),男,四川自贡人,教授,博士,主要研究方向:人工智能、大数据分析、计算机系统。
基金资助:
国家自然科学基金资助项目（61502082）。

Alarm-filtering algorithm of alarm management system for telecom networks

XU Bingke¹, ZHOU Yuzhe¹, YANG Maolin¹, XIE Yuanhang², LI Xiaoyu¹, LEI Hang¹

1. School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu Sichuan 610054, China;
2. SI-TECH Information Technology Company Limited, Beijing 100046, China

Received:2018-04-28 Revised:2018-06-11 Online:2018-10-10 Published:2018-10-13
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61502082).

摘要/Abstract

摘要： 为了减小电信网络中海量告警数据对的告警根源分析所造成的干扰，提出一种告警过滤算法。首先，基于电信网络告警数据对告警量分布、告警持续时间等特征进行量化分析，提出告警影响力和高频瞬态告警概念。在此基础上，从告警量、平均告警时间、告警影响力以及告警实例持续时间四个维度对告警重要程度进行综合分析，并提出复杂度为O（n）（n为告警记录数）的告警过滤算法。单因素实验分析显示，告警压缩比与特定告警元的告警量、平均告警时间、告警影响力以及告警实例持续时间具有正相关关系。对比实验结果表明，在相似告警压缩比下，所提算法的准确性比FTD（Flexible Transient flapping Determination）算法最多提高18个百分点，可用于电信行业的告警数据样本分析以及在线告警过滤。

关键词: 故障诊断, 告警过滤, 告警关联, 电信网络, 数据挖掘

Abstract: A large amount of alarms considerably complicate the root-cause analysis in telecom networks, thus a new alarm filtering algorithm was proposed to minimize the interference on the analysis. Firstly, a quantitative analysis for the alarm data, e.g., the quantity distribution and the average duration, was conducted, and the concepts of alarm impact and high-frequency transient alarm were defined. Subsequently, the importance of each alarm instance was evaluated from four perspectives:the amount of the alarms, the average duration of the alarms, the alarm impact, and the average duration of the alarm instance. Accordingly, an alarm filtering algorithm with O (n) computation complexity in principle was proposed, where n is the number of alarms under analysis. Single-factor experimental analysis show that the compression ratio of the alarm data has a positive correlation with the alarm amount of a specific alarm element, the average duration of the alarms, the alarm impact, and the duration of the alarm instance; further, the accuracy of the proposed algorithm is improved by 18 percentage points at most compared with Flexible Transient Flapping Determination (FTD) algorithm. The proposed algorithm can be used both for off-line analysis of historical alarm data and for on-line alarm filtering.

Key words: fault diagnosis, alarm filtering, alarm correlation, telecom network, data mining

中图分类号:

TP391

徐冰珂, 周宇喆, 杨茂林, 谢远航, 李晓瑜, 雷航. 面向电信行业网络告警系统的告警过滤算法[J]. 计算机应用, 2018, 38(10): 2881-2885.

XU Bingke, ZHOU Yuzhe, YANG Maolin, XIE Yuanhang, LI Xiaoyu, LEI Hang. Alarm-filtering algorithm of alarm management system for telecom networks[J]. Journal of Computer Applications, 2018, 38(10): 2881-2885.

参考文献

[1] DUSIA A, SETHI A S. Recent advances in fault localization in computer networks[J]. IEEE Communications Surveys & Tutorials, 2016, 18(4):3030-3051.
[2] STEINDER M L, SETHI A S. A survey of fault localization techniques in computer networks[J]. Scientific Computer Program, 2004, 53(2):165-194.
[3] ZEYDAN E, YABAS U, SÖZVER S, et al. Streaming alarm data analytics for mobile service providers[C]//NOMS 2016:Proceedings of the 2016 IEEE/IFIP Network Operations and Management Symposium. Piscataway, NJ:IEEE, 2016:1021-1022.
[4] CHENG M, WU W. Data analytics for fault localization in complex networks[J]. IEEE Internet of Things Journal, 2016, 3(5):701-708.
[5] WIETGREFE H. Investigation and practical assessment of alarm correlation methods for the use in GSM access networks[C]//NOMS 2002:Proceedings of the 2002 IEEE/IFIP Network Operations and Management Symposium. Piscataway, NJ:IEEE,2002:391-403.
[6] SOZUER S, ETEMOGLU C, ZEYDAN E. A new approach for clustering alarm sequences in mobile operators[C]//NOMS 2016:Proceedings of the 2016 IEEE/IFIP Network Operations and Management Symposium. Piscataway, NJ:IEEE, 2016:1055-1060.
[7] APPLEBY K, FAIK J, KAR G, et al. Threshold management for problem determination in transaction based e-commerce systems[C]//Proceedings of the 20059th IFIP/IEEE International Symposium on Integrated Network Management. Piscataway, NJ:IEEE, 2005:733-746.
[8] STEINDER M, SETHI A S. Probabilistic fault localization in communication systems using belief networks[J]. IEEE/ACM Transactions on Networks, 2004, 12(5):809-822.
[9] BENNACER L, CIAVAGLIA L, CHIBANI S, et al. Scalable and fast root cause analysis using inter cluster inference[C]//Proceedings of the 2013 IEEE International Conference on Communications. Piscataway, NJ:IEEE, 2013:3563-3568.
[10] EMRE S, BUGRA G, HAKAN F, et al. ALACA:a platform for dynamic alarm collection and alert notification in network management[J]. International Journal of Network Management, 2017, 27(4):e1980.
[11] CUI Y, SHI J, WANG Z. Fault propagation reasoning and diagnosis for computer networks using cyclic temporal constraint network model[J]. IEEE Transactions on Systems, Man and Cybernetics, 2017, 47(8):1965-1978.
[12] WANG J, HE C, LIU Y, et al. Efficient alarm behavior analytics for telecom networks[J]. Information Science, 2017, 402:1-14.
[13] 闫生超, 巢玉坚, 吴博, 等. 基于相关性集合的电力通信网告警过滤方法[C]//2011年中国电机工程学会年会论文集. 北京:中国电机工程学会, 2011:1-5. (YAN S C, CHAO Y J, WU B, et al. A relevance-set based alarm filtering approach for electric power communication networks[C]//Proceedings of the 2011 Annual Meeting of China Electrical Engineering Society. Beijing:Chinese Society for Electrical Engineering, 2011:1-5.)
[14] 王保义, 郭雅薇, 史占成, 等. 基于依赖搜索树的电力通信网络告警关联方法的研究[J]. 电力系统保护与控制, 2008, 36(6):59-64. (WANG B Y, GUO Y W, SHI Z C, et al. Research of alarm correlation method based on dependency search tree in electric power communication work[J]. Power System Protection and Control, 2008, 36(6):59-64.)
[15] 石永革, 梅玉洁, 石峰. 通信网网管告警过滤机制的研究与应用[J]. 计算机工程与设计, 2008, 29(9):2169-2171. (SHI Y G, MEI Y J, SHI F. Research and application of alarm filtering in network management systems[J]. Computer Engineering and Design, 2008, 29(9):2169-2171.)
[16] 张现飞, 徐润萍, 李淑静. 电力通信网告警分层过滤机制的设计[J]. 电子设计工程, 2008, 16(12):47-48. (ZHANG X F, XU R P, LI S J. Design of alarm stratification filtering m mechanism sin power communication network[J]. Electronic Design Engineering, 2008, 16(12):47-48.)

面向电信行业网络告警系统的告警过滤算法

Alarm-filtering algorithm of alarm management system for telecom networks

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