Efficient alarm behavior analytics for telecom networks

Jiantao WANG, Caifeng HE, Yijun LIU, Guangjian TIAN, Ivy PENG, Jia XING, Xiangbing RUAN, Haoran XIE*, Fu Lee WANG

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

29 Citations (Scopus)


Locating network fault problems and filtering trivial alarms from important ones are the two main challenges in Network Operation Centers (NOCs). In this paper, we present an alarm behavior analysis and discovery system, AABD, that establishes flapping and parent–child (P–C) rules to reveal the operation patterns from a large number of alarms in telecom networks. These rules can be exploited to filter out unimportant alarms, conduct multi-dimensional analysis of the alarms and identify potential network problems. We propose two novel and effective algorithms to establish the flapping rules and P-C rules. The proposed system is validated using alarm datasets from five Internet service providers. Specifically, we verify the system and methodology in each of the five network domains, i.e., circuit-switched network (CS), packet-switched network (PS), 2G-radio access network (RAN-2G), 3G-radio access network (RAN-3G) and 4G-radio access network (RAN-4G), as these five domains can, to a great extent, form a complete network environment. More importantly, our system can establish a small number of rules, only dozens of flapping rules and P-C rules, and compress the alarms by approximately 84%, i.e., 84% of alarms will not be sent to the network operator. To summarize, the proposed system can help network operators respond to network faults in a timely fashion, locate the faults accurately and significantly reduce the time spent on these tasks.

Original languageEnglish
Pages (from-to)1-14
Number of pages14
JournalInformation Sciences
Early online date20 Mar 2017
Publication statusPublished - Sept 2017
Externally publishedYes


  • Alarm analysis and discovery
  • Big data
  • Correlation
  • Data mining
  • Frequent pattern mining
  • Telecom


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