Author(s)

Deussom Djomadji Eric Michel^1,2, Takembo Ntahkie Clovis¹, Tchapga Tchito Christian¹, Arabo Mamadou², Michael Ekonde Sone¹

¹Department of Electrical and Electronic Engineering, College of Technology, University of Buea, Buea, Cameroon.
²Division of Information and Communications Technology, The National School of Posts and Telecommunications and Information and Communication Technologies, University of Yaoundé I, Yaoundé, Cameroon.

The evolution of telecommunications has allowed the development of broadband services based mainly on fiber optic backbone networks. The operation and maintenance of these optical networks is made possible by using supervision platforms that generate alarms that can be archived in the form of log files. But analyzing the alarms in the log files is a laborious and difficult task for the engineers who need a degree of expertise. Identifying failures and their root cause can be time consuming and impact the quality of service, network availability and service level agreements signed between the operator and its customers. Therefore, it is more than important to study the different possibilities of alarms classification and to use machine learning algorithms for alarms correlation in order to quickly determine the root causes of problems faster. We conducted a research case study on one of the operators in Cameroon who held an optical backbone based on SDH and WDM technologies with data collected from 2016-03-28 to “2022-09-01” with 7201 rows and 18. In this paper, we will classify alarms according to different criteria and use 02 unsupervised learning algorithms namely the K-Means algorithm and the DBSCAN to establish correlations between alarms in order to identify root causes of problems and reduce the time to troubleshoot. To achieve this objective, log files were exploited in order to obtain the root causes of the alarms, and then K-Means algorithm and the DBSCAN were used firstly to evaluate their performance and their capability to identify the root cause of alarms in optical network.

Optical Network, Alarms, Log Files, Root Cause Analysis, Machine Learning

Michel, D.D.E., Clovis, T.N., Christian, T.T., Mamadou, A. and Sone, M.E. (2023) Machine Learning-Based Alarms Classification and Correlation in an SDH/WDM Optical Network to Improve Network Maintenance. Journal of Computer and Communications, 11, 122-141. doi: 10.4236/jcc.2023.112009.