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2 2018

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Alarm Correlation in Mobile Telecommunications Networks based on k-means Cluster Analysis Method

Maździarz A.

Journal of Telecommunications and Information Technology

2018

nr 2

95--102

Event correlation and root cause analysis play a fundamental role in the process of troubleshooting all technical faults and malfunctions. An in-depth, complicated multiprotocol analysis can be greatly supported or even replaced by a troubleshooting methodology based on data analysis approaches. The mobile telecommunications domain has been experiencing rapid development recently. Introduction of new technologies and services, as well as multivendor environment distributed across the same geographical area create a lot of challenges in network operation routines. Maintenance tasks have been recently becoming more and more complicated, time consuming and require big data analyses to be performed. Most network maintenance activities are completed manually by experts using raw network management information available in the network management system via multiple applications and direct database queries. With these circumstances considered, identiﬁcation of network failures is a very difficult, if not an impossible task. This explains why eﬀective yet simple tools and methods providing network operators with carefully selected, essential information are needed. Hence, in this paper efficient approximated alarm correlation algorithm based on the k-means cluster analysis method is proposed.

Temporal alarm pattern discovery in mobile telecommunication networks based on binary series analysis

Mazdziarz A.

Control and Cybernetics

2018

Vol. 47, no. 2

191--213

Highly-advanced systems, such as mobile telecommunication networks, characterized by increased complexity, make maintenance routines diﬃcult. Amount of data to be analyzed in a short time during fault diagnosis of the mobile telecommunication networks strongly justiﬁes the need to automate alarm correlation and root cause analysis. A major challenge in the establishment of alarm correlation is to determine how to reﬂect the alarm ﬂow inertia. Thus, adequate temporal alarm pattern discovery methods should be used in fault diagnosis for correlation-related purposes. Automatic temporal alarm pattern discovery allows fast generation of root cause analysis hypotheses and supports eﬀective troubleshooting of network problems. The process for fault propagation throughout the network is manifested by the time lag between the root-cause alarm and potentially linked symptoms, as well as weakening correlation strength with time. The paper presents a novel method for alarm correlation analysis in mobile telecommunication networks, based on binary series analysis. The method allows for discovery of causal relationship between alarms with dynamic alarm correlation window size estimation.