Abstract
Fault management is critical to telecommunication networks. It consists of detecting, diagnosing, isolating and fixing network problems, a task that is time-consuming. A promising approach to improve fault management is to find patterns revealing the relationships between network alarms, to then only show the most important alarms to network operators. However, a limitation of current algorithms of this type is that they ignore the network topology. But the network topology is important to understand how alarms propagate on a network. This paper addresses this issue by modeling a real-life telecommunication network as a dynamic attributed graph and then extracting correlation patterns between network alarms called Alarm Correlation Rules. Experiments on a large telecommunication network show that interesting patterns are found that can greatly compress the number of alarms presented to network operators, which can reduce network maintenance costs.
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Fournier-Viger, P., He, G., Zhou, M., Nouioua, M., Liu, J. (2021). Discovering Alarm Correlation Rules for Network Fault Management. In: Hacid, H., et al. Service-Oriented Computing – ICSOC 2020 Workshops. ICSOC 2020. Lecture Notes in Computer Science(), vol 12632. Springer, Cham. https://doi.org/10.1007/978-3-030-76352-7_24
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DOI: https://doi.org/10.1007/978-3-030-76352-7_24
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