Roots-tracing of communication network alarm: A real-time processing framework

In the communication network, since the interconnection of a large number of components, mobile network operators run Operations Support Systems that generate vast amounts of alarm events. The harsh challenge for network operators is how to find the potential root causes from massive alarms in real time. In this paper, we propose a novel solution for the root causes analysis. The solution includes a silent gap based approach to resolve the asynchrony of alarms, an algorithm for constructing Bayesian network (BN) based on sequentiality between alarms, and Bayesian inference to identify the root causes. The silent gap-based approach reduces preprocessing time while taking into account the validity. Also, the proposed BN-based mechanism allows the identification of the root causes with a higher accuracy. Experiments conducted on a real alarm dataset are provided to support the proposed methods. In addition, we propose a new algorithm processing framework.

Automated summary

The paper proposes a novel solution for root cause analysis in communication networks, which includes a silent gap method, a Bayesian network algorithm, and Bayesian inference. Experimental results confirm the effectiveness and accuracy of the proposed methods.