Root-cause localization using Restricted Boltzmann Machines

Monitoring complex systems and identifying degrading system components before the system or parts thereof fail, is crucial for many application areas, among them today's and future telecommunication systems. With the increasing complexity of such systems, the need to aid human operators through the use of machine learning tools is growing. In this paper, we present an automated approach for root-cause localization, a first step towards root-cause analysis, using a Restricted Boltzmann Machine (RBM). We describe an experiment conducted on data with ground truth, stemming from a simplified network. We use the RBM to cluster symptoms of degradation and we show how the results produced by the RBM capture the location of different possible combinations of hidden root causes.