This paper proposes a model to identify the noise source in a virtualized infrastructure. This phenomenon appears when network functions running under virtual machines that are deployed on the same physical server compete for physical resources. First, an anomaly detection model is proposed to identify the machines that are in an abnormal state in the infrastructure by performing an unsupervised learning. An investigation of the root cause is later achieved by searching how anomalies are propagated in the system. To do this, a supervised learning of the anomaly propagation paths is proposed. A propagation graph is automatically created with a score assigned to its components. With a testbed created using Openstack, an experimentation study with real data is held giving promising results.

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