  5G networks are expected to be more dynamic and chaotic in their structure
than current networks. With the advent of Network Function Virtualization
(NFV), Network Functions (NF) will no longer be tightly coupled with the
hardware they are running on, which poses new challenges in network management.
Noisy neighbor is a term commonly used to describe situations in NFV
infrastructure where an application experiences degradation in performance due
to the fact that some of the resources it needs are occupied by other
applications in the same cloud node. These situations cannot be easily
identified using straightforward approaches, which calls for the use of
sophisticated methods for NFV infrastructure management. In this paper we
demonstrate how Machine Learning (ML) techniques can be used to identify such
events. Through experiments using data collected at real NFV infrastructure, we
show that standard models for automated classification can detect the noisy
neighbor phenomenon with an accuracy of more than 90% in a simple scenario.
