  Deployment of Network Function Virtualization (NFV) over multiple clouds
accentuates its advantages like the flexibility of virtualization, proximity to
customers and lower total cost of operation. However, NFV over multiple clouds
has not yet attained the level of performance to be a viable replacement for
traditional networks. One of the reasons is the absence of a standard based
Fault, Configuration, Accounting, Performance and Security (FCAPS) framework
for the virtual network services. In NFV, faults and performance issues can
have complex geneses within virtual resources as well as virtual networks and
cannot be effectively handled by traditional rule-based systems. To tackle the
above problem, we propose a fault detection and localization model based on a
combination of shallow and deep learning structures. Relatively simpler
detection of 'fault' and 'no-fault' conditions or 'manifest' and 'impending'
faults have been effectively shown to be handled by shallow machine learning
structures like Support Vector Machine (SVM). Deeper structure, i.e. the
stacked autoencoder has been found to be useful for a more complex localization
function where a large amount of information needs to be worked through, in
different layers, to get to the root cause of the problem. We provide
evaluation results using a dataset adapted from logs of disruption in an
operator's live network fault datasets available on Kaggle and another based on
multivariate kernel density estimation and Markov sampling.
