  By leveraging large clusters of commodity hardware, the Cloud offers great
opportunities to optimize the operative costs of software systems, but impacts
significantly on the reliability of software applications. The lack of control
of applications over Cloud execution environments largely limits the
applicability of state-of-the-art approaches that address reliability issues by
relying on heavyweight training with injected faults. In this paper, we propose
\emph(LOUD}, a lightweight fault localization approach that relies on positive
training only, and can thus operate within the constraints of Cloud systems.
\emph{LOUD} relies on machine learning and graph theory. It trains machine
learning models with correct executions only, and compensates the inaccuracy
that derives from training with positive samples, by elaborating the outcome of
machine learning techniques with graph theory algorithms. The experimental
results reported in this paper confirm that \emph{LOUD} can localize faults
with high precision, by relying only on a lightweight positive training.
