  Most existing failure detection algorithms rely on statistical methods, and
very few use machine learning (ML). This paper explores the viability of ML in
the field of failure detection: is it possible to implement an ML-based
detector that achieves a satisfactory quality of service? We implement a
prototype that uses a basic long short-term memory neural network algorithm,
and study its behavior with real traces. Although ML model has comparatively
longer computing time, our prototype performs well in terms of accuracy and
detection time.
