  Reliability is a cumbersome problem in High Performance Computing Systems and
Data Centers evolution. During operation, several types of fault conditions or
anomalies can arise, ranging from malfunctioning hardware to improper
configurations or imperfect software. Currently, system administrator and final
users have to discover it manually. Clearly this approach does not scale to
large scale supercomputers and facilities: automated methods to detect faults
and unhealthy conditions is needed. Our method uses a type of neural network
called autoncoder trained to learn the normal behavior of a real, in-production
HPC system and it is deployed on the edge of each computing node. We obtain a
very good accuracy (values ranging between 90% and 95%) and we also demonstrate
that the approach can be deployed on the supercomputer nodes without negatively
affecting the computing units performance.
