  In response to the demand for higher computational power, the number of
computing nodes in high performance computers (HPC) increases rapidly. Exascale
HPC systems are expected to arrive by 2020. With drastic increase in the number
of HPC system components, it is expected to observe a sudden increase in the
number of failures which, consequently, poses a threat to the continuous
operation of the HPC systems. Detecting failures as early as possible and,
ideally, predicting them, is a necessary step to avoid interruptions in HPC
systems operation. Anomaly detection is a well-known general purpose approach
for failure detection, in computing systems. The majority of existing methods
are designed for specific architectures, require adjustments on the computing
systems hardware and software, need excessive information, or pose a threat to
users' and systems' privacy. This work proposes a node failure detection
mechanism based on a vicinity-based statistical anomaly detection approach
using passively collected and anonymized system log entries. Application of the
proposed approach on system logs collected over 8 months indicates an anomaly
detection precision between 62% to 81%.
