# Anomaly Detection in High Performance Computers: A Vicinity Perspective

## TL;DR

This paper presents a vicinity-based statistical anomaly detection method for early failure detection in high performance computers, using anonymized system logs to improve robustness and privacy.

## Contribution

It introduces a novel vicinity-based anomaly detection approach that does not require hardware adjustments or extensive data, suitable for large-scale HPC systems.

## Key findings

- Anomaly detection precision ranges from 62% to 81%.
- Method effectively detects node failures in HPC systems.
- Approach preserves user and system privacy.

## Abstract

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%.