Anomaly Detection in High Performance Computers: A Vicinity Perspective

Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/GutachtenBeitrag in KonferenzbandBeigetragenBegutachtung

Beitragende

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%.

Details

OriginalspracheEnglisch
Titel2019 18th International Symposium on Parallel and Distributed Computing (ISPDC)
Seiten112-120
Seitenumfang9
PublikationsstatusVeröffentlicht - 2019
Peer-Review-StatusJa

Publikationsreihe

ReiheInternational Symposium on Parallel and Distributed Computing
ISSN2379-5352

Konferenz

Titel18th International Symposium on Parallel and Distributed Computing
KurztitelISPDC 2019
Veranstaltungsnummer18
Dauer5 - 7 Juni 2019
Webseite
BekanntheitsgradInternationale Veranstaltung
OrtVrije Universiteit Amsterdam
StadtAmsterdam
LandNiederlande

Externe IDs

WOS 000502088700022
Scopus 85071452722

Schlagworte