Name
Abstract | ||
|---|---|---|
Detecting anomalies in the flow of system logs of a high performance computing (HPC) facility is a challenging task. Although previous research has been conducted to identify nominal and abnormal phases; practical ways to provide system administrators with a reduced set of the most useful messages to identify abnormal behaviour remains a challenge. In this paper we describe an extensive study of logs classification and anomaly detection using K-means on real HPC unlabelled data extracted from the Curie supercomputer. This method involves (1) classifying logs by format, which is a valuable information for admin, then (2) build normal and abnormal classes for anomaly detection. Our methodology shows good performances for clustering and detecting abnormal logs. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-60045-1_23 | ADVANCES IN ARTIFICIAL INTELLIGENCE: FROM THEORY TO PRACTICE (IEA/AIE 2017), PT II |
Keywords | Field | DocType |
Anomaly detection,HPC,Log processing,K-means | k-means clustering,Data mining,Anomaly detection,Supercomputer,Computer science,Cluster analysis | Conference |
Volume | ISSN | Citations |
10351 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohamed-Cherif Dani | 1 | 0 | 0.34 |
| Henri Doreau | 2 | 0 | 0.34 |
| Samantha Alt | 3 | 0 | 2.03 |