Anomaly localization in large-scale clusters

— A critical problem facing by managing large-scale clusters is to identify the location of problems in a system in case of unusual events. As the scale of high performance computing (HPC) grows, systems are getting bigger. When a system fails to function properly, health-related data are collected for troubleshooting. However, due to the massive quantities of information obtained from a large number of components, the root causes of anomalies are often buried like needles in a haystack. In this paper, we present a localization method to automatically find out the potential root causes (i.e. a subset of nodes) of the problem from the overwhelming amount of data collected system-wide. System managers can focus on examining these potential locations, thereby significantly reducing human efforts required for anomaly localization. Our method consists of three interrelated steps: (1) feature collection to assemble a feature space for the system; (2) feature extraction to obtain the most...

• Type: Conference
• Year: 2007
• Where: CLUSTER
• Authors: Ziming Zheng, Yawei Li, Zhiling Lan
• Keywords: Cell-based Detection Algorithm, CLUSTER 2007, Cluster Computing, Critical Problem, Large-scale Clusters