Extracting the textual and temporal structure of supercomputing logs

Supercomputers are prone to frequent faults that adversely affect their performance, reliability and functionality. System logs collected on these systems are a valuable resource of information about their operational status and health. However, their massive size, complexity, and lack of standard format makes it difficult to automatically extract information that can be used to improve system management. In this work we propose a novel method to succinctly represent the contents of supercomputing logs, by using textual clustering to automatically find the syntactic structures of log messages. This information is used to automatically classify messages into semantic groups via an online clustering algorithm. Further, we describe a methodology for using the temporal proximity between groups of log messages to identify correlated events in the system. We apply our proposed methods to two large, publicly available supercomputing logs and show that our technique features nearly perfect acc...

• Type: Journal
• Year: 2009
• Where: HIPC
• Authors: Sourabh Jain, Inderpreet Singh, Abhishek Chandra, Zhi-Li Zhang, Greg Bronevetsky
• Keywords: Distributed And Parallel Computing, HIPC 2009, Log Messages, Online Clustering Algorithm, Temporal Message Patterns