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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/2755

Title: On Handling Redundancy for Failure Log Analysis of Cluster Systems
Authors: Gurumdimma, Nentawe
Jhumka, Arshad
Liakata, Maria
Chuah, Edward
Browne, James
Keywords: Cluster Log Data
Unsupervised learning
Levenshtein distance
Issue Date: 2015
Publisher: DEPEND 2015 : The Eighth International Conference on Dependability
Abstract: System event logs contain information that capture the sequence of events occurring in the system. They are often the primary source of information from large-scale distributed systems, such as cluster systems, which enable system administrators to determine the causes and detect system failures. Due to the complex interactions between the system hardware and software components, the system event logs are typically huge in size, comprising streams of interleaved log messages. However, only a small fraction of those log messages are relevant for analysis. We thus develop a novel, generic log compression or filtering (i.e., redundancy removal) technique to address this problem. We apply the technique over three different log files obtained from two different production systems and validate the technique through the application of an unsupervised failure detection approach. Our results are positive: (i) our technique achieves good compression, (ii) log analysis yields
URI: http://hdl.handle.net/123456789/2755
ISBN: 978-1-61208-429-9
Appears in Collections:Computer Science

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