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Open AccessArticle

Priolog: Mining Important Logs via Temporal Analysis and Prioritization

1
Department of Computer Science and Engineering, Kyungpook National University, 80 Daehakro, Bukgu, Daegu 41566, Korea
2
IBM TJ Watson Research Center, 1101 Kitchawan Rd, Yorktown Heights, NY 10598, USA
*
Author to whom correspondence should be addressed.
Sustainability 2019, 11(22), 6306; https://doi.org/10.3390/su11226306 (registering DOI)
Received: 27 September 2019 / Revised: 31 October 2019 / Accepted: 6 November 2019 / Published: 9 November 2019
(This article belongs to the Collection Advanced IT based Future Sustainable Computing)
Log analytics are a critical part of the operational management in today’s IT services. However, the growing software complexity and volume of logs make it increasingly challenging to mine useful insights from logs for problem diagnosis. In this paper, we propose a novel technique, Priolog, that can narrow down the volume of logs into a small set of important and most relevant logs. Priolog uses a combination of log template temporal analysis, log template frequency analysis, and word frequency analysis, which complement each other to generate an accurately ranked list of important logs. We have implemented this technique and applied to the problem diagnosis task of the popular OpenStack platform. Our evaluation indicates that Priolog can effectively find the important logs that hold direct hints to the failure cause in several scenarios. We demonstrate the concepts, design, and evaluation results using actual logs.
Keywords: log analysis; problem diagnosis; temporal correlation; log template; hierarchical clustering log analysis; problem diagnosis; temporal correlation; log template; hierarchical clustering
MDPI and ACS Style

Tak, B.; Park, S.; Kudva, P. Priolog: Mining Important Logs via Temporal Analysis and Prioritization. Sustainability 2019, 11, 6306.

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