A Method and Tool for Automated Induction of Relations from Quantitative Performance Logs
| Autor: | Joshua Kimball, Calton Pu |
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| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | Cloud Computing – CLOUD 2019 ISBN: 9783030235017 CLOUD |
| DOI: | 10.1007/978-3-030-23502-4_2 |
| Popis: | Operators use performance logs to manage large-scale web service infrastructures. Detecting, isolating and diagnosing fine-grained performance anomalies require integrating system performance measures across space and time. The diversity of these logs layouts impedes their efficient processing and hinders such analyses. Performance logs possess some unique features, which challenge current log parsing techniques. In addition, most current techniques stop at extraction leaving relational definition as a post-processing activity, which can be a substantial effort at web scale. To achieve scale, we introduce our perftables approach, which automatically interprets performance log data and transforms the text into structured relations. We interpret the signals provided by the layout using our template catalog to induce an appropriate relation. We evaluate our method on a large sample obtained from our experimental computer science infrastructure in addition to a sample drawn from the wild. We were able to successfully extract on average over 97% and 85% of the data respectively. |
| Databáze: | OpenAIRE |
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