EPJ Web Conf.
Volume 245, 202024th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2019)
|Number of page(s)||7|
|Section||3 - Middleware and Distributed Computing|
|Published online||16 November 2020|
Automatic log analysis with NLP for the CMS workflow handling
INFN, National Institute of Nuclear Physics, Naples, Italy
2 MIT, Massachusetts Institute of Technology, Cambridge, USA
3 DESY, Deutsches Elektronen-Synchrotron, Hamburg, Germany
4 FNAL, Fermi National Accelerator Laboratory, Batavia, USA
5 CERN, European Organization for Nuclear Research, Geneva, Switzerland
6 Catholic University of America, Washington DC, USA
7 University of California, Riverside, USA
8 California Institute of Technology, Pasadena, USA
* e-mail: firstname.lastname@example.org
Published online: 16 November 2020
The central Monte-Carlo production of the CMS experiment utilizes the WLCG infrastructure and manages daily thousands of tasks, each up to thousands of jobs. The distributed computing system is bound to sustain a certain rate of failures of various types, which are currently handled by computing operators a posteriori. Within the context of computing operations, and operation intelligence, we propose a Machine Learning technique to learn from the operators with a view to reduce the operational workload and delays. This work is in continuation of CMS work on operation intelligence to try and reach accurate predictions with Machine Learning. We present an approach to consider the log files of the workflows as regular text to leverage modern techniques from Natural Language Processing (NLP). In general, log files contain a substantial amount of text that is not human language. Therefore, different log parsing approaches are studied in order to map the log files’ words to high dimensional vectors. These vectors are then exploited as feature space to train a model that predicts the action that the operator has to take. This approach has the advantage that the information of the log files is extracted automatically and the format of the logs can be arbitrary. In this work the performance of the log file analysis with NLP is presented and compared to previous approaches.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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