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A flexible data-driven approach for execution trace filtering

Kadjo Gwandy Kouamé, Naser Ezzati-Jivan and Michel Dagenais

Paper (2015)

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Execution traces are frequently used to study system run-time behaviour and to detect problems. However, the huge amount of data in an execution trace may complexify its analysis. Moreover, users are not usually interested in all events of a trace, hence the need for a proper filtering approach. Filtering is used to generate an enhanced trace, with a reduced size and complexity, that is easier to analyse. The approach described in this paper allows to define custom filtering patterns, declaratively in XML, to concentrate the analysis on the most important and interesting events. The filtering scenarios include syntaxes to describe various analysis patterns using finite state machines. The patterns range from very simple event filtering to complex multi-level event abstraction, covering various types of synthetic behaviours that can be captured from execution trace data. The paper provides the details on this data-driven filtering approach and some interesting use cases for the trace events generated by the LTTng Linux kernel tracer.

Uncontrolled Keywords

LTTng, Trace Analysis, Performance analysis, Data Filtering

Subjects: 2700 Information technology > 2705 Software and development
2700 Information technology > 2720 Computer systems software
Department: Department of Computer Engineering and Software Engineering
Grant number: CRDPJ468687-14
PolyPublie URL: https://publications.polymtl.ca/2985/
Conference Title: IEEE International Congress on Big Data (BigData Congress 2015)
Conference Location: New York, NY, USA
Conference Date(s): 2015-06-27 - 2015-07-02
Publisher: IEEE
DOI: 10.1109/bigdatacongress.2015.112
Official URL: https://doi.org/10.1109/bigdatacongress.2015.112
Date Deposited: 13 Feb 2018 10:39
Last Modified: 20 Apr 2023 13:41
Cite in APA 7: Kouamé, K. G., Ezzati-Jivan, N., & Dagenais, M. (2015, June). A flexible data-driven approach for execution trace filtering [Paper]. IEEE International Congress on Big Data (BigData Congress 2015), New York, NY, USA (6 pages). https://doi.org/10.1109/bigdatacongress.2015.112


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