A Log Parsing Framework for ALICE O2 Facilities
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- @Article{Marlaithong:2023:ACC,
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author = "Tinnakorn Marlaithong and Vasco Chibante Barroso and
Phond Phunchongharn",
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journal = "IEEE Access",
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title = "A Log Parsing Framework for {ALICE} O2 Facilities",
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year = "2023",
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volume = "11",
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pages = "69439--69457",
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abstract = "The ALICE (A Large Ion Collider Experiment) detector
at the European Organization for Nuclear Research
(CERN) generates a substantial volume of experimental
data, demanding efficient online and offline
processing. To enhance the stability and reliability of
the ALICE computing system, this study introduces an
Artificial Intelligence-based logging system designed
to detect, identify, and resolve issues through the
analysis of system runtime information contained in
logs. Existing online log parsing methods, however,
often lack full automation and generality, relying
instead on manual parameter definition and regular
expressions that are better suited for static logs. In
this study, we propose a novel and fully automated
online log parsing framework for ALICE O2
(Online-Offline). To overcome key challenges, we employ
the Term Frequency-Inverse Document Frequency (TF-IDF)
algorithm to create ground truth, employ genetic
programming to generate regular expressions, use the
Artificial Bee Colony (ABC) algorithm for
hyperparameter optimisation, and implement a log
template reduction algorithm to reduce similarity among
log templates. Our framework's effectiveness is
validated through experiments on 5 benchmark log
datasets and ALICE application logs, comparing its
performance with the state-of-art online log parsing
framework, Drain. The empirical results demonstrate the
automated nature of our approach and its ability to
achieve accurate parsing with high accuracy (i.e.,
99.89percent on the ALICE application log).",
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keywords = "genetic algorithms, genetic programming, Real-time
systems, Optimisation, Benchmark testing, Anomaly
detection, Tuning, Task analysis, Systems architecture,
Machine learning, ALICE experiment, FLP cluster,
machine learning, online log parser, TF-IDF",
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DOI = "doi:10.1109/ACCESS.2023.3293406",
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ISSN = "2169-3536",
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notes = "Also known as \cite{10176271}",
- }
Genetic Programming entries for
Tinnakorn Marlaithong
Vasco Chibante Barroso
Phond Phunchongharn
Citations