A Log Parsing Framework for ALICE O2 Facilities

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, utilize the Artificial Bee Colony (ABC) algorithm
for hyperparameter optimization, 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., 99.89% on the ALICE application log).