Search for Anomalies in the Computational Jobs of the ATLAS Experiment with the Application of Visual Analytics

ATLAS is the largest experiment at the LHC. It generates vast volumes of scientific data accompanied with auxiliary metadata, representing all stages of data processing, Monte-Carlo simulation, and characteristics of computing environment. Terabytes of metadata was accumulated by the workflow and data management, and metadata archiving systems. These metadata can help physicists carrying out studies to evaluate in advance the duration of their analysis jobs. As these jobs are executed in a heterogeneous distributed and dynamically changing infrastructure, their duration varies across computing centers and depends on many factors. Ensuring the uniformity in jobs execution requires searching for anomalies and analyzing the reasons of non-trivial jobs execution behavior to predict and avoid the recurrence in future. Detailed analysis of large volume of jobs execution benefits from application of machine learning and visual analysis methods. The approach of visual analytics technique was demonstrated on the analysis of jobs archive. Proposed method allowed to figure out computing sites having non-trivial jobs execution process, and the visual cluster analysis showed parameters affecting or indicating possible time delays. Further work will concentrate on increasing of the amount of analyzed jobs and the development of interactive visual models, facilitating the interpretation of analysis results.