A New Visual Analytics Toolkit for ATLAS Computing Metadata

Autor: Tatiana Korchuganova, V.V. Pilyugin, Mikhail Titov, I. E. Milman, Alexei Klimentov, Maria Grigorieva, S. Padolski, Aleksandr Alekseev, T.P. Galkin
Rok vydání: 2020
Předmět:
Zdroj: Journal of Physics: Conference Series. 1525:012086
ISSN: 1742-6596
1742-6588
Popis: The ATLAS experiment at the Large Hadron Collider has a complex heterogeneous distributed computing infrastructure, which is used to process and analyse exabytes of data. Metadata are collected and stored at all stages of data processing and physics analysis. All metadata could be divided into operational metadata to be used for the quasi on-line monitoring, and archival to study the behaviour of corresponding systems over a given period of time (i.e., long-term data analysis). Ensuring the stability and efficiency of functioning of complex and large-scale systems, such as those in the ATLAS Computing, requires sophisticated monitoring tools, and the long-term monitoring data analysis becomes as important as the monitoring itself. Archival metadata, which contains a lot of metrics (hardware and software environment descriptions, network states, application parameters, errors) accumulated for more than decade, can be successfully processed by various machine learning (ML) algorithms for classification, clustering and dimensionality reduction. However, the ML data analysis, despite the massive use, is not without shortcomings: the underlying algorithms are usually treated as “black boxes”, as there are no effective techniques for understanding their internal mechanisms, and the domain-experts involvement in the process of ML data analysis is very limited. As a result, the data analysis suffers from the lack of human supervision. Moreover, sometimes the conclusions made by algorithms with a high accuracy may have no sense regarding the real data model. In this work we will demonstrate how the interactive data visualization can be applied to extend the routine ML data analysis methods. Visualization allows an active use of human spatial thinking to identify new tendencies and patterns found in the collected data, avoiding the necessity of struggling with the instrumental analytics tools. The architecture and the corresponding prototype of Interactive Visual Explorer (InVEx) - visual analytics toolkit for the multidimensional data analysis of ATLAS computing metadata will be presented. The web-application part of the prototype provides an interactive visual clusterization of ATLAS computing jobs, search for computing jobs non-trivial behaviour and its possible reasons. The ATLAS experiment at the Large Hadron Collider has a complex heterogeneous distributed computing infrastructure, which is used to process and analyse exabytes of data. Metadata are collected and stored at all stages of data processing and physics analysis. All metadata could be divided into operational metadata to be used for the quasi on-line monitoring, and archival to study the behaviour of corresponding systems over a given period of time (i.e. long-term data analysis). Ensuring the stability and efficiency of complex and large-scale systems, such as those in the ATLAS Computing, requires sophisticated monitoring tools, and the long-term monitoring data analysis becomes as important as the monitoring itself. Archival metadata, which contains a lot of metrics (hardware and software environment descriptions, network states, application parameters, errors) accumulated for more than a decade, can be successfully processed by various machine learning (ML) algorithms for classification, clustering and dimensionality reduction. However, the ML data analysis, despite the massive use, is not without shortcomings: the underlying algorithms are usually treated as “black boxes”, as there are no effective techniques for understanding their internal mechanisms. As a result, the data analysis suffers from the lack of human supervision. Moreover, sometimes the conclusions made by algorithms may not be making sense with regard to the real data model. In this work we will demonstrate how the interactive data visualization can be applied to extend the routine ML data analysis methods. Visualization allows an active use of human spatial thinking to identify new tendencies and patterns found in the collected data, avoiding the necessity of struggling with the instrumental analytics tools. The architecture and the corresponding prototype of Interactive Visual Explorer (InVEx) - visual analytics toolkit for the multidimensional data analysis of ATLAS computing metadata will be presented. The web-application part of the prototype provides an interactive visual clusterization of ATLAS computing jobs, search for computing jobs non-trivial behaviour and its possible reasons.
Databáze: OpenAIRE
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