Operational Analytics Studies for ATLAS Distributed Computing: Data Popularity Forecast and Utilization of the WLCG Centers

Journal Article · 05 May 2024 · EPJ Web of Conferences (Online)

DOI: https://doi.org/10.1051/epjconf/202429504033 · OSTI ID:2448348

Grigoryeva, Maria ^[1]; Klimentov, Alexei ^[2]; Popova, Nina ^[1]; Shubin, Mikhail ^[1]; Schulz, Markus ^[3]

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1. Lomonosov Moscow State University (Russian Federation)
2. Brookhaven National Laboratory (BNL), Upton, NY (United States)
3. European Organization for Nuclear Research (CERN), Geneva (Switzerland)

Operational analytics is the direction of research related to the analysis of the current state of computing processes and the prediction of future states in order to anticipate imbalances and take timely measures to stabilize a complex system. There are two relevant areas in ATLAS Distributed Computing that are currently the focus of studies: user physics analysis including the forecast of popularity of data samples among users, and evaluating WLCG centers for their readiness to process user analysis payloads. Studying these areas is challenging due to the complexity involved, as it requires a comprehensive understanding of numerous boundary conditions typically found in large-scale distributed computing infrastructures. Forecasts of data popularity are problematic without the categorization of user tasks by their types (data transformation or physics analysis), which do not always appear on the surface but may induce noise, which introduces significant distortions for predictive analysis. Evaluating the WLCG resources by their analysis workloads is also a challenging task as it is necessary to find a balance between the workload of the resource, its performance, the waiting time for jobs on it, as well as the volume of jobs that it processes. This is especially difficult in a heterogeneous computing environment, where legacy resources are used along with modern high-performance machines. We will look at these areas of research in detail and discuss what tools and methods are used in our work, demonstrating results already obtained.

• Research Organization: Brookhaven National Laboratory (BNL), Upton, NY (United States)
• Sponsoring Organization: USDOE Office of Science (SC), High Energy Physics (HEP)
• Contributing Organization: ATLAS Computing Activity
• Grant/Contract Number: SC0012704
• OSTI ID: 2448348
• Report Number(s): BNL--226159-2024-JAAM
• Journal Information: EPJ Web of Conferences (Online), Journal Name: EPJ Web of Conferences (Online) Vol. 295; ISSN 2100-014X
• Publisher: EDP SciencesCopyright Statement
• Country of Publication: United States
• Language: English