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ASTrODS — Distributed storage for middle-size astroparticle physics facilities

Авторы: Kryukov A., Bychkov I., Mikhailov A., Nguyen M.-D., Shigarov A.

Журнал: CEUR Workshop Proceedings: 27th Symposium on Nuclear Electronics and Computing (NEC 2019; Budva, Montenegro)

Том: 2507

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Год: 2019

Отчётный год: 2020

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Аннотация: Currently, a number of experimental facilities for astrophysics of cosmic rays are being built and are already operating in the world. These installations produce large amounts of data that need to be collected, processed, and analyzed. Since many organizations around the world are involved in experimental collaboration, it is necessary to organize distributed data management and processing. Moreover, the widespread use of multi-messenger approach based on the coordinated observation, interpretation and analysis of disparate signals created by different astrophysical processes makes this problem much more urgent and complex. To meet a similar challenge in high energy physics, a WLCG grid was deployed as part of the LHC project. This solution, on the one hand, showed high efficiency, and, on the other hand, it turned out to be a rather heavy solution that requires high administrative costs, highly qualified staff and a very homogeneous environment on which applications operate. The paper considers a distributed data storage, called AstroDS, which was developed for middle-size astrophysical experiments. The storage currently integrates the data of the KASCADE and TAIGA experiments; in the future, the number of supported experiments will be increased. The main ideas and approaches used in the development of this storage are as follows: unification of access to local storages without changing their internal structure; data transfer only at the moment of actual access to them; search for the requested data using metadata and aggregation of the search results into a new collection available to the user. A distinguishing feature of the system is its focus on storing of both raw data and primary processed data, for example, data after calibration, using the Write-One-Read-Many method. Adding data to local repositories is carried out through special local services that provide, among other things, semi-automatic collection of meta-information in the process of downloading of the new data. At present, a prototype of the system was deployed on the basis of the SINP MSU. Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

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