| Issue |
EPJ Web Conf.
Volume 214, 2019
23rd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018)
|
|
|---|---|---|
| Article Number | 03037 | |
| Number of page(s) | 8 | |
| Section | T3 - Distributed computing | |
| DOI | https://doi.org/10.1051/epjconf/201921403037 | |
| Published online | 17 September 2019 | |
https://doi.org/10.1051/epjconf/201921403037
JAliEn: the new ALICE high-performance and high-scalability Grid framework
CERN,
CH-1211,
Geneva 23,
Switzerland
* Corresponding author: miguel.martinez.pedreira@cern.ch
Published online: 17 September 2019
The ALICE experiment will undergo extensive hardware and software upgrades for the LHC Run3. This translates in significant increase of the CPU and storage resources required for data processing, and at the same time the data access rates will grow linearly with the amount of resources. JAliEn (Java ALICE Environment) is the new Grid middleware designed to scale-out horizontally to fulfil the computing needs of the upgrade, and at the same time to modernize all parts of the distributed system software. This paper will present the architecture of the JAliEn framework, the technologies used and performance measurements. This work will also describe the next generation solution that will replace our main database backend, the AliEn File Catalogue. The catalogue is an integral part of the system, containing the metadata of all files written to the distributed Grid storage and also provides powerful search and data manipulation tools. As for JAliEn, the focus has been put onto horizontal scalability, with the aim to handle near exascale data volumes and order of magnitude more workload than the currently used Grid middleware. Lastly, this contribution will present how JAliEn manages the increased complexity of the tasks associated with the new ALICE data processing and analysis framework (ALFA) and multi-core environments.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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