| Issue |
EPJ Web Conf.
Volume 337, 2025
27th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2024)
|
|
|---|---|---|
| Article Number | 01044 | |
| Number of page(s) | 8 | |
| DOI | https://doi.org/10.1051/epjconf/202533701044 | |
| Published online | 07 October 2025 | |
https://doi.org/10.1051/epjconf/202533701044
Implementation and Performance Analysis of the ALICE grid middleware JAliEn’s Job Optimizer
1 Faculty of Engineering and Science, Western Norway University of Applied Sciences, Bergen, Norway
2 CERN, Geneva, Switzerland
* e-mail: harn@hvl.no
Published online: 7 October 2025
This paper presents a comprehensive analysis of the implementation and performance enhancements of the new job optimizer service within the JAliEn (Java ALICE environment) middleware framework developed for the ALICE grid. The job optimizer service aims to efficiently split large-scale computational tasks into smaller grid jobs, thereby optimizing resource utilization and throughput of the grid by ensuring more grid resources are able to match with grid jobs. New functionalities for users of the grid are described, while also back-end changes are delved into that have improved the job optimizer service. Through testing and evaluation in a production environment, significant improvements in database performance, faster job splitting, and better scalability have been observed when doing comparative analysis against the legacy job optimization service. Further potential improvements in the future will also be explored.
This paper will also provide a look into the technical intricacies of the new job optimizer service, highlighting functionalities, implementation strategies, and integration within the existing JAliEn framework. Furthermore, insights into the lessons learned and challenges encountered during the implementation phase, deployment, and operationalization of the job optimizer service will be discussed.
© The Authors, published by EDP Sciences, 2025
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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