EPJ Web Conf.
Volume 245, 202024th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2019)
|Number of page(s)||6|
|Section||7 - Facilities, Clouds and Containers|
|Published online||16 November 2020|
Running ALICE Grid Jobs in Containers A new approach to job execution for the next generation ALICE Grid framework
Faculty of Engineering and Science, Western Norway University of Applied Sciences, Bergen, Norway
2 CERN, Geneva, Switzerland
* e-mail: firstname.lastname@example.org
Published online: 16 November 2020
The new JAliEn (Java ALICE Environment) middleware is a Grid framework designed to satisfy the needs of the ALICE experiment for the LHC Run 3, such as providing a high-performance and high-scalability service to cope with the increased volumes of collected data. This new framework also introduces a split, two-layered job pilot, creating a new approach to how jobs are handled and executed within the Grid. Each layer runs on a separate JVM, with a separate authentication token, allowing for a finer control of permissions and improved isolation of the payload. Having these separate layers also allows for the execution of job payloads within containers. This allows for the further strengthening of isolation and creates a cohesive environment across computing sites, while avoiding the resource overhead associated with traditional virtualisation.
This contribution presents the architecture of the new split job pilot found in JAliEn, and the methods used to achieve the execution of Grid jobs while maintaining reliable communication between layers. Specifically, how this is achieved despite the possibility of a layer being run in a separate container, while retaining isolation and mitigating possible security risks. Furthermore, we discuss how the implementation remains agnostic to the choice of container platform, allowing it to run within popular platforms such as Singularity and Docker.
© The Authors, published by EDP Sciences, 2020
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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