ATLAS user analysis on private cloud resources at GoeGrid

21st International Conference on Computing in High Energy and Nuclear Physics

物理学;计算机科学

Glaser, F.^1 ; Nadal Serrano, J.^2 ; Grabowski, J.^1 ; Quadt, A.^2

Institute of Computer Science, University of Göttingen, Germany^1

II. Institute of Physics, University of Göttingen, Germany^2

关键词: Cloud environments;    Cloud infrastructures;    Computing model;    Computing resource;    Customizability;    Grid infrastructures;    Production jobs;    Research and development;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/664/2/022020/pdf
DOI  :  10.1088/1742-6596/664/2/022020

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

User analysis job demands can exceed available computing resources, especially before major conferences. ATLAS physics results can potentially be slowed down due to the lack of resources. For these reasons, cloud research and development activities are now included in the skeleton of the ATLAS computing model, which has been extended by using resources from commercial and private cloud providers to satisfy the demands. However, most of these activities are focused on Monte-Carlo production jobs, extending the resources at Tier-2. To evaluate the suitability of the cloud-computing model for user analysis jobs, we developed a framework to launch an ATLAS user analysis cluster in a cloud infrastructure on demand and evaluated two solutions. The first solution is entirely integrated in the Grid infrastructure by using the same mechanism, which is already in use at Tier-2: A designated Panda-Queue is monitored and additional worker nodes are launched in a cloud environment and assigned to a corresponding HTCondor queue according to the demand. Thereby, the use of cloud resources is completely transparent to the user. However, using this approach, submitted user analysis jobs can still suffer from a certain delay introduced by waiting time in the queue and the deployed infrastructure lacks customizability. Therefore, our second solution offers the possibility to easily deploy a totally private, customizable analysis cluster on private cloud resources belonging to the university.