EPJ Web Conf.
Volume 214, 201923rd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018)
|Number of page(s)||8|
|Section||T3 - Distributed computing|
|Published online||17 September 2019|
Harvester : an edge service harvesting heterogeneous resources for ATLAS
Brookhaven National Laboratory,
2 University of Texas at Arlington, TX, USA
3 Argonne National Laboratory, IL, USA
4 University of Oslo, Oslo, Norway
5 Sapienza Universita e INFN, Roma I, Roma, Italy
6 Jozef Stefan Institute, Ljubljana, Slovenia
7 Academia Sinica, Taipei, Taiwan
∗ Corresponding author: email@example.com
Published online: 17 September 2019
The Production and Distributed Analysis (PanDA) system has been successfully used in the ATLAS experiment as a data-driven workload management system. The PanDA system has proven to be capable of operating at the Large Hadron Collider data processing scale over the last decade including the Run 1 and Run 2 data taking periods. PanDA was originally designed to be weakly coupled with the WLCG processing resources. Lately the system is revealing the difficulties to optimally integrate and exploit new resource types such as HPC and preemptible cloud resources with instant spin-up, and new workflows such as the event service, because their intrinsic nature and requirements are quite different from that of traditional grid resources. Therefore, a new component, Harvester, has been developed to mediate the control and information flow between PanDA and the resources, in order to enable more intelligent workload management and dynamic resource provisioning based on detailed knowledge of resource capabilities and their real-time state. Harvester has been designed around a modular structure to separate core functions and resource specific plugins, simplifying the operation with heterogeneous resources and providing a uniform monitoring view. This paper will give an overview of the Harvester architecture, current status with various resources, and future plans.
© The Authors, published by EDP Sciences, 2019
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