EPJ Web Conf.
Volume 214, 201923rd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018)
|Number of page(s)||6|
|Section||T4 - Data handling|
|Published online||17 September 2019|
Towards an Event Streaming Service for ATLAS data processing
University of Udine, Udine,
2 CERN, Meyrin, Switzerland,
3 University of Wisconsin, Madison, USA
4 Brookhaven National Laboratory, Upton, USA
5 INFN Genova, Genova, Italy
6 Iowa State University, Ames, USA
7 Lawrence Berkeley National Laboratory, Berkeley, USA
8 Ludwig Maximilians Universität, München, Germany
* Corresponding author: Nicolo.Magini@cern.ch
Published online: 17 September 2019
The ATLAS experiment at the LHC is gradually transitioning from the traditional file-based processing model to dynamic workflow management at the event level with the ATLAS Event Service (AES). The AES assigns finegrained processing jobs to workers and streams out the data in quasi-real time, ensuring fully efficient utilization of all resources, including the most volatile. The next major step in this evolution is the possibility to intelligently stream the input data itself to workers. The Event Streaming Service (ESS) is now in development to asynchronously deliver only the input data required for processing when it is needed, protecting the application payload fromWAN latency without creating expensive long-term replicas. In the current prototype implementation, ESS processes run on compute nodes in parallel to the payload, reading the input event ranges remotely over the network, and replicating them in small input files that are passed to the application. In this contribution, we present the performance of the ESS prototype for different types of workflows in comparison to tasks accessing remote data directly. Based on the experience gained with the current prototype, we are now moving to the development of a server-side component of the ESS. The service can evolve progressively into a powerful Content Delivery Network-like capability for data streaming, ultimately enabling the delivery of ‘virtual data’ generated on demand.
© 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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