CMS workflow execution using intelligent job scheduling and data access strategies.
Delgado Peris, Antonio
Hernandez, José M.
van Lingen, Frank
AffiliationUniversity of the West of England
European Center for Nuclear Research
Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas
Fermi National Accelerator Laboratory
Universidad Complutense de Madrid
University of Bristol
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AbstractComplex scientific workflows can process large amounts of data using thousands of tasks. The turnaround times of these workflows are often affected by various latencies such as the resource discovery, scheduling and data access latencies for the individual workflow processes or actors. Minimizing these latencies will improve the overall execution time of a workflow and thus lead to a more efficient and robust processing environment. In this paper, we propose a pilot job concept that has intelligent data reuse and job execution strategies to minimize the scheduling, queuing, execution and data access latencies. The results have shown that significant improvements in the overall turnaround time of a workflow can be achieved with this approach. The proposed approach has been evaluated, first using the CMS Tier0 data processing workflow, and then simulating the workflows to evaluate its effectiveness in a controlled environment.
CitationHasham, K. et al. (2011) 'CMS workflow execution using intelligent job scheduling and data access strategies', IEEE Transactions on Nuclear Science. 58(3), pp.1221 - 1232. DOI:10.1109/TNS.2011.2146276
JournalIEEE Transactions on Nuclear Science