CMS Workflow Execution using Intelligent Job Scheduling and Data Access Strategies

Journal Article · Wed Feb 01 00:00:00 EST 2012 · IEEE Trans.Nucl.Sci.

DOI:https://doi.org/10.1109/TNS.2011.2146276· OSTI ID:1560853

Hasham, Khawar ^[1]; Delgado Peris, Antonio ^[2]; Anjum, Ashiq ^[3]; Evans, Dave ^[4]; Hufnagel, Dirk ^[1]; Huedo, Eduardo ^[5]; Hernandez, Jose M. ^[2]; McClatchey, Richard ^[3]; Gowdy, Stephen ^[1]; Metson, Simon ^[6]

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1. CERN
2. Madrid, CIEMAT
3. West England U.
4. Fermilab
5. Madrid U.
6. Bristol U.

Complex 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 based infrastructure 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.

• Research Organization: Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
• Sponsoring Organization: USDOE Office of Science (SC), High Energy Physics (HEP)
• DOE Contract Number: AC02-07CH11359
• OSTI ID: 1560853
• Report Number(s): arXiv:1202.5480; CMS-NOTE-2010-014; CERN-CMS-NOTE-2010-014; FERMILAB-PUB-10-717-CMS; 1090824
• Journal Information: IEEE Trans.Nucl.Sci., Vol. 58
• Country of Publication: United States
• Language: English

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