CMS Workflow Execution Using Intelligent Job Scheduling and Data Access Strategies

Khawar Hasham, Antonio Delgado Peris, Ashiq Anjum, Dave Evans, Stephen Gowdy, José M. Hernandez, Eduardo Huedo, Dirk Hufnagel, Frank van Lingen, Richard McClatchey, Simon Metson
<span title="">2011</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/irkebyewo5dzlfovel4fhqmk34" style="color: black;">IEEE Transactions on Nuclear Science</a> </i> &nbsp;
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
more &raquo; ... 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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tns.2011.2146276">doi:10.1109/tns.2011.2146276</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/sax2bk5jabh77f2bbug73paw34">fatcat:sax2bk5jabh77f2bbug73paw34</a> </span>
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