2013
DOI: 10.1016/j.future.2013.04.019
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Characterizing workflow-based activity on a production e-infrastructure using provenance data

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Cited by 12 publications
(13 citation statements)
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References 27 publications
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“…Ferreira et al, classifies the state of each task in a workflow and apply the appropriate rule to mitigate failures [6]; however, resource scaling were not addressed since Grid environments assign tasks in a static resource. Madougou et al, analysed task failures in an e-Infrastructure, based on history of workflow activity from the e-BioInfra platform [7]; however, the analysis only focuses on underlying resource usage and did not connect with the higher level workflow deception tasks. The work presented in [8] proposes an online mechanism for detecting anomalies while executing scientific workflows on clouds.…”
Section: Related Workmentioning
confidence: 99%
“…Ferreira et al, classifies the state of each task in a workflow and apply the appropriate rule to mitigate failures [6]; however, resource scaling were not addressed since Grid environments assign tasks in a static resource. Madougou et al, analysed task failures in an e-Infrastructure, based on history of workflow activity from the e-BioInfra platform [7]; however, the analysis only focuses on underlying resource usage and did not connect with the higher level workflow deception tasks. The work presented in [8] proposes an online mechanism for detecting anomalies while executing scientific workflows on clouds.…”
Section: Related Workmentioning
confidence: 99%
“…They categorized the virtual machine workload with respect to the following patterns: periodicity, threshold, relationship, variability, and image similarity. Madougou et al [57] provided a characterization of workflow executions using provenance data captured from a workflow management system. They analyzed usage and failure patterns at the workflow and task levels.…”
Section: Related Workmentioning
confidence: 99%
“…For each workflow execution it collects data related to the jobs, their inputs and output results, users in charge of the experiments, and dependency relationships among these data. The collector follows a similar approach to our previous implementations [51,52], deploying PROV-man to gather provenance information and organize it according to the experiment context. Figure 11 illustrates two use case scenarios of the provenance collector, namely gUSE/WS-PGRADE and Neuroscience gateway where detailed information about executed workflows are gathered from gUse and NSG databases, as well as from the log files generated by the jobs executed on the DCIs.…”
Section: Provenance Of a Science Gatewaymentioning
confidence: 99%
“…Additionally, we enhanced PLIER with a set of tools to build, store, retrieve, share, and visualize workflow experiments. PLIER has been extensively used to collect and explore provenance for scientific experiments performed on a grid infrastructure, namely: (1) as an integrated component within the WS-VLAM workflow system [49,50], and (2) as a core component to automatically gather provenance data from existing grid workflow enactments services [51,52] First, we conducted a study comparing PROV to OPM, based on the provenance specifications as defined for OPM Core Specification (v1.1) and the latest PROV documentation [7]. Table 1 illustrates the main OPM concepts with their counterparts in the PROV specification.…”
Section: Introductionmentioning
confidence: 99%