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- W4379471875 abstract "Nowadays, massive data-intensive scientific applications are represented in the form of workflows. Workflows are the set of dependent or independent tasks groups that together execute the scientific applications effectively and quickly by acquiring computation resources from the various distributed environments, such as grid and cloud. On executing the dependent workflows, failure of a task leads to resubmission of the whole workflow again. It is proposed to predict the status of the tasks and execute them in the cloud resources. This is called as label tuning. Based on similarity checking and normalization mechanisms, tasks are categorized into Success/Partial Success and Failure/Partial Failure. Such multi-label classification is used to reschedule the tasks before the failure occurs. The experimental results show that predicting the task failure using multi-label classification provides 97.5% accuracy, which is 1.5% greater than binary label classification." @default.
- W4379471875 created "2023-06-07" @default.
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- W4379471875 date "2023-01-01" @default.
- W4379471875 modified "2023-10-18" @default.
- W4379471875 title "Mutli-Label Classification Using Label Tuning Method in Scientific Workflows" @default.
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- W4379471875 doi "https://doi.org/10.1007/978-3-031-28324-6_10" @default.
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