David A. Monge

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In this study we discuss how to enable grid sites for the support of data-intensive workflows. Usually, within grid sites, tasks and resources are administrated by local resource managers (LRMs). Many of LRMs have been designed for managing compute-intensive applications. Therefore, data-intensive workflow applications might not perform well on such(More)
We propose a technique for semi-automatic construction of gene expression data analysis workflows by grammar-like inference based on predefined workflow templates. The templates represent routinely used sequences of procedures such as normalization, data transformation, classifier learning, etc. Variations of such workflows (such as different instantiations(More)
Through the years, scienti c applications have demanded more powerful and sophisticated computing environments and management techniques. Work ows facilitated the design and management of scienti c applications. The complexity of today's work ows demand a high amount of resources and mechanisms for provisioning them. The execution of scienti c work ow(More)
The adequate management of scientific workflow applications strongly depends on the availability of accurate performance models of sub-tasks. Numerous approaches use machine learning to generate such models autonomously, thus alleviating the human effort associated to this process. However, these standalone models may lack robustness, leading to a decay on(More)
Given mobile devices ubiquity and capabilities, some researchers now consider them as resource providers of distributed environments called mobile Grids for running resource intensive software. Therefore, job scheduling has to deal with device singularities, such as energy constraints, mobility and unstable connectivity. Many existing schedulers consider at(More)
One of the central issues for the efficient management of Scientific workflow applications is the prediction of tasks performance. This paper proposes a novel approach for constructing performance models for tasks in data-intensive scientific workflows in an autonomous way. Ensemble Machine Learning techniques are used to produce robust combined models with(More)