In a data-intensive workflow, data and metadata, either temporary or persistent, are created and read at a high rate.
We leverage the Globus Toolkit to address some limitations in supporting the dynamic nature of large-scale Grid and data-intensive workflow executions.
On the other hand, for a data-intensive workflow with large inputs and several I/O-heavy intermediary phases, even the cost of accessing resources on the same node cannot be overlooked.
Higher-level dataflow languages are used on top of parallel and distributed dataflow systems to enable faster data-intensive workflow programs development, their easier optimization, and more maintainable code.
This joint study revealed that while 454 sequencing allows for reliable HLA genotype identification, it is a time-consuming, labor-intensive workflow requiring automation.
Combining workflow support to data-intensive workflows leads to only 8 works (7%).
Data-Intensive: works that effectively encompass one or more aspects inherent to data-intensive workflows.
Data-intensive workflows are generally computing- and data-intensive with large volume of data generated during their execution.
In solely data-intensive workflows, with no computationally intensive component, under-utilization of multiprocessor systems may not become apparent as storage and network throughput are the limiting factors.
Finally, hybrid environments, data-intensive workflows, and energy-aware scheduling represent the novel challenges in terms of cloud scheduling resource management according to the authors.
Lack of mechanisms to address the particularities of data-intensive workflows, especially considering that future trends point to the direction of I/O workflows with intensive data movement and with reliability-related mechanisms highly dependent on I/O as well.
