Abstract. Scientific workflows are increasingly popular for large-scale data analyses as they promise better documentation, increased.

Automatic workflow adaptation requires a cost model setting properties of different tools, data set sizes, and characteristics of the given infrastructure into ...

Abstract—Recent advances in cloud technologies and on- demand network circuits have created an unprecedented op- portunity to enable complex data-intensive ...

We present a flexible framework that enables workflow systems to adapt to changing conditions. The model is designed to reveal key aspects of the tasks ...

In this paper, we propose a new approach that addresses the third cornerstone of experimental reproducibility: the equipment. This work focuses on the equipment ...

We show that data transfer jobs from two workflows with different priorities are accurately arbitrated as the relative priorities change. Published in: 2017 ...

Scientific workflow management systems are mainly data-flow oriented, which face several challenges due to the huge amount of data and the required ...

For example, a workflow that adapts dynamically to changes in environment or data values requires formal and comprehensive descriptions so that a machine can ...

Depending on the workflow, changing the first steps can be done in a few minutes or may require more work, especially when it has been implemented using ...

Scientific workflows have become the primary mechanism for conducting analyses on distributed comput- ing infrastructures such as grids and clouds.