Stork data scheduler: mitigating the data bottleneck in e-Science.

In this paper, we present the Stork data scheduler as a solution for mitigating the data bottleneck in e-Science and data-intensive scientific discovery. Stork focuses on planning, scheduling, monitoring and management of data placement tasks and application-level end-to-end optimization of networked inputs/outputs for petascale distributed e-Science applications. Unlike existing approaches, Stork treats data resources and the tasks related to data access and movement as first-class entities just like computational resources and compute tasks, and not simply the side-effect of computation.