Model-driven multisite workflow scheduling

Workflows continue to play an important role in expressing and deploying scientific applications. In recent years, a wide variety of computational sites have emerged with shared access to users. A user may not be able to complete a complex workflow at a single site. It is thus beneficial to run different tasks of a workflow on different sites. For such cases, judicious scheduling strategy is required in order to map tasks in the workflow to resources at multiple sites so that the workload is balanced among sites and the overhead is minimized in data transfer. The key challenge is that the data transfer rate among sites varies based on the network capacity and load. We propose a workflow scheduling technique that tackles the multi-site task distribution challenge by using data movement performance modeling. We applied this technique to schedule an earth observation science workflow over three sites. Executed via the Swift parallel scripting paradigm, we augmented its default schedule and improved the time-to-completion by up to 52%.