Parallelization of Scientific Workflows in the Cloud

Nowadays, more and more scientific experiments need to handle massive amounts of data. Their data processing consists of multiple computational steps and dependencies within them. A data-intensive scientific workflow is an appropriate tool for modeling such process. Since the execution of data-intensive scientific workflows requires large-scale computing and storage resources, a cloud environment, which provides virtually infinite resources is appealing. However, because of the general geographical distribution of scientific groups collaborating in the experiments, multisite management of data-intensive scientific workflows in the cloud is becoming an important problem. This paper presents a general study of the current state of the art of data-intensive scientific workflow execution in the cloud and corresponding multisite management techniques.