Optimizing scientific workflows in the cloud

As scientific workflows are increasingly deployed in clouds, a myriad of studies have been conducted-including the development of workflow execution systems and scheduling/resource-management algorithms-for optimizing the execution of these workflows. However, the efficacy of most, if not all, of these previous works is limited by the original design and structure of workflow, i.e., Sequential code and few bottleneck tasks. In this paper, we address the optimization of scientific workflow execution in clouds by exploiting multi-core systems with the parallelization of bottleneck tasks. To this end, we develop a workflow visualization toolkit to synthetize resource consumption and data transfer patterns, as well as to identify the bottleneck, of the workflow being studied. Parallelization techniques are then applied to the module that is identified as the bottleneck in order to take full advantage of the underlying multicore computing environment. Testing results with a 6.0-degree Montage example on Amazon EC2 with various configurations show that our optimization of workflows (bottleneck tasks in particular) reduces completion time (or make span) by 21% to 43% depending on the instance type being used to run the workflow, without any impact on the cost.