Lawrence Berkeley National Laboratory

Cloud computing is increasingly considered as an additional computational resource platform for scientific workflows. The cloud offers opportunity to scale-out applications from desktops and local cluster resources. At the same time, it can eliminate the challenges of restricted software environments and queue delays in shared high performance computing environments. Choosing from these diverse resource platforms for a workflow execution poses a challenge for many scientists. Scientists are often faced with deciding resource platform selection trade-offs with limited information on the actual workflows. While many workflow planning methods have explored task scheduling onto different resources, these methods often require fine-scale characterization of the workflow that is onerous for a scientist. In this position paper, we describe our early exploratory work into using blackbox characteristics to do a cost-benefit analysis across of using cloud platforms. We use only very limited high-level information on the workflow length, width, and data sizes. The length and width are indicative of the workflow duration and parallelism. The data size characterizes the IO requirements. We compare the effectiveness of this approach to other resource selection models using two exemplar scientific workflows scheduled on desktops, local clusters, HPC centers, and clouds. Early results suggest that the blackbox model often makes the same resource selections as a more fine-grained whitebox model. We believe the simplicity of the blackbox model can help inform a scientist on the applicability of cloud computing resources even before porting an existing workflow.