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. Each platform has different properties (e.g., queue wait times in high performance systems, virtual machine startup overhead in clouds) and characteristics (e.g., custom environments in cloud) that makes choosing from these diverse resource platforms for a workflow execution a challenge for 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 resource selection or task scheduling, these methods often require fine-scale characterization of the workflow that is onerous for a scientist. In this paper, we describe our early exploratory work in using blackbox characteristics for a cost-benefit analysis of using different resource platforms. In our blackbox method, we use only limited high-level information on the workflow length, width, and data sizes. The length and width are indicative of the workflow duration and parallelism. We compare the effectiveness of this approach to other resource selection models using two exemplar scientific workflows on desktop, local cluster, HPC center, and cloud platforms. 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 a new resource platform, such as cloud resources, even before porting an existing workflow.