Scientific Workflow Applications (SWFA) play a vital role for both service consumers and service providers in designing and implementing large and complex scientific processes. Previously, researchers used parallel and distributed computing technologies, such as utility and grid computing to execute the SWFAs, these technologies provide limited utilization for the shared resources. In contrast, the scalability and flexibility challenges are better handled by using cloud-computing technologies for SWFA. Since cloud computing offers a technology that can significantly utilize the amounts of storage space and computing resources necessary for processing large-size and complex SWFAs. The workflow pattern design has provided the facility of re-using previously developed workflow solutions that enable the developers to adopt them for the considered SWFA. Inspired by this, the researchers have adopted several patterns of design to better design the SWFA. Effective pattern design that can consider challenges that may not become visible only in the implementation stage of a SWFA. However, the selection of the most effective pattern design in accordance with an execution method, data size, and problem complexity of a SWFA remains a challenging task. Motivated by this, we have proposed a conceptual framework that facilitates in recommending a suitable pattern design based on the quality requirements and capabilities are given and advertised by cloud consumers and providers, respectively. Finally, guidelines to assist in a smooth migrating of SWFA from other computation paradigms to cloud computing.