TY - GEN
T1 - A Conceptual framework supporting pattern design selection for scientific workflow applications in cloud computing
AU - Al-Khannaq, Ehab Nabiel
AU - Khan, Saif Ur Rehman
AU - Verbraeck, Alexander
AU - Van Lint, Hans
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Cloud Computing Environment
KW - Design Patterns
KW - Scientific Workflow Application
KW - Workflow Management System
UR - http://www.scopus.com/inward/record.url?scp=85088362695&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85088362695
T3 - CLOSER 2020 - Proceedings of the 10th International Conference on Cloud Computing and Services Science
SP - 229
EP - 236
BT - CLOSER 2020 - Proceedings of the 10th International Conference on Cloud Computing and Services Science
A2 - Ferguson, Donald
A2 - Helfert, Markus
A2 - Pahl, Claus
PB - SciTePress
T2 - 10th International Conference on Cloud Computing and Services Science, CLOSER 2020
Y2 - 7 May 2020 through 9 May 2020
ER -