TY - JOUR
T1 - A terminology for scientific workflow systems
AU - Suter, Frédéric
AU - Coleman, Tainã
AU - Altintaş, İlkay
AU - Badia, Rosa M.
AU - Balis, Bartosz
AU - Chard, Kyle
AU - Colonnelli, Iacopo
AU - Deelman, Ewa
AU - Di Tommaso, Paolo
AU - Fahringer, Thomas
AU - Goble, Carole
AU - Jha, Shantenu
AU - Katz, Daniel S.
AU - Köster, Johannes
AU - Leser, Ulf
AU - Mehta, Kshitij
AU - Oliver, Hilary
AU - Peterson, J.-Luc
AU - Pizzi, Giovanni
AU - Pottier, Loïc
AU - Sirvent, Raül
AU - Suchyta, Eric
AU - Thain, Douglas
AU - Wilkinson, Sean R.
AU - Wozniak, Justin M.
AU - Ferreira da Silva, Rafael
PY - 2025/6/24
Y1 - 2025/6/24
N2 - The term “scientific workflow” has evolved over the last two decades to encompass a broad range of compositions of interdependent compute tasks and data movements. It has also become an umbrella term for processing in modern scientific applications. Today, many scientific applications can be considered as workflows made of multiple dependent steps, and hundreds of workflow systems have been developed to manage and run these scientific workflows. However, no turnkey solution has emerged from the field to address the diversity of scientific processes and the infrastructure on which they are supposed to be implemented. Instead, new research problems requiring the execution of scientific workflows with some novel feature often lead to the development of an entirely new workflow system. A direct consequence of this situation is that many existing workflow management systems (WMSs) share some salient features, offer similar functionalities, and can manage the same categories of workflows but at the same time also have some distinct capabilities that can be important for specific applications. This situation makes researchers who develop workflows face the complex question of selecting a WMS. This selection can be driven by technical considerations, to find the system that is the most appropriate for their application and for the computing and storage resources available to them, or other factors such as reputation, adoption, strong community support, or long-term sustainability. To address this problem, a group of WMS developers and practitioners joined their efforts to produce a community-based terminology of WMSs. This paper summarizes their findings and introduces this new terminology to characterize WMSs. This terminology is composed of fives axes: workflow structure and characteristics, composition, orchestration, data management, and metadata capture. Each axis comprises several concepts that capture the prominent features of WMSs. Based on this terminology, this paper also presents a classification of 23 existing WMSs according to the proposed axes and terms.
AB - The term “scientific workflow” has evolved over the last two decades to encompass a broad range of compositions of interdependent compute tasks and data movements. It has also become an umbrella term for processing in modern scientific applications. Today, many scientific applications can be considered as workflows made of multiple dependent steps, and hundreds of workflow systems have been developed to manage and run these scientific workflows. However, no turnkey solution has emerged from the field to address the diversity of scientific processes and the infrastructure on which they are supposed to be implemented. Instead, new research problems requiring the execution of scientific workflows with some novel feature often lead to the development of an entirely new workflow system. A direct consequence of this situation is that many existing workflow management systems (WMSs) share some salient features, offer similar functionalities, and can manage the same categories of workflows but at the same time also have some distinct capabilities that can be important for specific applications. This situation makes researchers who develop workflows face the complex question of selecting a WMS. This selection can be driven by technical considerations, to find the system that is the most appropriate for their application and for the computing and storage resources available to them, or other factors such as reputation, adoption, strong community support, or long-term sustainability. To address this problem, a group of WMS developers and practitioners joined their efforts to produce a community-based terminology of WMSs. This paper summarizes their findings and introduces this new terminology to characterize WMSs. This terminology is composed of fives axes: workflow structure and characteristics, composition, orchestration, data management, and metadata capture. Each axis comprises several concepts that capture the prominent features of WMSs. Based on this terminology, this paper also presents a classification of 23 existing WMSs according to the proposed axes and terms.
UR - https://www.scopus.com/pages/publications/105008892180
U2 - 10.1016/j.future.2025.107974
DO - 10.1016/j.future.2025.107974
M3 - Article
SN - 0167-739X
VL - 174
JO - Future Generation Computer Systems
JF - Future Generation Computer Systems
M1 - 107974
ER -