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Formal Approach to Workflow Application Fragmentations Over Cloud Deployment Models

Hyun Ahn, Kwanghoon Pio Kim*

Division of Computer Science and Engineering, Gyeonggi, 16227, Korea

* Corresponding Author: Kwanghoon Pio Kim. Email:

Computers, Materials & Continua 2021, 67(3), 3071-3088.


Workflow management technologies have been dramatically improving their deployment architectures and systems along with the evolution and proliferation of cloud distributed computing environments. Especially, such cloud computing environments ought to be providing a suitable distributed computing paradigm to deploy very large-scale workflow processes and applications with scalable on-demand services. In this paper, we focus on the distribution paradigm and its deployment formalism for such very large-scale workflow applications being deployed and enacted across the multiple and heterogeneous cloud computing environments. We propose a formal approach to vertically as well as horizontally fragment very large-scale workflow processes and their applications and to deploy the workflow process and application fragments over three types of cloud deployment models and architectures. To concretize the formal approach, we firstly devise a series of operational situations fragmenting into cloud workflow process and application components and deploying onto three different types of cloud deployment models and architectures. These concrete approaches are called the deployment-driven fragmentation mechanism to be applied to such very large-scale workflow process and applications as an implementing component for cloud workflow management systems. Finally, we strongly believe that our approach with the fragmentation formalisms becomes a theoretical basis of designing and implementing very large-scale and maximally distributed workflow processes and applications to be deployed on cloud deployment models and architectural computing environments as well.


Cite This Article

H. Ahn and K. Pio Kim, "Formal approach to workflow application fragmentations over cloud deployment models," Computers, Materials & Continua, vol. 67, no.3, pp. 3071–3088, 2021.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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