37 lines
3.2 KiB
Markdown
37 lines
3.2 KiB
Markdown
tags:: [[ReadingNotes]]
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date:: 12/2021
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issn:: 25426605
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doi:: 10.1016/j.iot.2021.100440
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title:: @Conceptualization and scalable execution of big data workflows using domain-specific languages and software containers
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pages:: 100440
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volume:: 16
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item-type:: [[journalArticle]]
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access-date:: 2023-01-18T12:07:58Z
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original-title:: Conceptualization and scalable execution of big data workflows using domain-specific languages and software containers
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language:: en
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url:: https://linkinghub.elsevier.com/retrieve/pii/S2542660521000834
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publication-title:: Internet of Things
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journal-abbreviation:: Internet of Things
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authors:: [[Nikolay Nikolov]], [[Yared Dejene Dessalk]], [[Akif Quddus Khan]], [[Ahmet Soylu]], [[Mihhail Matskin]], [[Amir H. Payberah]], [[Dumitru Roman]]
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library-catalog:: DOI.org (Crossref)
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links:: [Local library](zotero://select/library/items/9WPJ3Y5N), [Web library](https://www.zotero.org/users/1039502/items/9WPJ3Y5N)
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- [[Abstract]]
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- Big Data processing, especially with the increasing proliferation of Internet of Things (IoT) technologies and convergence of IoT, edge and cloud computing technologies, involves handling massive and complex data sets on heterogeneous resources and incorporating different tools, frameworks, and processes to help organizations make sense of their data collected from various sources. This set of operations, referred to as Big Data workflows, requires taking advantage of Cloud infrastructures’ elasticity for scalability. In this article, we present the design and prototype implementation of a Big Data workflow approach based on the use of software container technologies, message-oriented middleware (MOM), and a domain-specific language (DSL) to enable highly scalable workflow execution and abstract workflow definition. We demonstrate our system in a use case and a set of experiments that show the practical applicability of the proposed approach for the specification and scalable execution of Big Data workflows. Furthermore, we compare our proposed approach’s scalability with that of Argo Workflows – one of the most prominent tools in the area of Big Data workflows – and provide a qualitative evaluation of the proposed DSL and overall approach with respect to the existing literature.
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- [[Attachments]]
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- [Nikolov et al. - 2021 - Conceptualization and scalable execution of big da.pdf](zotero://select/library/items/QYUBVQAD) {{zotero-imported-file QYUBVQAD, "Nikolov et al. - 2021 - Conceptualization and scalable execution of big da.pdf"}}
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- [[Highlights]]
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- ((63c906c7-b466-43a5-b10f-60888e50c43b))
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- ((63c9067a-740c-4655-87e3-5b3bd74dac41))
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- ((63c906ee-8b20-4a86-977d-ae6227effaa5)) [[New Motivations]]
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- ((63c9073e-b5ea-489d-a7c7-073fc7f42ade)) [[New Motivations]]
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- ((63c90746-161a-4dff-a651-0e37cd1bdd6d)) [[New Motivations]]
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- ((63c913c2-8cb1-4b36-8a39-9d871c440104)) [[New Motivations]]
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- ((63c91a5f-d65f-4093-bd68-857ebf016a2d)) [[New Motivations]]
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- ((63c91ab8-3355-4a4b-bb07-0833b5c5f506))
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- **GOAL OF THE APPROACH**
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- ((63c913e1-53f6-4423-a3a1-2c73281aeed6))
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- ((63c913e6-9a90-4d8b-a6a6-6094b67725ca))
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- ((63c91848-d3d0-47cb-93e2-61070479dc67))
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- We could be container technology independent? (aka TYPHONDL???)
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- ((63ca5656-a823-48d9-a94a-b150c12863fe)) |