Publikation: Big Data Pipeline Scheduling and Adapta...
Stammdaten
Titel: | Big Data Pipeline Scheduling and Adaptation on the Computing Continuum |
Untertitel: | |
Kurzfassung: | The Computing Continuum, covering Cloud, Fog, and Edge systems, promises to provide on-demand resource-as-a-service for Internet applications with diverse requirements, ranging from extremely low latency to high-performance processing. However, eminent challenges in automating the resources man-agement of Big Data pipelines across the Computing Continuum remain. The resource management and adaptation for Big Data pipelines across the Computing Continuum require significant research effort, as the current data processing pipelines are dynamic. In contrast, traditional resource management strategies are static, leading to inefficient pipeline scheduling and overly complex process deployment. To address these needs, we propose in this work a scheduling and adaptation approach implemented as a software tool to lower the technological barriers to the management of Big Data pipelines over the Computing Continuum. The approach separates the static scheduling from the run-time execution, em-powering domain experts with little infrastructure and software knowledge to take an active part in the Big Data pipeline adaptation. We conduct a feasibility study using a digital healthcare use case to validate our approach. We illustrate concrete scenarios supported by demonstrating how the scheduling and adaptation tool and its implementation automate the management of the lifecycle of a remote patient monitoring, treatment, and care pipeline. |
Schlagworte: | Scheduling, Adaptation, Computing Continuum, Fog and Edge computing, Resources management |
Publikationstyp: | Beitrag in Proceedings (Autorenschaft) |
Erscheinungsdatum: | 06.2022 (Print) |
Erschienen in: |
COMPSAC'22 Proceedings of the 2022 IEEE 46th Annual Computers, Software, and Applications Conference
COMPSAC'22 Proceedings of the 2022 IEEE 46th Annual Computers, Software, and Applications Conference
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IEEE Xplore Digital Library;
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zur Publikation |
Titel der Serie: | - |
Bandnummer: | - |
Erstveröffentlichung: | Ja |
Version: | - |
Seite: | S. 1153 - 1158 |
Versionen
Keine Version vorhanden |
Erscheinungsdatum: | 06.2022 |
ISBN: |
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ISSN: | - |
Homepage: | https://ieeexplore.ieee.org/document/9842650 |
Erscheinungsdatum: | 10.08.2022 |
ISBN (e-book): | - |
eISSN: | - |
DOI: | http://dx.doi.org/10.1109/compsac54236.2022.00181 |
Homepage: | https://ieeexplore.ieee.org/document/9842650 |
Open Access |
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AutorInnen
Dragi Kimovski (intern) |
Christian Bauer (intern) |
Narges Mehran (intern) |
Radu Aurel Prodan (intern) |
Zuordnung
Organisation | Adresse | ||||
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Fakultät für Technische Wissenschaften
Institut für Informationstechnologie
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AT - 9020 Klagenfurt am Wörthersee |
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Peer Reviewed |
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