Deadline-Constrained Cost Minimisation for Cloud Computing Environments

The interest in performing scientific computations using commercially available cloud computing resources has grown rapidly in the last decade. However, scheduling multiple workflows in cloud computing is challenging due to its non-functional constraints and multi-dimensional resource requirements....

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Veröffentlicht in:IEEE access Jg. 11; S. 38514 - 38522
Hauptverfasser: Manam, Samuel, Moessner, Klaus, Vural, Serdar
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Cloud computing
Constraints
Costs
deadline constraints
Delays
Dynamic scheduling
optimisation
Optimization
Resource scheduling
Scheduling algorithms
scientific workflow
Task analysis
Task scheduling
ISSN:2169-3536, 2169-3536
Online-Zugang:Volltext
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Zusammenfassung:The interest in performing scientific computations using commercially available cloud computing resources has grown rapidly in the last decade. However, scheduling multiple workflows in cloud computing is challenging due to its non-functional constraints and multi-dimensional resource requirements. Scheduling algorithms proposed in literature use search-based approaches which often result in very high computational overhead and long execution time. In this paper, a Deadline-Constrained Cost Minimisation (DCCM) algorithm is proposed for resource scheduling in cloud computing. In the proposed scheme, tasks were grouped based on their scheduling deadline constraints and data dependencies. Compared to other approaches, DCCM focuses on meeting the user-defined deadline by sub-dividing tasks into different levels based on their priorities. Simulation results showed that DCCM achieved higher success rates when compared to the state-of-the-art approaches.
Bibliographie:ObjectType-Article-1
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3258682