Deadline-Constrained Cost Minimisation for Cloud Computing Environments

• Published in: IEEE access Vol. 11; p. 1
• Main Authors: Manam, Samuel, Moessner, Klaus, Vural, Serdar
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-01-2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 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
• DOI: 10.1109/ACCESS.2023.3258682

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 are 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 show that DCCM achieves higher success rate when compared to the state-of-the-art approaches.