Clouds are becoming an effective platform for scientific workflow applications. In the meantime, Cloud computing structures are moving towards being more heterogeneous. In heterogeneous service-oriented systems, managing the reliability of resources (e.g., processors and communication networks) is widely identified as a critical issue due to processor and communication failures affecting user quality of service requirements. Therefore, these types of failures should be taken into account when scheduling algorithms. The present paper proposes a scheduling approach which includes four algorithms for minimizing the workflow execution cost while also meeting the user-specified deadline and reliability. To meet the application’s requirements, the first algorithm partitions the workflow into several clusters based on a critical parent called CbCP. After that, the resource assignment algorithm, consisting of reliability and deadline distribution methods, satisfies the application’s constraints. Experimental outcomes on various workflows, generated at different scales in real and random fashion, demonstrate that the proposed heuristics meet the deadline and reliability. This ensures the minimal cost when performing a similar quality of service as opposed to the performance of the state-of-the-art DRR and QFEC+ algorithms.

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