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.

中文翻译：

---

具有截止日期和可靠性约束的云上成本驱动的工作流调度

云正在成为科学工作流应用程序的有效平台。与此同时，云计算结构正朝着更加异构的方向发展。在异构面向服务的系统中，由于处理器和通信故障影响用户的服务质量要求，管理资源（例如，处理器和通信网络）的可靠性被广泛认为是一个关键问题。因此，在调度算法时应考虑这些类型的故障。本文提出了一种调度方法，其中包括四种算法，用于最小化工作流执行成本，同时满足用户指定的截止日期和可靠性。为了满足应用程序的要求，第一个算法根据称为 CbCP 的关键父级将工作流划分为多个集群。之后，由可靠性和期限分配方法组成的资源分配算法满足应用程序的约束。以真实和随机方式以不同规模生成的各种工作流程的实验结果表明，所提出的启发式方法符合截止日期和可靠性。与最先进的 DRR 和 QFEC+ 算法的性能相比，这确保了在执行类似服务质量时的最低成本。