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Workflow scheduling with data transfer optimisation and enhancement of reliability in cloud data centres

机译:具有数据传输优化和增强云数据中心可靠性的工作流计划

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Infrastructure as a service (IaaS) clouds offer huge opportunities to solve large-scale scientific problems. Executing workflows in such environments can be expensive in time if not scheduled rightly. Although scheduling workflows in the cloud is widely studied, most approaches focus on two user's quality of service requirements namely makespan (i.e., completion time) and costs. Other important features of cloud computing such as the heterogeneity of resources and reliability must be considered. In this paper, we present a reliability-aware method based on discrete particle swarm optimisation (RDPSO) for workflow scheduling in multiple and heterogeneous cloud data centres. Our aim is to optimise data transfer time while minimising makespan and enhancing reliability. Based on simulation, our results show a significant improvement in terms of makespan, transferred data and reliability relative to reliability-aware HEFT method (heterogeneous earliest finish time), for the real-world workflows.
机译:基础架构即服务(IaaS)云提供了解决大规模科学问题的巨大机会。如果没有正确安排,在这样的环境中执行工作流程可能会花费大量时间。尽管对云中的调度工作流进行了广泛的研究,但大多数方法都集中在两个用户的服务质量要求上,即制造期(即完成时间)和成本。必须考虑云计算的其他重要特征,例如资源的异构性和可靠性。在本文中,我们提出了一种基于离散粒子群优化(RDPSO)的可靠性感知方法,用于在多个异构云数据中心进行工作流调度。我们的目标是优化数据传输时间,同时最小化制造时间并提高可靠性。基于仿真,我们的结果表明,在实际工作流程中,相对于具有可靠性的HEFT方法(异构最早完成时间),在制造跨度,传输的数据和可靠性方面有了显着改善。

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