Cloud computing is recently getting increasingly popular for supporting scientific applications and complex business processes. Clouds are highly potent for executing workflow-based tasks due to the fact that they provide elastic resource provisioning styles through which computational-intensive workflows can obtain requested resources according to their elastic demand and establish execution environment over virtual machines (VMs). However, it remains a challenge to guarantee cost-effectiveness and quality of service of workflow deployed upon clouds due to the fact that real-world cloud infrastructures are usually with fluctuating and time-varying performance. Existing researches mainly consider that cloud infrastructures are with fixed, random, or bounded quality of service (QoS). In this work, however, we consider that scientific computing processes to be supported by decentralized cloud infrastructures with fluctuating QoS and aim at managing the monetary cost of workflows with the completion-time constraint to be satisfied. We address the performance-variation-aware workflow scheduling problem by leveraging a time-series-based prediction model and a Critical-Path-Duration-Estimation-based (CPDE for short) VM Selection strategy. The proposed method is capable of exploiting real-time trends of performance changes of cloud infrastructures and generating dynamic workflow scheduling plans. To prove the effectiveness of our proposed method, we perform extensive experimental case analysis over real-world third-party commercial clouds and show that our method clearly beats existing approaches.

中文翻译：

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一种在性能波动的情况下基于云资源调度工作流的新方法

云计算最近因支持科学应用程序和复杂的业务流程而变得越来越流行。由于云提供了弹性的资源供应方式，计算密集型工作流可以根据其弹性需求获取所需的资源，并在虚拟机（VM）上建立执行环境，因此，云对于执行基于工作流的任务具有很高的效力。但是，由于现实世界中的云基础架构通常具有波动和时变的性能，因此，确保部署在云上的工作流的成本效益和服务质量仍然是一个挑战。现有研究主要考虑云基础架构具有固定，随机或有界的服务质量（QoS）。但是，在这项工作中 我们认为，科学的计算流程将受到QoS波动的分散式云基础架构的支持，旨在通过满足完成时间约束来管理工作流程的货币成本。我们通过利用基于时间序列的预测模型和基于关键路径持续时间估算（简称CPDE）的VM选择策略来解决性能变化感知的工作流调度问题。所提出的方法能够利用云基础设施性能变化的实时趋势并生成动态工作流调度计划。为了证明我们提出的方法的有效性，我们在真实的第三方商业云上进行了广泛的实验案例分析，并表明我们的方法明显优于现有方法。