A Performance Model to Estimate Execution Time of Scientific Workflows on the Cloud
A Performance Model to Estimate Execution Time of Scientific Workflows on the Cloud
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DOI：
10.1109/works.2014.12
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发表时间：
2014-11
期刊：
影响因子：
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通讯作者：
Ilia Pietri;G. Juve;E. Deelman;R. Sakellariou
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文献类型：
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作者：
Ilia Pietri;G. Juve;E. Deelman;R. Sakellariou
捕获大量计算问题的科学工作流可以在大规模的分布式系统（例如云）上执行。确定为执行科学工作流提供的资源数量是实现成本效益的资源管理和良好绩效的关键组成部分。在本文中，提出了一个绩效预测模型，以考虑其结构及其依赖于系统的特征，以估计科学工作流的执行时间。在评估中，使用三个现实世界的科学工作流来比较该模型计算出的估计的制造PAN与在Amazon EC2的不同系统配置上实现的实际MakePAN。结果表明，所提出的模型可以预测执行时间，而超过96.8％的实验的误差小于20％。
Scientific workflows, which capture large computational problems, may be executed on large-scale distributed systems such as Clouds. Determining the amount of resources to be provisioned for the execution of scientific workflows is a key component to achieve cost-efficient resource management and good performance. In this paper, a performance prediction model is presented to estimate execution time of scientific workflows for a different number of resources, taking into account their structure as well as their system-dependent characteristics. In the evaluation, three real-world scientific workflows are used to compare the estimated makespan calculated by the model with the actual makespan achieved on different system configurations of Amazon EC2. The results show that the proposed model can predict execution time with an error of less than 20% for over 96.8% of the experiments..