结合构件应用性能模型的工作流现场自动调优
In-situ Workflow Auto-tuning via Combining Performance Models of Component Applications
摘要：
现场并行工作流通过流数据传输耦合多个组件应用程序，例如模拟和分析。以避免经由共享文件系统的数据交换。由于可能的配置空间很大，因此配置此类工作流以实现最佳性能具有挑战性。专家经验很少足以识别最佳配置，并且现有的经验自动调整方法由于获得用于机器学习模型的训练数据的高成本而效率低下。由于组件相互作用，单独优化单个组件也是不可行的。我们在这里提出了一种新的自动调整方法，基于代理的Ensemble主动学习（CEAL），结合机器学习技术与知识的现场工作流程结构，使自动化的工作流程配置与有限数量的性能测量。
英文摘要：
In-situ parallel workflows couple multiple component applications, such as simulation and analysis, via streaming data transfer. in order to avoid data exchange via shared file systems. Such workflows are challenging to configure for optimal performance due to the large space of possible configurations. Expert experience is rarely sufficient to identify optimal configurations, and existing empirical auto-tuning approaches are inefficient due to the high cost of obtaining training data for machine learning models. It is also infeasible to optimize individual components independently, due to component interactions. We propose here a new auto-tuning method, Component-based Ensemble Active Learning (CEAL), that combines machine learning techniques with knowledge of in-situ workflow structure to enable automated workflow configuration with a limited number of performance measurements.
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