Evaluating Energy-Aware Scheduling Algorithms for I/O-Intensive Scientific Workflows
Evaluating Energy-Aware Scheduling Algorithms for I/O-Intensive Scientific Workflows
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DOI：
10.1007/978-3-030-77961-0_16
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
T. Coleman;H. Casanova;Ty Gwartney;Rafael Ferreira da Silva
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文献类型：
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作者：
T. Coleman;H. Casanova;Ty Gwartney;Rafael Ferreira da Silva
提高能源效率是实现可持续计算科学的必要条件。与此同时，科学工作流程是促进几乎所有领域科学的分布式计算的关键。随着数据和计算需求的增加，I/O密集型工作流已经变得普遍。在这项工作中，我们评估两个流行的能量感知工作流调度算法的能力，为这类工作流应用程序提供有效的时间表，也就是说，时间表罢工之间的工作流执行时间和能量消耗很好的妥协。这两种算法基于广泛使用的功耗模型做出决策，该模型简单地假设与CPU使用率线性相关。以前的工作表明，这种模型是不准确的，特别是对I/O密集型工作流执行的功耗建模，并提出了一个准确的模型。我们评估的有效性，上述两种算法的基础上，这个准确的模型。我们发现，在做出决策时，这些算法可能会低估高达360%的功耗，这使得我们不清楚这些算法在实践中的表现如何。为了评估在实践中使用更准确的功耗模型的好处，我们提出了一个简单的调度算法，该算法依赖于此模型来平衡可用计算资源之间的I/O负载。实验结果表明，该算法在能量消耗和工作流执行时间之间取得了比两种流行算法更好的折衷。
Improving energy efficiency has become necessary to enable sustainable computational science. At the same time, scientific workflows are key in facilitating distributed computing in virtually all domain sciences. As data and computational requirements increase, I/O-intensive workflows have become prevalent. In this work, we evaluate the ability of twopopular energy-aware workflow scheduling algorithms to provide effective schedules for this class of workflow applications, that is, schedules that strike a good compromise between workflow execution time and energy consumption. These two algorithms make decisions based on a widely used power consumption model that simply assumes linear correlation to CPU usage. Previous work has shown this model to be inaccurate, in particular for modeling power consumption of I/O-intensive workflow executions, and has proposed an accurate model. We evaluate the effectiveness of the two aforementioned algorithms based on this accurate model. We find that, when making their decisions, these algorithms can underestimate power consumption by up to 360%, which makes it unclear how well these algorithm would fare in practice. To evaluate the benefit of using the more accurate power consumption model in practice, we propose a simple scheduling algorithm that relies on this model to balance the I/O load across the available compute resources. Experimental results show that this algorithm achieves more desirable compromises between energy consumption and workflow execution time than the two popular algorithms.