Characterizing, Modeling, and Accurately Simulating Power and Energy Consumption of I/O-intensive Scientific Workflows
Characterizing, Modeling, and Accurately Simulating Power and Energy Consumption of I/O-intensive Scientific Workflows
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DOI：
10.1016/j.jocs.2020.101157
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发表时间：
2020-07-01
影响因子：
3.3
通讯作者：
Suter, Frederic
中科院分区：
文献类型：
--
作者：
da Silva, Rafael Ferreira;Casanova, Henri;Suter, Frederic
虽然分布式计算基础设施可以提供基础设施级别的技术来管理能源消耗，但也开发了应用程序级别的能源消耗模型来支持节能调度和资源供应算法。在这项工作中，我们分析了一个广泛使用的应用层模型的准确性，该模型已经开发并在科学工作流执行的背景下使用。为此，我们在一个配备了电能表的分布式平台上分析了两个生产科学工作流。然后，我们对电力和能源消耗测量进行分析。该分析表明，功耗与CPU利用率不是线性相关的，并且I/O操作会显著影响功耗，从而影响能耗。然后，我们提出了一个考虑I/O操作的功耗模型，包括等待这些操作完成的影响，以及多插槽、多核计算节点上的并发任务执行。我们将我们提出的模型作为模拟器的一部分实现，该模拟器允许我们在真实世界和模拟的功率和能源消耗之间进行直接比较。我们发现，与真实世界的执行相比，我们的模型具有很高的准确性。此外，与节能工作流调度文献中使用的传统模型相比，我们的模型将准确率提高了大约两个数量级。(C)2020爱思唯尔B.V.保留所有权利。
While distributed computing infrastructures can provide infrastructure-level techniques for managing energy consumption, application-level energy consumption models have also been developed to support energy-efficient scheduling and resource provisioning algorithms. In this work, we analyze the accuracy of a widely-used application-level model that has been developed and used in the context of scientific workflow executions. To this end, we profile two production scientific workflows on a distributed platform instrumented with power meters. We then conduct an analysis of power and energy consumption measurements. This analysis shows that power consumption is not linearly related to CPU utilization and that I/O operations significantly impact power, and thus energy, consumption. We then propose a power consumption model that accounts for I/O operations, including the impact of waiting for these operations to complete, and for concurrent task executions on multi-socket, multi-core compute nodes. We implement our proposed model as part of a simulator that allows us to draw direct comparisons between real-world and modeled power and energy consumption. We find that our model has high accuracy when compared to real-world executions. Furthermore, our model improves accuracy by about two orders of magnitude when compared to the traditional models used in the energy-efficient workflow scheduling literature. (C) 2020 Elsevier B.V. All rights reserved.