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.

尽管分布式计算基础架构可以提供用于管理能耗的基础架构级别的技术，但也已经开发了应用程序级别的能耗模型来支持节能调度和资源供应算法。在这项工作中，我们分析了广泛使用的应用程序级模型的准确性，该模型已在科学的工作流执行环境中开发和使用。为此，我们在配备功率计的分布式平台上介绍了两个生产科学工作流程。然后，我们对功耗和能耗测量进行分析。该分析表明，功耗与CPU利用率不是线性关系，并且I / O操作会显着影响功耗，进而影响能耗。然后，我们提出一种功耗模型，该模型考虑了I / O操作，包括等待这些操作完成的影响以及在多路，多核计算节点上并发任务执行的影响。我们将我们提出的模型作为模拟器的一部分实施，该模拟器使我们能够在真实世界与建模的功耗和能耗之间进行直接比较。我们发现，与实际执行相比，我们的模型具有较高的准确性。此外，与节能工作流调度文献中使用的传统模型相比，我们的模型将准确性提高了大约两个数量级。我们将我们提出的模型作为模拟器的一部分实施，该模拟器使我们能够在真实世界与建模的功率和能耗之间进行直接比较。我们发现，与实际执行相比，我们的模型具有较高的准确性。此外，与节能工作流调度文献中使用的传统模型相比，我们的模型将准确性提高了大约两个数量级。我们将我们提出的模型作为模拟器的一部分实施，该模拟器使我们能够在真实世界与建模的功耗和能耗之间进行直接比较。我们发现，与实际执行相比，我们的模型具有较高的准确性。此外，与节能工作流调度文献中使用的传统模型相比，我们的模型将准确性提高了大约两个数量级。