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Characterizing, Modeling, and Accurately Simulating Power and Energy Consumption of I/O-intensive Scientific Workflows

Abstract : 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.
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https://hal.archives-ouvertes.fr/hal-02876736
Contributor : Anne-Cécile Orgerie <>
Submitted on : Sunday, June 21, 2020 - 4:40:43 PM
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Rafael Ferreira da Silva, Henri Casanova, Anne-Cécile Orgerie, Ryan Tanaka, Ewa Deelman, et al.. Characterizing, Modeling, and Accurately Simulating Power and Energy Consumption of I/O-intensive Scientific Workflows. Journal of computational science, Elsevier, 2020, 44, pp.101157. ⟨10.1016/j.jocs.2020.101157⟩. ⟨hal-02876736⟩

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