Workflow Scheduling on Power Constrained VMs

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 have 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 energyefficient workflow scheduling literature.

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Accurately Simulating Energy Consumption of I/O-Intensive Scientific Workflows

Silva¹,

Orgerie

Casanova

et al. 2019

Lecture Notes in Computer Science

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Evaluating Energy-Aware Scheduling Algorithms for I/O-Intensive Scientific Workflows

Coleman¹,

Casanova

Gwartney

et al. 2021

Lecture Notes in Computer Science

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Workflow Scheduling on Power Constrained VMs

Cited by 2 publications

References 8 publications

Accurately Simulating Energy Consumption of I/O-Intensive Scientific Workflows

Accurately Simulating Energy Consumption of I/O-Intensive Scientific Workflows

Evaluating Energy-Aware Scheduling Algorithms for I/O-Intensive Scientific Workflows

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