Energy Efficient Scheduling of Scientific Workflows in Cloud Environment

Ghose, Manojit; Verma, Pratyush; Karmakar, Sushanta; Sahu, Aryabartta

doi:10.1109/hpcc-smartcity-dss.2017.22

Cited by 16 publications

(10 citation statements)

References 22 publications

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“…In the context of scientific workflows, several works [6,7,8,9,10,11,12,13,14,15] have proposed energy-aware algorithms for task scheduling or resource provisioning. These algorithms are often designed to meet energy budget or deadline constraints.…”

Section: Related Workmentioning

confidence: 99%

“…Concurrently, researchers have investigated application-level techniques and algorithms to enable energy-efficient executions [5]. In the context of scientific workflows, researchers have proposed a range of energyaware workflow task scheduling or resource provisioning algorithms [6,7,8,9,10,11,12,13,14,15]. Results therein are obtained based on a model of power consumption that is easy to instantiate but that makes strong assumptions: power consumption is considered to be linearly correlated with CPU utilization, and equally divided among virtual machines or CPU cores within a computational node.…”

Section: Introductionmentioning

confidence: 99%

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

Silva

Casanova

Orgerie

et al. 2020

Journal of Computational Science

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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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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Silva

Casanova

Orgerie

et al. 2020

Journal of Computational Science

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“…Energy-aware workflow scheduling studies [8,17,20,23,30] typically assume that the power consumed by the execution of a task at time t, P (t), is linearly related to the task's CPU utilization, u(t), as:…”

Section: Workflow Energy Consumption Analysismentioning

confidence: 99%

“…In the context of scientific workflows, several works [8,17,20,23,30] have proposed energy-aware algorithms for task scheduling or resource provisioning. These algorithms are often designed to meet energy budget or deadline constraints.…”

Section: Related Workmentioning

confidence: 99%

“…Concurrently, researchers have investigated application-level techniques and algorithms to enable energy-efficient executions [19]. In the context of scientific workflows, researchers have proposed a range of energy-aware workflow task scheduling or resource provisioning algorithms [8,17,20,23,30]. Results therein are obtained based on a model of power consumption that is easy to instantiate but that makes strong assumptions: power consumption is considered to be linearly correlated with CPU utilization, and equally divided among virtual machines or CPU cores within a computational node.…”

Section: Introductionmentioning

confidence: 99%

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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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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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