Energy Saving Algorithms for Workflow Scheduling in Cloud Computing

Watanabe, Elaine Naomi; Campos, Pedro Paulo Vezzá; Braghetto, Kelly Rosa; Batista, Daniel Macêdo

doi:10.1109/sbrc.2014.2

Cited by 6 publications

(3 citation statements)

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“…They achieved an 85% reduction in energy in some cases while achieving a 3.3% reduction in the make span of the workflows by using realistic energy consumption and performance models for task execution. New task schedulers focusing on the inter-dependency of tasks were introduced to achieve similar results [28]. They achieved a 22.7% reduction in energy at no cost to the make span of the algorithm.…”

Section: Evaluating Energy Usage In Computationmentioning

confidence: 99%

Measuring the Energy and Performance of Scientific Workflows on Low-Power Clusters

2022

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Scientific problems can be formulated as workflows to allow them to take advantage of cluster computing resources. Generally, the assumption is that the greater the resources dedicated to completing these tasks the better. This assumption does not take into account the energy cost of performing the computation and the specific characteristics of each workflow. In this paper, we present a unique approach to evaluating the energy consumption of scientific workflows on compute clusters. Two workflows from different domains, Astronomy and Bioinformatics, are presented and their execution is analyzed on a cluster of low powered small board computers. The paper presents a theoretical analysis of an energy-aware execution of workflows that can reduce the energy consumption of workflows by up to 68% compared to normal execution. We demonstrate that there are limitations to the benefits of increasing cluster sizes and there are trade-offs when considering energy vs. performance of the workflows and that the performance and energy consumption of any scientific workflow is heavily dependent on its underlying structure. The study concludes that the energy consumption of workflows can be optimized to improve both aspects of the workflow and motivates the development of an energy-aware scheduler.

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Section: Evaluating Energy Usage In Computationmentioning

confidence: 99%

Measuring the Energy and Performance of Scientific Workflows on Low-Power Clusters

2022

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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%

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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