Amazon Elastic Compute Cloud (EC2) vs. In-House HPC Platform: A Cost Analysis

Emeras, Joseph; Varrette, Sébastien; Bouvry, Pascal

doi:10.1109/cloud.2016.0046

Cited by 14 publications

(12 citation statements)

References 17 publications

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“…For other applications, the cost comparison could be either in favor of the cloud or local clusters, depending on the use case, the computational efficiency, and the choice of cost model (Section 2.1 of Netto et al, 2017; Roloff et al, 2017; Siuta et al, 2016; Thackston & Fortenberry, 2015). Our results contrast with some previous studies concluding that the AWS cloud was not cost‐efficient compared to local HPC clusters (e.g., Chang et al, 2018; Emeras et al, 2017), partly because these studies did not take advantage of recent advances in cloud networking.…”

Section: Application To Massively Parallel Geos‐chem Simulations On Tcontrasting

confidence: 99%

Enabling High‐Performance Cloud Computing for Earth Science Modeling on Over a Thousand Cores: Application to the GEOS‐Chem Atmospheric Chemistry Model

Zhuang

Jacob

Lin

et al. 2020

J Adv Model Earth Syst

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Cloud computing platforms can facilitate the use of Earth science models by providing immediate access to fully configured software, massive computing power, and large input data sets. However, slow internode communication performance has previously discouraged the use of cloud platforms for massively parallel simulations. Here we show that recent advances in the network performance on the Amazon Web Services cloud enable efficient model simulations with over a thousand cores. The choices of Message Passing Interface library configuration and internode communication protocol are critical to this success. Application to the Goddard Earth Observing System (GEOS)-Chem global 3-D chemical transport model at 50-km horizontal resolution shows efficient scaling up to at least 1,152 cores, with performance and cost comparable to the National Aeronautics and Space Administration Pleiades supercomputing cluster.Plain Language Summary Earth science model simulations are computationally expensive, typically requiring the use of high-end supercomputing clusters that are managed by universities or national laboratories. Commercial cloud computing offers an alternative. However, past work found that cloud computing platforms were not efficient for large-scale simulations on over 100 CPU cores, because the network communication performance on the cloud was slow compared to local clusters. Here we show that recent advances in the cloud network performance enable efficient model simulations with over a thousand cores, and cloud platforms can now serve as a viable alternative to local clusters for simulations at large scale. Computing on the cloud has extensive advantages, such as providing immediate access to fully configured model code and large data sets for any users, allowing full reproducibility of model simulation results, offering quick access to novel hardware that might not be available on local clusters, and being able to scale to virtually unlimited amounts of compute and storage resources. Those benefits will help advance Earth science modeling research.

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Section: Application To Massively Parallel Geos‐chem Simulations On Tcontrasting

confidence: 99%

Enabling High‐Performance Cloud Computing for Earth Science Modeling on Over a Thousand Cores: Application to the GEOS‐Chem Atmospheric Chemistry Model

Zhuang

Jacob

Lin

et al. 2020

J Adv Model Earth Syst

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“…In this context, [315] proposed a TCO analysis for this in-house HPC facility for the time period 2007-2014 which is reported in the next sections. Also, although the comparative performance of Cloud vs. HPC systems received a wide audience in the recent literature, the analogous analysis covering the costs remains at an early stage, with very few contribution from the academic community.…”

Section: A Full-cost Model For Estimating the Energy Consumption Of Cmentioning

confidence: 99%

“…Furthermore the direct comparison of a given Cloud instance price with an HPC operating cost is biased and should be taken with precautions as it omits the performance aspect where the Cloud instance performance does not match with the one of an HPC node. To feed this gap and allowing a fair cost analysis, the approach proposed in [315] was twofold. First a theoretical price -performance model was established based on the study of the actual Cloud instances proposed by one of the major Cloud IaaS actors (Amazon).…”

Section: A Full-cost Model For Estimating the Energy Consumption Of Cmentioning

confidence: 99%

“…In this section, and inspired by the work proposed in [315], we propose a TCO analysis of an in-house academic HPC facility of medium-size (in particular the one operated at the University of Luxembourg since 2007, or within the Grid'5000 project [319]), and compare it with the investment that would have been required to run the same platform (and the same workload) over a competitive Cloud IaaS offer.…”

Section: A Full-cost Model For Estimating the Energy Consumption Of Cmentioning

confidence: 99%

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Energy aware ultrascale systems

Oleksiak

Lefèvre

Alonso

et al. 2019

Ultrascale Computing Systems

Self Cite

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HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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“…In [44], Emeras et al made a cost analysis of running a workload on a cloud as compared to processing it on local high-performance computing platforms. A pricing model was proposed to compare the costs of running high performance computing applications on local clusters of an in-house facility with Amazon EC2 instances.…”

Section: Techniques To Estimate the Net Profit In Cloudsmentioning

confidence: 99%

A User Relinquishment-based Resource Assignment Scheme to Maximize the Net Profit of Cloud Service Providers

Singh¹

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In a cloud federation, by using the pay-as-you-go billing model users can relinquish their services at any point in time and pay accordingly. Therefore, this thesis aims to study the resource assignment problem in the situation where the user relinquishment impacts the net profit of a cloud service provider. As a solution, our study 1) proposes a tool to calculate the net profit which includes income, electricity expenses, and relinquishment loss; 2) compares different ways to predict the user behavior and deduce a better prediction technique based on linear regression; and 3) proposes a relinquishment-aware resource optimization model to estimate the amount of resources based upon the predicted user behavior. Simulations were performed with the CloudSim framework. The results show that instead of blindly assigning resources to users, a cloud service provider with a finite resource pool can gain more by estimating the resources using better prediction techniques.ii

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Amazon Elastic Compute Cloud (EC2) vs. In-House HPC Platform: A Cost Analysis

Cited by 14 publications

References 17 publications

Enabling High‐Performance Cloud Computing for Earth Science Modeling on Over a Thousand Cores: Application to the GEOS‐Chem Atmospheric Chemistry Model

Enabling High‐Performance Cloud Computing for Earth Science Modeling on Over a Thousand Cores: Application to the GEOS‐Chem Atmospheric Chemistry Model

Energy aware ultrascale systems

A User Relinquishment-based Resource Assignment Scheme to Maximize the Net Profit of Cloud Service Providers

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