A Machine Learning-Based Approach for Predicting the Execution Time of CFD Applications on Cloud Computing Environment

Hieu, Duong Ngoc; Minh, Thai Tieu; Quang, Trinh Van; Giang, Bui Xuan; Hoài, Trần Văn

doi:10.1007/978-3-319-48057-2_3

Cited by 6 publications

(3 citation statements)

References 9 publications

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“…Additionally, as another solution to deal with computational limitations, machine learning can be employed to mimic the behaviour of the complex solid/fluid systems. The main idea is to save the computational sources by solving a series of typical/representative problems and extend the results to all similar cases [37,38,39]. For this purpose, we need to break down a large problem into several small independent pieces and try to find computationally inexpensive features which are statistically related to the original problem.…”

Section: Direct Simulation Methodsmentioning

confidence: 99%

Hybrid pore-network and lattice-Boltzmann permeability modelling accelerated by machine learning

Rabbani

Babaei

2019

Advances in Water Resources

125

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Section: Direct Simulation Methodsmentioning

confidence: 99%

Hybrid pore-network and lattice-Boltzmann permeability modelling accelerated by machine learning

Rabbani

Babaei

2019

Advances in Water Resources

125

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“…Predicting the execution time of applications is commonly studied [Hieu et al, 2016;Malakar et al, 2018;Kim et al, 2019]. [Hieu et al, 2016] used applications in computational fluid dynamics (CFD). Those CFD applications were executed in a cloud environment.…”

Section: Related Workmentioning

confidence: 99%

Estimating the execution time of fully-online multiscale numerical simulations

Fabian¹,

Gomes²,

Ogasawara³

2020

Anais Do XXI Simpósio Em Sistemas Computacionais De Alto Desempenho (SSCAD 2020)

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In this paper, we propose a methodology for estimating the execution time of simulations driven by multiscale numerical methods. The methodology explores the idiosyncrasies of multiscale simulators to reduce the uncertainty of predictions. We use the multiscale hybrid-mixed (MHM) ﬁnite element method to validate our methodology. We compare our proposed technique with prediction models automatically selected and calibrated by Auto-WEKA. We show that the models obtained with our technique are competitive when compared with the models coming from Auto-WEKA, being interpretable and with much less computational effort during the learning process.

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“…Performance prediction using machine learning for improving the quality of system management decisions has been investigated in [23]. Authors in [24] used machine learning to predict the execution time of computational fluid dynamics applications in the cloud. These predictions were used in scheduling algorithms.…”

Section: Related Workmentioning

confidence: 99%

Distributing the computation in combinatorial optimization experiments over the cloud

Brčić¹,

Hlupić²,

Katanic³

2017

Adv. sci. technol. eng. syst. j.

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Combinatorial optimization is an area of great importance since many of the real-world problems have discrete parameters which are part of the objective function to be optimized. Development of combinatorial optimization algorithms is guided by the empirical study of the candidate ideas and their performance over a wide range of settings or scenarios to infer general conclusions. Number of scenarios can be overwhelming, especially when modeling uncertainty in some of the problem's parameters. Since the process is also iterative and many ideas and hypotheses may be tested, execution time of each experiment has an important role in the efficiency and successfulness. Structure of such experiments allows for significant execution time improvement by distributing the computation. We focus on the cloud computing as a cost-efficient solution in these circumstances. In this paper we present a system for validating and comparing stochastic combinatorial optimization algorithms. The system also deals with selection of the optimal settings for computational nodes and number of nodes in terms of performance-cost tradeoff. We present applications of the system on a new class of project scheduling problem. We show that we can optimize the selection over cloud service providers as one of the settings and, according to the model, it resulted in a substantial cost-savings while meeting the deadline.

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A Machine Learning-Based Approach for Predicting the Execution Time of CFD Applications on Cloud Computing Environment

Cited by 6 publications

References 9 publications

Hybrid pore-network and lattice-Boltzmann permeability modelling accelerated by machine learning

Hybrid pore-network and lattice-Boltzmann permeability modelling accelerated by machine learning

Estimating the execution time of fully-online multiscale numerical simulations

Distributing the computation in combinatorial optimization experiments over the cloud

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