Toward Generalized Neural Model for VMs Power Consumption Estimation in Data Centers

Lei, Yu; Zhang, Guangxing; Li, Zhenyu; Liangs, Wei; Xie, Gaogang

doi:10.1109/icc.2019.8762017

Cited by 8 publications

(3 citation statements)

References 20 publications

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“…Another work focusing on a neural model for estimating the power consumption of virtual machines was presented in [28]. The authors proposed a deep neural network model to estimate power consumption of virtual machines, considering the impact of CPU, memory, disk, and network resource utilization.…”

Section: Related Workmentioning

confidence: 99%

A Guide to Data Collection for Computation and Monitoring of Node Energy Consumption

Río

Conti

Castaño-Solís

et al. 2023

BDCC

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The digital transition that drives the new industrial revolution is largely driven by the application of intelligence and data. This boost leads to an increase in energy consumption, much of it associated with computing in data centers. This fact clashes with the growing need to save and improve energy efficiency and requires a more optimized use of resources. The deployment of new services in edge and cloud computing, virtualization, and software-defined networks requires a better understanding of consumption patterns aimed at more efficient and sustainable models and a reduction in carbon footprints. These patterns are suitable to be exploited by machine, deep, and reinforced learning techniques in pursuit of energy consumption optimization, which can ideally improve the energy efficiency of data centers and big computing servers providing these kinds of services. For the application of these techniques, it is essential to investigate data collection processes to create initial information points. Datasets also need to be created to analyze how to diagnose systems and sort out new ways of optimization. This work describes a data collection methodology used to create datasets that collect consumption data from a real-world work environment dedicated to data centers, server farms, or similar architectures. Specifically, it covers the entire process of energy stimuli generation, data extraction, and data preprocessing. The evaluation and reproduction of this method is offered to the scientific community through an online repository created for this work, which hosts all the code available for its download.

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Section: Related Workmentioning

confidence: 99%

A Guide to Data Collection for Computation and Monitoring of Node Energy Consumption

Río

Conti

Castaño-Solís

et al. 2023

BDCC

View full text Add to dashboard Cite

show abstract

“…Correlation analysis is frequently utilized to evaluate the relationship between variables and determine the suitable ones as predictors to be used in estimation algorithms [18,[21][22][23][24]. Thus, in this study, the correlation matrix, which includes Pearson correlation coefficients, was initially created for 70 variables obtained from measured data in order to determine prospective predictors and reveal their effects on the power consumption of the ARGE rack.…”

Section: Correlation Analysismentioning

confidence: 99%

“…However, in order to determine the most convenient predictors, recent studies generally prefer to utilize only correlation analysis or another specific feature-selection method, instead of taking into account the direct effect of predictors on estimation results [4,6,7,[18][19][20][21][22][23][24]. In this paper, the individual and group effects of various variables (e.g., CPU and RAM usage ratios, temperature, network load, etc.)…”

Section: Introduction 1background and Motivationmentioning

confidence: 99%

Effects of Predictors on Power Consumption Estimation for IT Rack in a Data Center: An Experimental Analysis

Takcı

Gözel

2022

Sustainability

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The appropriate feature/predictor selection is as significant as building efficient estimation methods for the accurate estimation of power consumption, which is required for self-awareness and autonomous decision systems. Traditional methodologies define predictors by assessing whether there is a relationship between the predictors and the response variable. Contrarily, this study determines predictors based on their individual and group impacts on the estimation accuracy directly. To analyze the impact of predictors on the power-consumption estimation of an IT rack in a data center, estimations were carried out employing each prospective predictor separately using the measured data under the real-world workload. Then, the ratio of CPU usage was set as the default predictor, and the remaining variables were assigned as the second predictor one by one. By utilizing the same approach, the best combination of predictors was determined. As a result, it was discovered that some variables with a low correlation coefficient with power consumption improved the estimation accuracy, whereas some variables with high correlation coefficients worsened the estimation result. The CPU is the most power-consuming component in the server and one of the most used predictors in the literature. However, the estimation accuracy obtained using only the CPU is 10 times worse than the estimation result conducted by utilizing the predictor set determined at the end of the experiments. This study shows that instead of choosing predictors only from one point of view or one method, it is more convenient to select predictors by assessing their influence on estimation results. Examining the trend and characteristics of the estimated variable should also be considered.

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Power Consumption Estimation in Data Centers Using Machine Learning Techniques

Karantoumanis

Πλόσκας

2020

Lecture Notes in Computer Science

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Toward Generalized Neural Model for VMs Power Consumption Estimation in Data Centers

Cited by 8 publications

References 20 publications

A Guide to Data Collection for Computation and Monitoring of Node Energy Consumption

A Guide to Data Collection for Computation and Monitoring of Node Energy Consumption

Effects of Predictors on Power Consumption Estimation for IT Rack in a Data Center: An Experimental Analysis

Power Consumption Estimation in Data Centers Using Machine Learning Techniques

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