• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권10호
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• pp.2682-2700
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• 2023

To cope with the risks of climate change and promote the realization of carbon peaking and carbon neutrality, this paper first comprehensively considers the policy background, technical trends and carbon reduction paths of energy conservation and emission reduction in data center server industry. Second, we propose a computing power carbon efficiency of data center server, and constructs the carbon emission per performance of server (CEPS) model. According to the model, this paper selects the mainstream data center servers for testing. The result shows that with the improvement of server performance, the total carbon emissions are rising. However, the speed of performance improvement is faster than that of carbon emission, hence the relative carbon emission per unit computing power shows a continuous decreasing trend. Moreover, there are some differences between different products, and it is calculated that the carbon emission per unit performance is 20-60KG when the service life of the server is five years.

• 정보과학회 컴퓨팅의 실제 논문지
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• 제22권3호
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• pp.151-156
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• 2016

클라우드 컴퓨팅이 주목을 받으며 대규모 스토리지 서버에 대한 필요성이 증대됐고, 그에 따라 소비전력 절감이 중요한 연구 주제로 부상하고 있다. 그러나 산업계에서 운영 중인 대규모 데이터 센터를 실험용으로 구축하는데 물리적 한계가 있기 때문에, 많은 선행 연구가 제안한 에너지 절감 기법들의 효과를 입증하는데 어려움을 겪고 있다. 이에 따라, 본 연구는 산업계에서 운영 중인 오픈소스 기반의 OCP Cold Storage를 소규모 테스트 베드로 구축하고, 이를 활용한 스토리지 서버의 정확한 소비 전력 측정 방안을 제고하였다. 또한, 제안한 테스트베드는 클라우드 응용 플랫폼과의 결합을 통해 쉽게 확장 가능하기 때문에, 저전력 스토리지 서버의 정책 개발 및 성능 평가에 크게 기여할 수 있을 것으로 기대한다.

• Environmental Engineering Research
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• 제22권3호
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• pp.302-311
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• 2017

Nowadays, with the burgeoning development of economy, $CO_2$ emissions increase rapidly in China. It has become a common concern to seek effective methods to forecast $CO_2$ emissions and put forward the targeted reduction measures. This paper proposes a novel hybrid model combined principal component analysis (PCA) with regularized extreme learning machine (RELM) to make $CO_2$ emissions prediction based on the data from 1978 to 2014 in China. First eleven variables are selected on the basis of Pearson coefficient test. Partial autocorrelation function (PACF) is utilized to determine the lag phases of historical $CO_2$ emissions so as to improve the rationality of input selection. Then PCA is employed to reduce the dimensionality of the influential factors. Finally RELM is applied to forecast $CO_2$ emissions. According to the modeling results, the proposed model outperforms a single RELM model, extreme learning machine (ELM), back propagation neural network (BPNN), GM(1,1) and Logistic model in terms of errors. Moreover, it can be clearly seen that ELM-based approaches save more computing time than BPNN. Therefore the developed model is a promising technique in terms of forecasting accuracy and computing efficiency for $CO_2$ emission prediction.