Environment data processing for a data centre
There are rising concerns over the carbon footprint of data centre (DC) in Singapore as their total electricity consumption has increased from 5.3% in 2019 to 7% in 2020. This figure is projected to increase, in line with the growing demand for DC. In this study, we will focus on the DC’s informa...
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| 其他作者: | |
| 格式: | Final Year Project |
| 語言: | English |
| 出版: |
Nanyang Technological University
2022
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/163017 |
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| 機構: | Nanyang Technological University |
| 語言: | English |
| 總結: | There are rising concerns over the carbon footprint of data centre (DC) in Singapore as their total
electricity consumption has increased from 5.3% in 2019 to 7% in 2020. This figure is projected
to increase, in line with the growing demand for DC. In this study, we will focus on the DC’s
information technology (IT) system which is one of the major energy consumers. Five types of
predictive models: Multilayer Perceptron, Linear Regression, Decision Tree, Support Vector
Regression and Stacking-based Ensemble were developed to predict total IT power consumption
using IT facilities' operating conditions and meteorological parameters as inputs. This research
aims to investigate the relationship between a DC’s IT power consumption with its IT facilities
and environmental factors outside the DC. This paper also presents a feature importance analysis
and a detailed comparison of the performance of different models. The results of the feature
importance analysis indicate that CPU utilization is the most significant factor that will affect
the total IT power consumption in the DC. As for the model evaluation, the Stacking-based
ensemble performs the best. Based on the Stacking-based ensemble model, a set of
recommendations on the most optimal IT operating conditions was made. |
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