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研究生:葉安捷
研究生(外文):Jason-Alan Yelinek
論文名稱:雲端運算中能源效率及綠色能源的研究
論文名稱(外文):Energy Efficiency and Green Energy in Cloud Computing
指導教授:Edward Hsieh
指導教授(外文):Edward Hsieh
口試委員:H.G. Chen
口試委員(外文):H.G. Chen
口試日期:2013-07-15
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:企業管理碩士專班
學門:商業及管理學門
學類:企業管理學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
語文別:英文
論文頁數:116
中文關鍵詞:Energy EfficiencyGreen EnergyCloud ComputingLCOEwLCOE
外文關鍵詞:Energy EfficiencyGreen EnergyCloud ComputingLCOEwLCOE
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One of the key questions that should be asked when making technological innovation is whether or not one should make the innovation. With the breakout success of cloud computing and mobility computing devices such as smart phones, tablets, and laptops the question is more of how quickly innovation can get to the market rather than should it get to the market. Due to the inherent limitations of mobile devices, a massive cloud based infrastructure is needed behind the scenes to enable ubiquity of apps and connectivity. The cloud application and mobility fields are continuously growing as is the cloud infrastructure powering and keeping the lion’s share of the data. Here, the question not being asked is what is the real impact on the environment from cloud computing and how can this be mitigated.

In a hope to make more informed decisions on the planning and commissioning of cloud computing data centers and data centers, this thesis explores the cloud computing landscape, energy efficiency, climate change and the intersection of each. There are great opportunities to reduce power consumption in cloud computing to save energy. There are also great opportunities to strategically apply green energy to the cloud. This thesis proposes a weighted levelized cost of energy metric to quantify the real cost and impact of different power sources. This is done at the global level as well as the local level in the United States and China.

With a weighted levelized cost of energy, this thesis then explores regional opportunities within the United States and considers power mix migration scenarios to improve overall weighted cost. The final step is to create a data center model and investigate permutations of power mix and location in order to better understand cost impact savings opportunities.


Abstract iii
Contents iv
List of Figures vii
List of Tables ix
1 Background, Motivation 1
1.1 Introduction: 1
1.2 Motivation: 1
1.3 Limitations 3
2 Literature Review 4
2.1 Cloud Computing and System Efficiency 5
2.1.1 Cloud computing definition 5
2.1.2 Cloud computing efficiency 8
2.1.3 System level efficiency 10
2.2 Cloud Computing Infrastructure Efficiency 15
2.3 Green Energy 23
2.3.1 Definition of Green Energy 23
2.3.2 Green Energy Technologies 24
2.3.3 Solar 27
2.3.4 Biomass 28
2.3.5 CO2 emission studies 29
2.3.6 Costs 30
2.3.7 Clean Development Mechanism 32
2.3.8 Lifetime levelized costs 32
2.3.9 Current Production / Capacity / Consumption 34
2.4 Green Energy in Cloud Computing 38
3 Research Methodology 47
3.1 Weighted LCOE 47
3.2 Data Collection / Research Methodology for the United States 49
3.3 Data Collection / Research Methodology for China 50
3.4 Data Center analysis 51
4 Data Analysis 52
4.1 Emissions and LCOE Study Analysis 52
4.1.1 Emissions Review and Analysis 52
4.1.2 LCOE Review and Analysis 60
4.2 World Bank Production Data Analysis 69
4.3 LEC/Emissions Analysis (US) 74
4.4 Weighted LCOE Derivation 76
4.5 Weighted LCOE Analysis (US) 80
4.6 CDM Analysis (China) 84
4.7 LCOE/Emissions Analysis (China) 85
4.8 Data Center Analysis 89
5 Conclusion, Future Work 96
5.1 Conclusion: 96
5.2 Future Work: 98
References: 100
Appendix 1 EIA U.S. Regional Electricity Generation per Day 109
Appendix 2 World Book Source Data China 111
Appendix 3 World Book Source Data United States 112
Appendix 4 World Book Source Data World 113
Appendix 5 World Book Source Data Mexico, Brazil, India 114
Appendix 6 China CDM Summary 115
Appendix 7 Data center model detailed data 116


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