DOI:10.35833/MPCE.2019.000052 |
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Minimizing Energy Cost for Green Data Center by Exploring Heterogeneous Energy Resource |
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Page view: 124
Net amount: 762 |
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Author:
Xiaoxuan Hu1,Peng Li2,Yanfei Sun3
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Author Affiliation:
1.School of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210003, China;2.School of Computer Science and Engineering, University of Aizu, Aizuwakamatsu 965-8580, Japan;3.School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
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Foundation: |
This work was supported in part by National Natural Science Foundation of China (No. 61772286, No. 61802208), China Postdoctoral Science Foundation (No. 2019M651923), Natural Science Foundation of Jiangsu Province of China (No. BK20191381), Primary Research & Development Plan of Jiangsu Province (No. BE2019742), and Natural Science Fund for Colleges and Universities in Jiangsu Province (No. 18KJB520036). |
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Abstract: |
With the deteriorating effects resulting from global warming in many areas, geographically distributed data centers contribute greatly to carbon emissions, because the major energy supply is fossil fuels. Considering this issue, many geographically distributed data centers are attempting to use clean energy as their energy supply, such as fuel cells and renewable energy sources. However, not all workloads can be powered by a single power sources, since different workloads exhibit different characteristics. In this paper, we propose a fine-grained heterogeneous power distribution model with an objective of minimizing the total energy costs and the sum of the energy gap generated by the geographically distributed data centers powered by multiple types of energy resources. In order to achieve these two goals, we design a two-stage online algorithm to leverage the power supply of each energy source. In each time slot, we also consider a chance-constraint problem and use the Bernstein approximation to solve the problem. Finally, simulation results based on real-world traces illustrate that the proposed algorithm can achieve satisfactory performance. |
Keywords: |
Data center ; heterogeneous energy resources ; Bernstein approximation ; energy management ; power distribution algorithm |
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Received:September 26, 2019
Online Time:2021/01/22 |
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