面向云网融合的数据中心能效评估方法

龙赛琴; 黄金娜; 李哲涛; 裴廷睿; 夏元清

doi:10.7544/issn1000-1239.2021.20201069

面向云网融合的数据中心能效评估方法

龙赛琴^1,2,3,
黄金娜¹,
李哲涛^1,2,3,
裴廷睿^1,2,3,
夏元清⁴

¹(湘潭大学计算机学院湖南湘潭 411105)
²(物联网与信息安全湖南省重点实验室(湘潭大学) 湖南湘潭 411105)
³(智慧网络湖南省国际科技创新合作基地(湘潭大学) 湖南湘潭 411105)
⁴(北京理工大学自动化学院北京 100081) (saiqinlong@xtu.edu.cn)

基金项目: 国家重点研发计划项目(2018YFB1003702)；国家自然科学基金项目(62032020,61502407,62076214)；湖南省杰出青年科学基金项目(2018JJ1025)；湖南省科技计划项目(2019RS3019,2018TP1036)；湖南省自然科学基金项目(2019JJ50592)；湖南省教育厅科学研究项目(18C0107)

详细信息

中图分类号: TP391
计量
- 文章访问数: 522
- HTML全文浏览量: 3
- PDF下载量: 343
出版历程
- 发布日期: 2021-05-31

Energy Efficiency Evaluation Method of Data Centers for Cloud-Network Integration

¹(School of Computer Science, Xiangtan University, Xiangtan, Hunan 411105)
²(Key Laboratory of Hunan Province for Internet of Things and Information Security (Xiangtan University), Xiangtan, Hunan 411105)
³(Hunan International Scientific and Technological Cooperation Base of Intelligent Network (Xiangtan University), Xiangtan, Hunan 411105)
⁴(School of Automation, Beijing Institute of Technology, Beijing 100081)

Funds: This work was supported by the National Key Research and Development Program of China (2018YFB1003702), the National Natural Science Foundation of China (62032020, 61502407, 62076214), the Hunan Provincial Natural Science Foundation of China for Distinguished Young Scholars (2018JJ1025), the Hunan Science and Technology Planning Project (2019RS3019, 2018TP1036), the Natural Science Foundation of Hunan Province of China (2019JJ50592), and the Science Research Foundation of Hunan Provincial Educational Department(18C0107).

摘要

摘要: 云网融合的加速发展，既推动着数据中心规模快速增长，也带来了巨大的能源消耗.如何制定合理的数据中心能效评估标准已成为指导数据中心能效提升亟需解决的关键问题.针对单一指标很难全面衡量数据中心的能源效率，且不同的数据中心能效指标各有侧重，甚至互相矛盾的问题，提出了将多指标进行融合来综合评估数据中心的能效，采用了主客观结合的赋权方法，为不同的能效指标设置权重，设计了基于云模型的多指标融合评估策略，得到了更加科学、全面的数据中心能效评估结果.最后，利用灰色关联法分析了评估结果与各能效指标之间的关系，分析结果对数据中心能效的提升具有重要的指导意义.
- 多指标 /
- 数据中心 /
- 能效 /
- 云模型 /
- 灰色关联
Abstract: Cloud-network integration is developing at an accelerated pace, which not only promotes the rapid growth of data center scale, but also brings huge energy consumption. How to formulate reasonable data center energy efficiency evaluation standards has become a key issue that needs to be solved urgently to guide the improvement of data center energy efficiency. It is difficult to evaluate the energy efficiency of data centers comprehensively based on a single metric, and different data center energy efficiency metrics have their own focuses, and even contradict each other. It is proposed to integrate multiple metrics to evaluate the energy efficiency of data centers comprehensively. The model adopts a combination of subjective and objective weighting methods to set weights for different energy efficiency metrics. A multi-metric fusion evaluation strategy is designed based on the cloud model to obtain a more scientific and comprehensive data center energy efficiency evaluation result. Finally, the gray correlation method is proposed to analyze the relationship between the evaluation results and various energy efficiency metrics. The analysis results have important guiding significance for the improvement of data center energy efficiency.
- multi-metric /
- data center /
- energy efficiency /
- cloud model /
- grey relation

HTML全文

参考文献(0)

施引文献(14)

期刊类型引用(8)

1.	刘金全，张铮，陈自东，曹晟. 一种基于联邦学习参与方的投毒攻击防御方法. 计算机应用研究. 2024(04): 1171-1176 . 百度学术
2.	杨文彬. 基于联邦学习的移动边缘节点计算的数据智能分类问题研究. 自动化与仪器仪表. 2024(06): 19-23 . 百度学术
3.	符太东，李育强. 基于联邦学习算法的复杂网络大数据隐私保护. 计算机仿真. 2024(06): 498-502 . 百度学术
4.	孙静，彭勇刚，倪旖旎，韦巍，蔡田田，习伟. 基于改进联邦学习算法的电力负荷预测方法. 高电压技术. 2024(07): 3039-3049 . 百度学术
5.	乐俊青，谭州勇，张迪，刘高，向涛，廖晓峰 . 面向车联网数据持续共享的安全高效联邦学习. 计算机研究与发展. 2024(09): 2199-2212 . 本站查看
6.	孙钰，刘霏霏，李大伟，刘建伟. 联邦学习拜占庭攻击与防御研究综述. 网络空间安全科学学报. 2023(01): 17-37 . 百度学术
7.	康孟珍，王秀娟，李冬，王旭伟，王浩宇，樊梦涵，许钰林，王飞跃. 基于联邦学习的分布式农业组织. 智能科学与技术学报. 2022(02): 288-297 . 百度学术
8.	王文鑫，柳彩云，岳梓岩. 基于联邦学习的工业互联网结构优化. 工业信息安全. 2022(01): 103-107 . 百度学术