数据中心效率模型:人工智能的新方法和作用

E. Isaev, V. Kornilov, A. A. Grigoriev
{"title":"数据中心效率模型:人工智能的新方法和作用","authors":"E. Isaev, V. Kornilov, A. A. Grigoriev","doi":"10.17537/2023.18.215","DOIUrl":null,"url":null,"abstract":"\nBioinformatics technologies play a significant and growing role in life science research, and as these technologies develop, so does the complexity of data. The challenge of biological data growth has given rise to a number of bioinformatics data centers that offer services and solutions ranging from large-scale biosystems analyze that accounts for entire OMICs to nanoscale experiments where molecular modeling can provide insight o structure and dynamics of molecular complexes of biological components. Obviously, this kind of research requires a highly specialized level of computational and statistical expertise, as well as high-performance resources. The importance of information technology is growing, as is the use of computer information systems throughout the world. There are more and more specialized data centers and they consume more energy. The development of new strategies for energy efficiency of data centers is becoming relevant. These strategies aim to reduce the amount of energy consumed by data centers and their environmental impact without sacrificing performance. The article examines performance metrics, proposes a new method for data center energy efficiency, and discusses the role of artificial intelligence techniques in achieving these goals.\n","PeriodicalId":53525,"journal":{"name":"Mathematical Biology and Bioinformatics","volume":"114 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Data Center Efficiency Model: A New Approach and the Role of Artificial Intelligence\",\"authors\":\"E. Isaev, V. Kornilov, A. A. Grigoriev\",\"doi\":\"10.17537/2023.18.215\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\nBioinformatics technologies play a significant and growing role in life science research, and as these technologies develop, so does the complexity of data. The challenge of biological data growth has given rise to a number of bioinformatics data centers that offer services and solutions ranging from large-scale biosystems analyze that accounts for entire OMICs to nanoscale experiments where molecular modeling can provide insight o structure and dynamics of molecular complexes of biological components. Obviously, this kind of research requires a highly specialized level of computational and statistical expertise, as well as high-performance resources. The importance of information technology is growing, as is the use of computer information systems throughout the world. There are more and more specialized data centers and they consume more energy. The development of new strategies for energy efficiency of data centers is becoming relevant. These strategies aim to reduce the amount of energy consumed by data centers and their environmental impact without sacrificing performance. The article examines performance metrics, proposes a new method for data center energy efficiency, and discusses the role of artificial intelligence techniques in achieving these goals.\\n\",\"PeriodicalId\":53525,\"journal\":{\"name\":\"Mathematical Biology and Bioinformatics\",\"volume\":\"114 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-06-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Mathematical Biology and Bioinformatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.17537/2023.18.215\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Mathematics\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mathematical Biology and Bioinformatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.17537/2023.18.215","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Mathematics","Score":null,"Total":0}
引用次数: 0

摘要

生物信息学技术在生命科学研究中发挥着越来越重要的作用,随着这些技术的发展,数据的复杂性也在不断提高。生物数据增长的挑战导致了许多生物信息学数据中心的出现,这些数据中心提供的服务和解决方案范围从解释整个组学的大规模生物系统分析到纳米级实验,其中分子建模可以提供对生物组分分子复合物的结构和动力学的见解。显然,这种研究需要高度专业化的计算和统计专业知识,以及高性能资源。信息技术的重要性正在增长,计算机信息系统在世界各地的使用也是如此。有越来越多的专业数据中心,它们消耗更多的能源。数据中心能源效率新战略的发展正变得越来越重要。这些策略的目的是在不牺牲性能的情况下减少数据中心消耗的能源及其对环境的影响。本文研究了性能指标,提出了一种提高数据中心能源效率的新方法,并讨论了人工智能技术在实现这些目标中的作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Data Center Efficiency Model: A New Approach and the Role of Artificial Intelligence
Bioinformatics technologies play a significant and growing role in life science research, and as these technologies develop, so does the complexity of data. The challenge of biological data growth has given rise to a number of bioinformatics data centers that offer services and solutions ranging from large-scale biosystems analyze that accounts for entire OMICs to nanoscale experiments where molecular modeling can provide insight o structure and dynamics of molecular complexes of biological components. Obviously, this kind of research requires a highly specialized level of computational and statistical expertise, as well as high-performance resources. The importance of information technology is growing, as is the use of computer information systems throughout the world. There are more and more specialized data centers and they consume more energy. The development of new strategies for energy efficiency of data centers is becoming relevant. These strategies aim to reduce the amount of energy consumed by data centers and their environmental impact without sacrificing performance. The article examines performance metrics, proposes a new method for data center energy efficiency, and discusses the role of artificial intelligence techniques in achieving these goals.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Mathematical Biology and Bioinformatics
Mathematical Biology and Bioinformatics Mathematics-Applied Mathematics
CiteScore
1.10
自引率
0.00%
发文量
13
期刊最新文献
Modeling Growth and Photoadaptation of Porphyridium purpureum Batch Culture Mathematical Modeling of the Initial Period of Spread of HIV-1 Infection in the Lymphatic Node Mathematical Model of Closed Microecosystem “Algae – Heterotrophic Bacteria” Using a Drug Repurposing Strategy to Virtually Screen Potential HIV-1 Entry Inhibitors That Block the NHR Domain of the Viral Envelope Protein gp41 Applying Laplace Transformation on Epidemiological Models as Caputo Derivatives
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1