{"title":"通过降低功耗减少数据中心耗水量的智能流量路由和服务分配策略","authors":"Sajjad Ghanbari, Ali Ghiasian","doi":"10.1016/j.suscom.2024.100974","DOIUrl":null,"url":null,"abstract":"<div><p>Due to the growth of communication networks, energy consumption in information and communication technology industries is increasing dramatically. Among these industries, data centers are operating with a large number of processors and other components, which, due to heavy processing and mass data transmission, in addition to consuming high electrical power, also cause high thermal losses. Since water cooling systems are used for cooling different parts of these centers as well as for cooling the electric power generation unit, these centers are among the major consumers of water and electricity resources. In this article, while examining the important factors affecting water consumption in data centers, useful methods are suggested to reduce the consumption of electrical energy and water. Optimizing energy consumption in data centers is possible in three parts: routing, servicing and use of cooling equipment. For all three parts, improvement methods are suggested in this article. For this purpose, an optimization problem is designed and an algorithm is presented to solve it. In the proposed solution, an energy-smart method based on SDN technology is used for routing, virtual machines equipped with the possibility of reducing power consumption in no-load conditions are used for servicing, and two types of water-cooled and air-cooled systems are used for cooling equipment. is used. The simulation results show that depending on the cooling system, the proposed method reduces water consumption between 24% and 32% compared to the case where the proposed solution is not used.</p></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"42 ","pages":"Article 100974"},"PeriodicalIF":3.8000,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Smart traffic routing and service allocation strategy to reduce water consumption in data centers through power reduction\",\"authors\":\"Sajjad Ghanbari, Ali Ghiasian\",\"doi\":\"10.1016/j.suscom.2024.100974\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Due to the growth of communication networks, energy consumption in information and communication technology industries is increasing dramatically. Among these industries, data centers are operating with a large number of processors and other components, which, due to heavy processing and mass data transmission, in addition to consuming high electrical power, also cause high thermal losses. Since water cooling systems are used for cooling different parts of these centers as well as for cooling the electric power generation unit, these centers are among the major consumers of water and electricity resources. In this article, while examining the important factors affecting water consumption in data centers, useful methods are suggested to reduce the consumption of electrical energy and water. Optimizing energy consumption in data centers is possible in three parts: routing, servicing and use of cooling equipment. For all three parts, improvement methods are suggested in this article. For this purpose, an optimization problem is designed and an algorithm is presented to solve it. In the proposed solution, an energy-smart method based on SDN technology is used for routing, virtual machines equipped with the possibility of reducing power consumption in no-load conditions are used for servicing, and two types of water-cooled and air-cooled systems are used for cooling equipment. is used. The simulation results show that depending on the cooling system, the proposed method reduces water consumption between 24% and 32% compared to the case where the proposed solution is not used.</p></div>\",\"PeriodicalId\":48686,\"journal\":{\"name\":\"Sustainable Computing-Informatics & Systems\",\"volume\":\"42 \",\"pages\":\"Article 100974\"},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2024-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Sustainable Computing-Informatics & Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2210537924000192\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sustainable Computing-Informatics & Systems","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2210537924000192","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
引用次数: 0
摘要
由于通信网络的发展,信息和通信技术行业的能耗正在急剧增加。在这些行业中,数据中心运行着大量的处理器和其他组件,由于要进行大量的处理和数据传输,除了消耗大量电能外,还会造成较高的热损耗。由于水冷系统用于冷却这些中心的不同部分以及冷却发电装置,因此这些中心是水电资源的主要消耗者之一。本文在研究影响数据中心耗水量的重要因素的同时,提出了减少电能和水消耗的有效方法。优化数据中心的能源消耗可分为三个部分:路由、服务和冷却设备的使用。本文针对这三个部分提出了改进方法。为此,本文设计了一个优化问题,并提出了一种算法来解决这个问题。在提出的解决方案中,路由采用了基于 SDN 技术的能源智能方法,服务采用了可降低空载功耗的虚拟机,冷却设备采用了水冷和风冷两种系统。模拟结果表明,根据冷却系统的不同,与未使用拟议解决方案的情况相比,拟议方法可减少 24% 至 32% 的耗水量。
Smart traffic routing and service allocation strategy to reduce water consumption in data centers through power reduction
Due to the growth of communication networks, energy consumption in information and communication technology industries is increasing dramatically. Among these industries, data centers are operating with a large number of processors and other components, which, due to heavy processing and mass data transmission, in addition to consuming high electrical power, also cause high thermal losses. Since water cooling systems are used for cooling different parts of these centers as well as for cooling the electric power generation unit, these centers are among the major consumers of water and electricity resources. In this article, while examining the important factors affecting water consumption in data centers, useful methods are suggested to reduce the consumption of electrical energy and water. Optimizing energy consumption in data centers is possible in three parts: routing, servicing and use of cooling equipment. For all three parts, improvement methods are suggested in this article. For this purpose, an optimization problem is designed and an algorithm is presented to solve it. In the proposed solution, an energy-smart method based on SDN technology is used for routing, virtual machines equipped with the possibility of reducing power consumption in no-load conditions are used for servicing, and two types of water-cooled and air-cooled systems are used for cooling equipment. is used. The simulation results show that depending on the cooling system, the proposed method reduces water consumption between 24% and 32% compared to the case where the proposed solution is not used.
期刊介绍:
Sustainable computing is a rapidly expanding research area spanning the fields of computer science and engineering, electrical engineering as well as other engineering disciplines. The aim of Sustainable Computing: Informatics and Systems (SUSCOM) is to publish the myriad research findings related to energy-aware and thermal-aware management of computing resource. Equally important is a spectrum of related research issues such as applications of computing that can have ecological and societal impacts. SUSCOM publishes original and timely research papers and survey articles in current areas of power, energy, temperature, and environment related research areas of current importance to readers. SUSCOM has an editorial board comprising prominent researchers from around the world and selects competitively evaluated peer-reviewed papers.