{"title":"评估数据中心的环境影响","authors":"João Ferreira, G. Callou, Albert Josua, P. Maciel","doi":"10.1109/NCA.2018.8548326","DOIUrl":null,"url":null,"abstract":"The demands of performance, availability and storage of new technologies have increased significantly the energy consumption of data centers. This consumption is rising both the environmental impact and operational costs of computational systems that support those technologies. Assuming the electricity consumption and CO2 emissions, the adopted utility power source is of substantial importance. This work estimates the availability, and conducts an evaluation of cost and CO2 emissions of electrical infrastructures in data centers, considering different energy sources. We use a multi-layered artificial neural network, which is able to forecast consumption over the following months, based on the energy consumption history of the data center. All these features are supported by a tool, the applicability of which is demonstrated through a case study that computes the CO2 emissions and operational costs of a data center using the energy mix adopted in Brazil. China. Germany and the US.","PeriodicalId":268662,"journal":{"name":"2018 IEEE 17th International Symposium on Network Computing and Applications (NCA)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Estimating the Environmental Impact of Data Centers\",\"authors\":\"João Ferreira, G. Callou, Albert Josua, P. Maciel\",\"doi\":\"10.1109/NCA.2018.8548326\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The demands of performance, availability and storage of new technologies have increased significantly the energy consumption of data centers. This consumption is rising both the environmental impact and operational costs of computational systems that support those technologies. Assuming the electricity consumption and CO2 emissions, the adopted utility power source is of substantial importance. This work estimates the availability, and conducts an evaluation of cost and CO2 emissions of electrical infrastructures in data centers, considering different energy sources. We use a multi-layered artificial neural network, which is able to forecast consumption over the following months, based on the energy consumption history of the data center. All these features are supported by a tool, the applicability of which is demonstrated through a case study that computes the CO2 emissions and operational costs of a data center using the energy mix adopted in Brazil. China. Germany and the US.\",\"PeriodicalId\":268662,\"journal\":{\"name\":\"2018 IEEE 17th International Symposium on Network Computing and Applications (NCA)\",\"volume\":\"43 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE 17th International Symposium on Network Computing and Applications (NCA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NCA.2018.8548326\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE 17th International Symposium on Network Computing and Applications (NCA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NCA.2018.8548326","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Estimating the Environmental Impact of Data Centers
The demands of performance, availability and storage of new technologies have increased significantly the energy consumption of data centers. This consumption is rising both the environmental impact and operational costs of computational systems that support those technologies. Assuming the electricity consumption and CO2 emissions, the adopted utility power source is of substantial importance. This work estimates the availability, and conducts an evaluation of cost and CO2 emissions of electrical infrastructures in data centers, considering different energy sources. We use a multi-layered artificial neural network, which is able to forecast consumption over the following months, based on the energy consumption history of the data center. All these features are supported by a tool, the applicability of which is demonstrated through a case study that computes the CO2 emissions and operational costs of a data center using the energy mix adopted in Brazil. China. Germany and the US.